visual presentation of data makes comparison easy ignou assignment

MCO-03: Research Methodology and Statistical Analysis

Ignou solved assignment solution for 2022-23, if you are looking for mco-03 ignou solved assignment solution for the subject research methodology and statistical analysis, you have come to the right place. mco-03 solution on this page applies to 2022-23 session students studying in mcom, mcomft, mcombpcg, mcommafs courses of ignou., looking to download all solved assignment pdfs for your course together, mco-03 solved assignment solution by gyaniversity.

Assignment Code: MCO-03 /ASST/TMA/2022-23

Course Code: MCO-03

Assignment Name: Research Methodology and Statistical Analysis

Year: 2022-2023

Verification Status: Verified by Professor

Q. 1 What is Research Design? List the various components of a research design? (20)

Ans ) Other titles for research design are research outline, plan, and blue print. According to Fred N. Kerlinger, it is the investigation's plan, structure, and strategy created in order to gather data and manage variance. The strategy details every action the researcher will do, from formulating the hypotheses and determining how they will operate through conducting the data analysis at the end.

The framework, plan, and operating paradigms for the variables make up the structure. The tactics to be employed for data collection and analysis are part of the plan. This plan (design) is typically hazy and unfinished at first. As the research develops and new insights into it become available, it experiences several alterations and changes. Making decisions about the what, why, where, when, who, and how of the study constitutes the process of carrying out the plan.

A research design, in the words of Pauline V. Young, is the methodical and logical planning and direction of a piece of study. Research designs, in the words of Reger E. Kirk, "are blueprints that outline how data should be collected and processed." The research needs to be tailored to the resources—time, money, energy, and data—that are available. There is no one perfect or singular design. A compromise is required in research design since there are so many real-world factors to take into account.

Components of a Research Design

The following are typically the contents or components of a research design:

Need for the Study : Describe the purpose, significance, and relevance of this investigation.

Review of Previous Studies : Describe the purpose of this study, its significance, and its relevance.

Statement of Problem: Give the study a title and clearly state the research problem.

Objectives of Study : What is the aim of this research? What goals do you hope to accomplish with this study? The aims statement shouldn't be too ambiguous. They need to be focused and specific.

Formulation of Hypothesis : Create potential outcomes or solutions to the study questions, then turn them into tests for the respective hypotheses.

Operational Definitions: They must be identified and defined if the study uses rare concepts, novel tools, or even familiar tools and concepts in a particular way.

Sources of Data: This phase of the research design is crucial. The researcher must choose the sources of data from which the data are to be gathered at this point, bearing in mind the nature of the research. Primary sources (field sources) and secondary sources are the two main categories of sources (documentary sources). Primary data are those that come from the primary source, and secondary data are those that come from the secondary source. Therefore, the researcher must choose whether to gather data from primary, secondary, or both sources.

Method of Collection: The researcher must choose the methods to be used for data collecting after choosing on the sources to use, generally either the census method or the sampling approach. This choice may be influenced by the nature, goal, and scope of the research, as well as by time constraints and available financial resources.

Tools & Techniques: It is necessary to choose and prepare the instruments and methods to be used for data collection, including observation, interview, survey, timetable, questionnaire, etc.

Sampling Design: If the study is a sample, decisions must be made on sampling methods, sample size, sample drawing procedures, etc.

Data Analysis: How will you organise and examine the data and information gathered? What basic or sophisticated statistical methods will be employed for hypothesis testing and analysis, so that the proper precautions can be taken during the data gathering phase.

Presentation of the Results of Study : What format will you use to deliver the study's findings? number of chapters What is the chapter organisation? It is necessary to outline the chapters, their goals, and their titles. Chapterization is the term for it.

Q. 2 a) Explain the concept of skewness. How does it help in analysing the data? (10)

Ans ) The measure of skewness tells us the direction of dispersion about the centre of the distribution. Measures of central tendency indicate only the single representative figure of the distribution while measures of variation, indicate only the spread of the individual values around the means. They do not give any idea of the direction of spread.

Two distributions may have the same mean and variation but may differ widely in the shape of their distribution. A distribution is often found skewed on either side of its average, which is termed as asymmetrical distribution. Thus, skewness refers to the lack of symmetry in distribution. Symmetry signifies that the value of variables are equidistant from the average on both sides. In other words, a balanced pattern of a distribution is called symmetrical distribution, whereas unbalanced pattern of distribution is called asymmetrical distribution.

A simple method of finding the direction of skewness is to consider the tails of a frequency polygon. The concept of skewness will be clear from the following three figures showing symmetrical, positively skewed and negatively skewed distributions.

visual presentation of data makes comparison easy ignou assignment

Carefully observe the figures presented above and try to understand the following rules governing them.

It is clear from figure that the data are symmetrical when the spread of the frequencies is the same on both sides of the middle point of the frequency polygon. In this case the value of mean, median, and mode coincide i.e.,

Mean = Median = Mode.

When the distribution is not symmetrical, it is said to be a skewed distribution. Such a distribution could be either positively skewed or negatively skewed. In Figure (b), when there is a longer tail towards the right hand side of the centre of distribution, the skewness is said to be Positively Skewed. In such a situation,

Mean > Median > Mode.

In Figure when there is a longer tail towards the left hand side of the centre, then the skewness is said to be Negatively Skewed. In such a case,

Mean < Median < Mode.

It is seen that, in positively skewed distribution, dispersal of individual observations is greater towards the right of the central value. Where as in a negatively skewed distribution, a greater dispersal of individual observations is towards the left of the central value. We can say, therefore, the concept of Skewness not only refers to lack of symmetry in a distribution but also indicates the magnitude as well as the direction of skewness in a distribution. The relationship of mean, median and mode in measuring the degree of skewness is that, for a moderately symmetrical distribution the interval between the mean and the median is approximately 1/3rd of the interval between the mean and mode.

b)What is reporting? What are the different stages in the preparation of a report? (10)

Ans ) Reporting simply means communicating or informing through reports. The researcher has collected some facts and figures, analysed the same and arrived at certain conclusions. He has to inform or report the same to the parties interested. Therefore “reporting is communicating the facts, data and information through reports to the persons for whom such facts and data are collected and compiled.”

A report is not a complete description of what has been done during the period of survey/research. It is only a statement of the most significant facts that are necessary for understanding the conclusions drawn by the investigator. Thus, “ a report by definition, is simply an account.” The report thus is an account describing the procedure adopted, the findings arrived at and the conclusions drawn by the investigator of a problem.

Stages in Preparation of A Report

Research reports are the product of slow and painstaking and accurate work. Therefore, the preparation of the report may be viewed in the following major stages:

Logical Understanding of the Subject Matter : It is the first stage which is primarily concerned with the development of a subject. There are two ways to develop a subject viz. a. logically and b. chronologically. The logical development is done on the basis of mental connections and associations between one aspect and another by means of logical analysis. Logical treatment often consists of developing material from the simple to the most complex. Chronological development is based on a connection or sequence in time or happening of the events. The directions for doing something usually follow the chronological order.

Designing the Final Outline of the Report: It is the second stage in writing the report. Having understood the subject matter, the next stage is structuring the report, ordering the parts, and sketching them. This stage can also be called as planning and organization stage. Ideas may pass through the author’s mind. Unless he first makes his plan/sketch/design he will be unable to achieve a harmonious succession and will not even know where to begin and how to end. Better communication of research results is partly a matter of language but mostly a matter of planning and organizing the report.

Preparation of the Rough Draft: The third stage is the write up/drafting of the report. This is the most crucial stage to the researcher, as he/she now sits to write down what he/she has done in his/her research study and what and how he/she wants to communicate the same. Here the clarity in communicating/reporting is influenced by some factors such as who the readers are, how technical the problem is, the researcher’s hold over the facts and techniques, the researcher’s command over language (his communication skills), the data and completeness of his notes and documentation and the availability of analysed results.

Depending on the above factors some authors may be able to write the report with one or two drafts. Some people who have less command over language, no clarity about the problem and subject matter may take more time for drafting the report and have to prepare more drafts (first draft, second draft, third draft, fourth draft etc.,)

Finalization of the Report: This is the last stage, perhaps the most difficult stage of all formal writing. It is easy to build the structure, but it takes more time for polishing and giving finishing touches. Take for example the construction of a house. Up to roofing (structure) stage the work is very quick but by the time the building is ready, it takes up a lot of time.

The rough draft (whether it is second draft or nth draft ) has to be rewritten, polished in terms of requirements. The careful revision of the rough draft makes the difference between a mediocre and a good piece of writing. While polishing and finalizing one should check the report for its weaknesses in logical development of the subject and presentation cohesion. He/she should also check the mechanics of writing — language, usage, grammar, spelling and punctuation.

Q. 3 Briefly comment on the following: (4×5)

a) “The research has to provide answers to the research questions raised.”

Ans ) The research questions posed at the start of the study must be answered by the researcher at the conclusion. He has conducted research and obtained pertinent facts and data as support or evidence for this. The methodologies or approaches of research that were used to acquire the results are discussed in the literature. In actuality, these are the primary techniques employed to gather the data. The following are these techniques:

Survey Method: The word "survey" has the definitions of "oversee," "look over," "study," and "systematically investigate." Studies of both large and small populations involve survey research (or universes). It is an investigational survey.

Observation Method: To observe is to see or to look at. It is a methodical viewing rather than a casual one. Therefore, "a systematic viewing of a specific phenomenon in its appropriate location for the aim of acquiring information for the specified research" is a definition of observation.

Case Method : The case study methodology is directly inspired by medical research. Like a patient, the case is carefully investigated in order to give a diagnosis and then suggest a course of action. An extensive investigation of a company or unit is necessary in order to pinpoint problems, differences, and specialties as well as to provide remedies.

Experimental Method: The primary method used by physical sciences like physics and chemistry to establish causal relationships and validate inferences is experimentation.

Historical Method: The historical approach is said to have been used when the researcher conducted their research using historical data. All study has a historical component because it heavily relies on observations and facts that were gathered in the past.

Comparative Method: The evolutionary or genetic technique is another name for the comparative method. The phrase "comparative approach" originated in the following manner: Some sciences, such comparative philology, comparative anatomy, comparative physiology, comparative psychology, comparative religion, etc., have long been referred to as "Comparative Sciences."

b)“Visual presentation of data makes comparison easy.”

Ans ) The use of visual representations of statistical data by researchers and statisticians in analysis has grown in popularity. Visual data presentation is the display of statistical data as diagrams and graphs. Today, every research project is supported by a visual presentation for the reasons listed below.

They relieve the dullness of the numerical data: Any long set of figures gets harder to understand and harder to make conclusions from. The mind is overworked when reading numbers from tables.

They make comparison easy: One of the main goals of data visualisation is to do this. Graphs and diagrams facilitate easy comparison of two or more sets of data, and the direction of curves reveals correlations and hidden facts in the statistical data.

They save time and effort: Only after putting a lot of mental effort into it is it possible to comprehend the properties of statistical data through tables. Diagrams and graphs make comprehending the fundamental properties of the data easier and faster.

They make it easier to find different statistical measurements and identify trends: Numerous metrics of central tendency, including the median, quartiles, mode, and more, can be located using graphs.

They have universal applicability: The presentation of numerical data via diagrams and graphs is a common practise. These days, it is a widely employed practise in a variety of industries, including agriculture, business, education, and health.

They have become an integral part of research: Today, it is actually challenging to discover any scientific work without visual aids. This is the most persuasive and appealing approach to convey the data, which is why. Data can be presented graphically and diagrammatically in journals, publications, reports, ads, television, and other media.

c) “The analysis of time series is of great utility not only to research workers but also to economists, businessmen and scientists, etc.”

Ans ) Researchers, as well as economists, businesspeople, and other professionals, can greatly benefit from time series analysis for the following reasons:

It aids in comprehending the historical behaviour of the variables under investigation.

With the aid of the changes that have occurred in the past, it makes it easier to predict future behaviour.

It aids in the planning of future actions.

Knowing recent success is helpful.

It is beneficial to compare various time series and draw important conclusions from them.

As a result, we may state that time series analysis is required in research because:

We wish to comprehend how the factors under investigation behave.

We are interested in the anticipated quantitative changes in the studied variable.

We wish to quantitatively quantify the impact of different causes.

In a summary, time series analysis is helpful for governments to develop effective future growth strategies, in addition to researchers and commercial research institutions.

d)“The interpretation of data is a very difficult task and requires a high degree of skill, care, judgment, and objectivity.”

Ans ) It is crucial to understand that if suitable measures are not performed, interpreting errors may occur. Data interpretation is an extremely challenging process that calls for a high level of expertise, consideration, judgement, and objectivity. In the absence of controls, there is a strong possibility that data will be abused to support untrue claims.

Before evaluating the data, the following safety measures must be taken.

The interpreter ought to be unbiased.

The interpreter needs to comprehend the issue from the correct angle.

He or she must recognise the importance of many aspects of the issue.

Make that all pertinent, sufficient, and accurate data are gathered.

Make sure the data is correctly categorised and evaluated.

Check to see if there are any restrictions on the data. What are they if so?

Take precautions to avoid mistake sources.

Don't extrapolate your interpretations beyond the information or data.

It is important to distinguish between personal interpretation and factual interpretation. They ought to remain apart.

The interpretation process can lead to reasonably sound results if these safety measures are adopted.

Q. 4 Write short notes on the following: (4×5)

a) Essentials of a good sample

Ans ) It is important that the sampling results must reflect the characteristics of the population. Therefore, while selecting the sample from the population under investigation it should be ensured that the sample has the following characteristics:

A sample must represent a true picture of the population from which it is drawn.

A sample must be unbiased by the sampling procedure.

A sample must be taken at random so that every member of the population of data has an equal chance of selection.

A sample must be sufficiently large but as economical as possible.

A sample must be accurate and complete. It should not leave any information incomplete and should include all the respondents, units or items included in the sample.

Adequate sample size must be taken considering the degree of precision required in the results of inquiry.

b) Coding of data

Ans ) Coding refers to the process by which data are categorized into groups and numerals or other symbols or both are assigned to each item depending on the class it falls in. Hence, coding involves deciding the categories to be used, and assigning individual codes to them. In general, coding reduces the huge amount of information collected into a form that is amenable to analysis.

A careful study of the answers is the starting point of coding. Next, a coding frame is to be developed by listing the answers and by assigning the codes to them. A coding manual is to be prepared with the details of variable names, codes and instructions. Normally, the coding manual should be prepared before collection of data, but for open-ended and partially coded questions. These two categories are to be taken care of after the data collection. The following are the broad general rules for coding:

Each respondent should be given a code number (an identification number).

Each qualitative question should have codes. Quantitative variables may or may not be coded depending on the purpose. Monthly income should not be coded if one of the objectives is to compute average monthly income. But if it is used as a classificatory variable it may be coded to indicate poor, middle or upper income group.

All responses including “don’t know,” “no opinion” “no response” etc., are to be coded.

Sometimes it is not possible to anticipate all the responses and some questions are not coded before collection of data. Responses of all the questions are to be studied carefully and codes are to be decided by examining the essence of the answers. In partially coded questions, usually there is an option “Any Other (specify).” Depending on the purpose, responses to this question may be examined and additional codes may be assigned.

c) Normal Distribution

Ans ) The normal distribution is the most versatile of all the continuous probability distributions. It is useful in statistical inferences, in characterising uncertainties in many real life situations, and in approximating other probability distributions.

The normal distribution is suitable for dealing with variables whose magnitudes are continuous. Many statistical data concerning business problems are displayed in the form of normal distribution. Height, weight and dimensions of a product are some of the continuous random variables which are found to be normally distributed. This knowledge helps us in calculating the probability of different events in varied situations, which in turn is useful for decision-making.

To define a particular normal probability distribution, we need only two parameters i.e., the mean (μ) and standard deviation (σ).

d) Characteristics of a good report

Ans ) Research report is a channel of communicating the research findings to the readers of the report. A good report is one which does this task efficiently and effectively. As such it should have the following characteristics/qualities.

It must be clear in informing the what, why, who, whom, when where and how of the research study.

It should be neither too short nor too long. One should keep in mind the fact that it should be long enough to cover the subject matter but short enough to sustain the reader’s interest.

It should be written in an objective style and simple language, correctness, precision and clarity should be the watchwords of the scholar. Wordiness, indirection and pompous language are barriers to communication.

A good report must combine clear thinking, logical organization and sound interpretation.

It should not be dull. It should be such as to sustain the reader’s interest.

It must be accurate. Accuracy is one of the requirements of a report. It should be factual with objective presentation. Exaggerations and superlatives should be avoided.

Clarity is another requirement of presentation. It is achieved by using familiar words and unambiguous statements, explicitly defining new concepts and unusual terms.

Coherence is an essential part of clarity. There should be logical flow of ideas (i.e., continuity of thought), sequence of sentences. Each sentence must be so linked with other sentences so as to move the thoughts smoothly.

Readability is an important requirement of good communication. Even a technical report should be easily understandable. Technicalities should be translated into language understandable by the readers.

A research report should be prepared according to the best composition practices. Ensure readability through proper paragraphing, short sentences, illustrations, examples, section headings, use of charts, graphs and diagrams.

Draw sound inferences/conclusions from the statistical tables. But don’t repeat the tables in text (verbal) form.

Footnote references should be in proper form. The bibliography should be reasonably complete and in proper form.

The report must be attractive in appearance, neat and clean whether typed or printed.

The report should be free from mistakes of all types of viz. language mistakes, factual mistakes, spelling mistakes, calculation mistakes etc.,

Q. 5 Distinguish between the following: (4×5)

a) Pilot testing and Pre-testing of the Questionnaire

Ans ) Difference between Pilot testing and Pre-testing is as follows:

In a pre-test, you only test one or a few components of the research study on a small fraction of your intended sample size. During a pilot, you conduct the research study in its entirety, but on a smaller sample size.

A pre-test is where a questionnaire is tested on a (statistically) small sample of respondents before a full-scale study, in order to identify any problems such as unclear wording or the questionnaire taking too long to administer.

To pre-test, or pilot, a questionnaire, choose a representative sample of your larger survey group and have them take the questionnaire. Then you can make improvements where necessary based on their feedback.

A pilot survey is a strategy used to test the questionnaire using a smaller sample compared to the planned sample size. In this phase of conducting a survey, the questionnaire is administered to a percentage of the total sample population, or in more informal cases just to a convenience sample.

b) Price Indices and Value Indices

Ans ) Difference between Price Indices and Value Indices is as follows:

d) Large samples and Small samples

Ans ) A sample is typically regarded as tiny if it has a size of 30 or less, and large if it has a size greater than 30. All the tests being taken into consideration can be divided into two groups: exact tests and approximative tests. Exact tests are ones that are based on the precise sample distribution of the test statistic and do not approximate the parent population's form or the test statistic's sampling distribution. Since exact tests are applicable for samples of any size and labour costs typically rise as sample sizes increase, we prefer to use small samples when doing exact tests.

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Explain the significance of diagrammatic representation of data

Diagrammatic representation of data involves the use of visual elements such as charts, graphs, and diagrams to convey information and patterns within a dataset.

Get the full solved assignment PDF of MEV-019 of 2023-24 session now.

This visual representation serves several significant purposes in data analysis and communication:

Enhances Understanding:
Visual representations simplify complex data and make it more accessible to a broader audience. Diagrams provide a clear and intuitive way to understand patterns, trends, and relationships within the data, even for individuals without a strong statistical background.
Facilitates Data Comparison:
Diagrams allow for easy comparison of different data points, categories, or groups. Visualizing data side by side enhances the ability to identify similarities, differences, and trends, aiding in more effective data interpretation.
Supports Decision-Making:
Visualizations enable decision-makers to quickly grasp key insights from the data. When faced with large datasets, decision-makers can use diagrams to identify critical information and make informed decisions more efficiently.
Highlights Key Trends and Patterns:
Diagrams effectively highlight trends, patterns, and outliers within the data. Whether it’s identifying peaks and valleys in a time series or understanding distribution patterns, visual representations make these insights more apparent.
Improves Communication:
Visualizations are powerful tools for communication. They provide a common visual language that can be easily understood across diverse audiences. When presenting findings or insights, diagrams enhance communication and engagement.
Aids Memory Recall:
Visual information is often easier to remember than numerical data alone. Diagrams create a visual memory aid, helping individuals recall key points, trends, and relationships when discussing or presenting data.
Facilitates Storytelling:
Diagrams are valuable in telling a story with data. They help construct a narrative around the information, making it more compelling and engaging. Visualizations can guide the audience through the data story in a more structured and impactful manner.
Identifies Outliers and Anomalies:
Visualizations make it easier to identify outliers or anomalies in the data. Patterns that might be less apparent in raw data become noticeable when presented graphically, helping to identify areas that require further investigation.
Promotes Data Exploration:
Diagrams encourage users to explore the data by allowing them to interact with visual representations. Interactive charts and graphs provide opportunities for users to drill down into specific data points or zoom in on particular aspects of the dataset.
In reports, presentations, and publications, visual representations make data more visually appealing and engaging. Well-designed diagrams can convey complex information in a concise and aesthetically pleasing manner.
Diagrams simplify the presentation of statistical concepts such as distribution, correlation, and regression. Visualizations make it easier for individuals with varying levels of statistical literacy to grasp these concepts.

In summary, the significance of diagrammatic representation lies in its ability to transform raw data into visual insights, making information more understandable, memorable, and impactful. Whether used in research, business, education, or public communication, visualizations play a crucial role in conveying the story embedded in the data.

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What is data visualization? Presenting data for decision-making

Data visualization is the presentation of data in a graphical format to make it easier for decision makers to see and understand trends, outliers, and patterns in data..

business intelligence data visualization tools analytics

Data visualization definition

Data visualization is the presentation of data in a graphical format such as a plot, graph, or map to make it easier for decision makers to see and understand trends, outliers, and patterns in data.

Maps and charts were among the earliest forms of data visualization. One of the most well-known early examples of data visualization was a flow map created by French civil engineer Charles Joseph Minard in 1869 to help understand what Napoleon’s troops suffered in the disastrous Russian campaign of 1812. The map used two dimensions to depict the number of troops, distance, temperature, latitude and longitude, direction of travel, and location relative to specific dates.

Today, data visualization encompasses all manners of presenting data visually, from dashboards to reports, statistical graphs, heat maps, plots, infographics, and more.

What is the business value of data visualization?

Data visualization helps people analyze data, especially large volumes of data, quickly and efficiently.

By providing easy-to-understand visual representations of data, it helps employees make more informed decisions based on that data. Presenting data in visual form can make it easier to comprehend, enable people to obtain insights more quickly. Visualizations can also make it easier to communicate those insights and to see how independent variables relate to one another. This can help you see trends, understand the frequency of events, and track connections between operations and performance, for example.

Key data visualization benefits include:

Unlocking the value big data by enabling people to absorb vast amounts of data at a glance
Increasing the speed of decision-making by providing access to real-time and on-demand information
Identifying errors and inaccuracies in data quickly

What are the types of data visualization?

There are myriad ways of visualizing data, but data design agency The Datalabs Agency breaks data visualization into two basic categories:

Exploration: Exploration visualizations help you understand what the data is telling you.
Explanation: Explanation visualizations tell a story to an audience using data .

It is essential to understand which of those two ends a given visualization is intended to achieve. The Data Visualisation Catalogue , a project developed by freelance designer Severino Ribecca, is a library of different information visualization types.

Some of the most common specific types of visualizations include:

2D area: These are typically geospatial visualizations. For example, cartograms use distortions of maps to convey information such as population or travel time. Choropleths use shades or patterns on a map to represent a statistical variable, such as population density by state.

Temporal: These are one-dimensional linear visualizations that have a start and finish time. Examples include a time series, which presents data like website visits by day or month, and Gantt charts, which illustrate project schedules.

Multidimensional: These common visualizations present data with two or more dimensions. Examples include pie charts, histograms, and scatter plots.

Hierarchical: These visualizations show how groups relate to one another. Tree diagrams are an example of a hierarchical visualization that shows how larger groups encompass sets of smaller groups.

Network: Network visualizations show how data sets are related to one another in a network. An example is a node-link diagram, also known as a network graph , which uses nodes and link lines to show how things are interconnected.

What are some data visualization examples?

Tableau has collected what it considers to be 10 of the best data visualization examples . Number one on Tableau’s list is Minard’s map of Napoleon’s march to Moscow, mentioned above. Other prominent examples include:

A dot map created by English physician John Snow in 1854 to understand the cholera outbreak in London that year. The map used bar graphs on city blocks to indicate cholera deaths at each household in a London neighborhood. The map showed that the worst-affected households were all drawing water from the same well, which eventually led to the insight that wells contaminated by sewage had caused the outbreak.
An animated age and gender demographic breakdown pyramid created by Pew Research Center as part of its The Next America project , published in 2014. The project is filled with innovative data visualizations. This one shows how population demographics have shifted since the 1950s, with a pyramid of many young people at the bottom and very few older people at the top in the 1950s to a rectangular shape in 2060.
A collection of four visualizations by Hanah Anderson and Matt Daniels of The Pudding that illustrate gender disparity in pop culture by breaking down the scripts of 2,000 movies and tallying spoken lines of dialogue for male and female characters. The visualizations include a breakdown of Disney movies, the overview of 2,000 scripts, a gradient bar with which users can search for specific movies, and a representation of age biases shown toward male and female roles.

Data visualization tools

Data visualization software encompasses many applications, tools, and scripts. They provide designers with the tools they need to create visual representations of large data sets. Some of the most popular include the following:

Domo: Domo is a cloud software company that specializes in business intelligence tools and data visualization. It focuses on business-user deployed dashboards and ease of use, making it a good choice for small businesses seeking to create custom apps.

Dundas BI: Dundas BI is a BI platform for visualizing data, building and sharing dashboards and reports, and embedding analytics.

Infogram: Infogram is a drag-and-drop visualization tool for creating visualizations for marketing reports, infographics, social media posts, dashboards, and more. Its ease-of-use makes it a good option for non-designers as well.

Klipfolio: Klipfolio is designed to enable users to access and combine data from hundreds of services without writing any code. It leverages pre-built, curated instant metrics and a powerful data modeler, making it a good tool for building custom dashboards.

Looker: Now part of Google Cloud, Looker has a plug-in marketplace with a directory of different types of visualizations and pre-made analytical blocks. It also features a drag-and-drop interface.

Microsoft Power BI: Microsoft Power BI is a business intelligence platform integrated with Microsoft Office. It has an easy-to-use interface for making dashboards and reports. It’s very similar to Excel so Excel skills transfer well. It also has a mobile app.

Qlik: Qlik’s Qlik Sense features an “associative” data engine for investigating data and AI-powered recommendations for visualizations. It is continuing to build out its open architecture and multicloud capabilities.

Sisense: Sisense is an end-to-end analytics platform best known for embedded analytics. Many customers use it in an OEM form.

Tableau: One of the most popular data visualization platforms on the market, Tableau is a platform that supports accessing, preparing, analyzing, and presenting data. It’s available in a variety of options, including a desktop app, server, and hosted online versions, and a free, public version. Tableau has a steep learning curve but is excellent for creating interactive charts.

Data visualization certifications

Data visualization skills are in high demand. Individuals with the right mix of experience and skills can demand high salaries. Certifications can help.

Some of the popular certifications include the following:

Data Visualization Nanodegree (Udacity)
Professional Certificate in IBM Data Science (IBM)
Data Visualization with Python (DataCamp)
Data Analysis and Visualization with Power BI (Udacity)
Data Visualization with R (Dataquest)
Visualize Data with Python (Codecademy)
Professional Certificate in Data Analytics and Visualization with Excel and R (IBM)
Data Visualization with Tableau Specialization (UCDavis)
Data Visualization with R (DataCamp)
Excel Skills for Data Analytics and Visualization Specialization (Macquarie University)

Data visualization jobs and salaries

Here are some of the most popular job titles related to data visualization and the average salary for each position, according to data from PayScale .

Data analyst: $64K
Data scientist: $98K
Data visualization specialist: $76K
Senior data analyst: $88K
Senior data scientist: $112K
BI analyst: $65K
Analytics specialist: $71K
Marketing data analyst: $61K

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MCO-03 Research Methodology and Statistical analysis in English Solved Assignment 2024

Mco-03 research methodology and statistical analysis solved assignment 2024.

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Course Code : MCO – 03 Course Title : Research Methodology and Statistical Analysis Assignment Code : MCO – 03 /TMA/2024 Coverage : All Blocks

Attempt all the questions. Q. 1 a) How do you select an appropriate scaling techniques for a research study? Explain the issues Involved in it? b) What is reporting? What are the different stages in the preparation of a report? (10+10) Q. 2 The following table gives the no. of defects per product and its frequency: No. of defects per product Frequency Under 15 32 15-20 50 20-25 75 25-30 130 30-35 145 35-40 105 40-45 85 45-50 50 50 above 20 i) What are the problems you may face in computing standard (20) deviation from the above data? ii) Compute Bowley’s co-efficient of skewness and comment on its value. iii)Do you agree that the suggested method for measuring skewness is an appropriate method? Give reasons of your opinion? Q. 3 Briefly comment on the following: a) “All science are knowledge, but all knowledge is not science”. b) “Index numbers are specialised averages”. c) “The analysis of time series help in knowing current accomplishment”. d) Statistical arguments are often misleading at first, but free discussion clear away statistical fallacies”. (4×5) Q. 4 Write short notes on the following: a) Splicing of Indices. b) Generalization. c) Characteristics of Poisson distribution. d) Sample space. (4×5) Q. 5 Distinguish between the following: a) Pilot study and Pre test. b) Correlation and Regression. c) Estimation and Testing of hypothesis. d) Probability distribution and Frequency distribution.

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The Role of Data Visualization in Presentations

Data visualization in presentations: types and advantages.

Sep 19, 2022

Your presentation should inspire, persuade, and inform your audience without boring them to tears. However, even with a creative mind and polished design skills, infusing life into sticky and data-populated presentation topics can be a tall order. But not if you leverage data visualization.

Data visualization is the representation of data through visual displays such as charts, histograms, maps, tables, dashboards, graphs, and infographics. Integrating data visualization into your presentation makes it easy for your audience to digest, absorb, and remember complex information and data. The American Management Association says visuals and actions make written information 70% more memorable .

Thus, if you want to design a stellar presentation that delights your audience from start to finish, utilize graphical displays to your advantage. Fortunately, as we discuss below, you can employ several types of data visualization in your presentation.

The Different Types of Interactive Data Visualizations

Interactive information visualization helps your audience quickly gather your presentation’s primary insights and takeaways by analyzing the visuals.

Interactive visualizations create a synergetic interaction between your audience and the data, empowering them to summarize and correlate findings more efficiently. They’re especially effective in the corporate world, for instance, when delivering a business process improvement presentation.

While interactive visualizations can take many forms, these are the most prevalent in presentations:

Pie Charts To Show Important Percentages

Pie charts are by far the most effective way of representing data in percentages. A pie chart denotes individual percentages of a whole figure, making it easier to interpret data since percentages tally up to 100%.

The full circle represents the whole figure, while each slice of the pie portrays the individual percentages. Ideally, you should use the pie chart to visualize five to six parts utmost, so it’s legible and not too populated. If you have seven or more sections to compare, go for the donut chart .

Lastly, make good use of color coding to differentiate each wedge of your pie chart as color schemes make your data more memorable. Research has shown that colors improve human memory by boosting concentration and focus.

Bar Chart or Scatter Plots for Easy Data Comparison

Bar charts contrast data along a vertical axis (y-axis) and a horizontal axis (x-axis). The graphical representation created by bar charts makes it easy to compare correlative data. For instance, when comparing the yearly profit revenues of a company, you can display the revenue numbers on the x-axis and the years on the y-axis.

Complete Dashboard Design With Multiple Graphs and Maps

When you need to display geographical data and protracted metrics, a dashboard design that integrates maps and graphs will suffice. You may need multiple graphs to present overlapping information like sales, revenue, and marketing data. Maps are handy when displaying geographical data like election results or meteorological data.

You need ample graphic design knowledge to create aesthetic data visualization designs — like business process flowcharts — to integrate them smoothly into your presentation. Good thing you can hire graphic design experts who understand the assignment inside out and are flexible and prompt.

Why Data Visualization Tools Are Necessary for a Presentation

You need data visualization tools to create all types of visual displays. These tools are software applications designed to render and present raw data in graphical formats, such as pie charts, graphs, and bar charts. Besides handling data rendering, data visualizations tools offer the following benefits:

Tells Your Data Story in an Elegant and Meaningful Way

Data in its raw form is complex and challenging to interpret and understand. It’s hard to tell a perceptive data story using blocks of text only. Given that the attention span for a typical audience is seven minutes , you’ll lose your audience sooner if your presentation is crammed with lots of raw data and statistics.

Conversely, visuals help you tell a compelling data story that your audience can follow without being at sea. Good thing you’ll find a suitable data visualization tool no matter your field of expertise. For instance, you’ll find a tool for creating complex scientific visualizations if you’re a scientist and one for creating simple pie charts if you’re a motivational speaker.

Supports Idea Generation Beyond Just Those in the Field of Statistics

It’s easier for your audience to derive business insights and spot data inaccuracies from a presentation with a lot of data visualizations. By assessing and probing these insights, your audience may get a light-bulb moment that births a conceptual idea with a real-world transformational impact.

With a graphical representation of data, it’s easier for a discerning eye to spot marginal differences in cycles and patterns. These are the subtle insights that decision-makers and top professionals need to implement innovative ideas. Without data visualization tools, it would take a great deal of time to structure raw data in an easy-to-read format that can foster idea generation.

Simplifies Data and Business Processes

If you had to draw all the data visualization examples you need in a presentation by yourself, it would be a huge undertaking that would tie up most of your productive time. But with data visualization tools, it’s simple and less time and resource-intensive. This has multifold benefits for you and your audience.

On the one hand, you’ll prepare your presentation visuals more swiftly. Faster preparation gives you more time to complete other tasks on your tab. On the other hand, your audience will access real-time data in a digested form, making it more valuable to their business processes.

Visualize Data With Ease By Outsourcing Your Presentations

Admittedly, adding data visualizations in your presentations isn’t a no-sweat job. Particularly, when dealing with large-scale data that needs multiple visual and graphic representations, the workflow can easily overwhelm you as there's much design thinking needed. But, creating data visualizations shouldn’t be overwhelming since you can hire presentation design experts like GhostRanch Communications to do all the heavy lifting.

At GhostRanch Communications, we design any graphical and visual representations you need for your presentation. Whether you want 3-D maps, bar graphs, or simple pie charts, we have the tools and talent to deliver exquisite designs that’ll turn heads, close deals, and save you time.

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Q. 3 Briefly comment on the following: a) “The research has to provide answers to the research questions raised.” b) “Visual presentation of data makes comparison easy.” c) “The analysis of time series is of great utility not only to research workers but also to economists, businessmen and scientists, etc.” d) “The interpretation of data is a very difficult task and requires a high degree of skill, care, judgment, and objectivity.”

IGNOU ASSIGNMENT

Course Code : MCO – 03

Course Title : Research Methodology and Statistical

Analysis

Assignment Code : MCO - 03 /TMA/2022-23

Coverage : All Blocks

Q. 3 Briefly comment on the following:

a) “The research has to provide answers to the research questions raised.”

b) “Visual presentation of data makes comparison easy.”

c) “The analysis of time series is of great utility not only to research workers but also to economists, businessmen and scientists, etc.”

d) “The interpretation of data is a very difficult task and requires a high degree of skill, care, judgment, and objectivity.”

The statement "The research has to provide answers to the research questions raised" is a fundamental principle of scientific inquiry. Research questions are the starting point of any scientific investigation, and the purpose of research is to provide answers to those questions.

Research questions are important because they help to define the scope of the study, guide the selection of appropriate research methods, and provide a clear focus for data collection and analysis. Without clear research questions, a study may lack direction, and the results may be difficult to interpret or apply.

Furthermore, research questions provide a basis for evaluating the success of a study. If the research questions are not answered or the answers are ambiguous, then the study may not have been successful in achieving its goals.

Therefore, it is essential that research provides answers to the research questions raised. This ensures that the study is focused, relevant, and useful, and that it contributes to the advancement of knowledge in the field. Ultimately, research should aim to address important questions and provide insights that have practical implications for society.

The statement "Visual presentation of data makes comparison easy" is an accurate assessment of the importance of data visualization in research and decision-making processes. Visualizations can be used to represent complex data sets in a way that is easily interpretable and understandable to a wide range of audiences.

When data is presented visually, patterns and relationships that may not be immediately apparent in the raw data can become more apparent. Visualizations can help researchers and decision-makers identify trends, outliers, and potential relationships between variables, leading to deeper insights and better-informed decisions.

Furthermore, visualizations allow for easy comparison between different data sets, variables, or time periods. Through the use of charts, graphs, and other visualizations, it becomes easier to see how different factors relate to each other and how they change over time. This can be particularly useful in fields such as finance, where trends and changes in data can have significant implications for investments and economic decisions.

Visualizations can also be effective tools for communicating research findings to non-expert audiences. By presenting data in a visually appealing and easy-to-understand way, researchers can engage a wider audience and convey complex findings in a more accessible manner.

In conclusion, the visual presentation of data is a crucial aspect of research and decision-making. Through effective data visualization techniques, complex data sets can be represented in a way that is easily interpretable, facilitating deeper insights and better-informed decisions.

The statement "The analysis of time series is of great utility not only to research workers but also to economists, businessmen and scientists, etc." is certainly true. Time series data is a valuable tool for studying trends and patterns over time and can be used by professionals across many different fields.

For research workers, time series analysis can be used to examine complex systems and to develop models that can help predict future events. Economists can use time series data to study economic indicators and make informed decisions about the future of the economy. Businessmen can use time series analysis to track sales and market trends and make strategic decisions about future investments or product development. Scientists can use time series analysis to study phenomena such as climate change or disease outbreaks and to develop models that can help predict future events.

The ability to analyze time series data has become increasingly important in today's data-driven world. With the rise of big data, the analysis of time series data has become an essential tool for understanding trends, identifying patterns, and making informed decisions about the future. Therefore, professionals in a variety of fields can benefit from understanding and utilizing time series analysis techniques.

The statement "The interpretation of data is a very difficult task and requires a high degree of skill, care, judgment, and objectivity" is a fundamental truth of scientific research. Data interpretation involves making sense of the results obtained from data collection and analysis, and drawing conclusions based on these results. This requires a combination of technical expertise, analytical skills, and critical thinking.

The process of data interpretation involves several challenges. Firstly, the data must be examined for accuracy and completeness to ensure that it is reliable and valid. Secondly, researchers must be careful to avoid bias or personal opinions when interpreting the data. This requires a high degree of objectivity and impartiality. Thirdly, data interpretation requires a high degree of skill and judgment to determine the significance of the results obtained and to draw accurate conclusions.

In addition, data interpretation involves contextualizing the findings within the broader research literature, and considering potential alternative explanations for the results obtained. This requires a deep understanding of the research topic and a broad knowledge of relevant research.

Therefore, the interpretation of data is a complex and demanding task that requires a combination of technical expertise, analytical skills, critical thinking, judgment, care, and objectivity. Researchers must be diligent in their approach, ensuring that their interpretations are based on sound reasoning and evidence, and that their conclusions are valid and reliable. The accuracy and reliability of research findings depend on the quality of the data interpretation, making it a crucial aspect of the research process.

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Microsoft fabric updates blog.

Microsoft Fabric May 2024 Update

Monthly Update

Welcome to the May 2024 update.

Here are a few, select highlights of the many we have for Fabric. You can now ask Copilot questions about data in your model, Model Explorer and authoring calculation groups in Power BI desktop is now generally available, and Real-Time Intelligence provides a complete end-to-end solution for ingesting, processing, analyzing, visualizing, monitoring, and acting on events.

There is much more to explore, please continue to read on.

Microsoft Build Announcements

At Microsoft Build 2024, we are thrilled to announce a huge array of innovations coming to the Microsoft Fabric platform that will make Microsoft Fabric’s capabilities even more robust and even customizable to meet the unique needs of each organization. To learn more about these changes, read the “ Unlock real-time insights with AI-powered analytics in Microsoft Fabric ” announcement blog by Arun Ulag.

Fabric Roadmap Update

Last October at the Microsoft Power Platform Community Conference we announced the release of the Microsoft Fabric Roadmap . Today we have updated that roadmap to include the next semester of Fabric innovations. As promised, we have merged Power BI into this roadmap to give you a single, unified road map for all of Microsoft Fabric. You can find the Fabric Roadmap at https://aka.ms/FabricRoadmap .

We will be innovating our Roadmap over the coming year and would love to hear your recommendation ways that we can make this experience better for you. Please submit suggestions at https://aka.ms/FabricIdeas .

Earn a discount on your Microsoft Fabric certification exam!

We’d like to thank the thousands of you who completed the Fabric AI Skills Challenge and earned a free voucher for Exam DP-600 which leads to the Fabric Analytics Engineer Associate certification.

If you earned a free voucher, you can find redemption instructions in your email. We recommend that you schedule your exam now, before your discount voucher expires on June 24 th . All exams must be scheduled and completed by this date.

If you need a little more help with exam prep, visit the Fabric Career Hub which has expert-led training, exam crams, practice tests and more.

Missed the Fabric AI Skills Challenge? We have you covered. For a limited time , you could earn a 50% exam discount by taking the Fabric 30 Days to Learn It Challenge .

Modern Tooltip now on by Default

Matrix layouts, line updates, on-object interaction updates, publish to folders in public preview, you can now ask copilot questions about data in your model (preview), announcing general availability of dax query view, copilot to write and explain dax queries in dax query view public preview updates, new manage relationships dialog, refreshing calculated columns and calculated tables referencing directquery sources with single sign-on, announcing general availability of model explorer and authoring calculation groups in power bi desktop, microsoft entra id sso support for oracle database, certified connector updates, view reports in onedrive and sharepoint with live connected semantic models, storytelling in powerpoint – image mode in the power bi add-in for powerpoint, storytelling in powerpoint – data updated notification, git integration support for direct lake semantic models.

Editor’s pick of the quarter
New visuals in AppSource
Financial Reporting Matrix by Profitbase
Horizon Chart by Powerviz

Milestone Trend Analysis Chart by Nova Silva

Sunburst Chart by Powerviz
Stacked Bar Chart with Line by JTA

Fabric Automation

Streamlining fabric admin apis, microsoft fabric workload development kit, external data sharing, apis for onelake data access roles, shortcuts to on-premises and network-restricted data, copilot for data warehouse.

Unlocking Insights through Time: Time travel in Data warehouse

Copy Into enhancements

Faster workspace resource assignment powered by just in time database attachment, runtime 1.3 (apache spark 3.5, delta lake 3.1, r 4.3.3, python 3.11) – public preview, native execution engine for fabric runtime 1.2 (apache spark 3.4) – public preview , spark run series analysis, comment @tagging in notebook, notebook ribbon upgrade, notebook metadata update notification, environment is ga now, rest api support for workspace data engineering/science settings, fabric user data functions (private preview), introducing api for graphql in microsoft fabric (preview), copilot will be enabled by default, the ai and copilot setting will be automatically delegated to capacity admins, abuse monitoring no longer stores your data, real-time hub, source from real-time hub in enhanced eventstream, use real-time hub to get data in kql database in eventhouse, get data from real-time hub within reflexes, eventstream edit and live modes, default and derived streams, route streams based on content in enhanced eventstream, eventhouse is now generally available, eventhouse onelake availability is now generally available, create a database shortcut to another kql database, support for ai anomaly detector, copilot for real-time intelligence, eventhouse tenant level private endpoint support, visualize data with real-time dashboards, new experience for data exploration, create triggers from real-time hub, set alert on real-time dashboards, taking action through fabric items, general availability of the power query sdk for vs code, refresh the refresh history dialog, introducing data workflows in data factory, introducing trusted workspace access in fabric data pipelines.

Introducing Blob Storage Event Triggers for Data Pipelines
Parent/child pipeline pattern monitoring improvements

Fabric Spark job definition activity now available

Hd insight activity now available, modern get data experience in data pipeline.

Power BI tooltips are embarking on an evolution to enhance their functionality. To lay the groundwork, we are introducing the modern tooltip as the new default , a feature that many users may already recognize from its previous preview status. This change is more than just an upgrade; it’s the first step in a series of remarkable improvements. These future developments promise to revolutionize tooltip management and customization, offering possibilities that were previously only imaginable. As we prepare for the general availability of the modern tooltip, this is an excellent opportunity for users to become familiar with its features and capabilities.

Discover the full potential of the new tooltip feature by visiting our dedicated blog . Dive into the details and explore the comprehensive vision we’ve crafted for tooltips, designed to enhance your Power BI experience.

We’ve listened to our community’s feedback on improving our tabular visuals (Table and Matrix), and we’re excited to initiate their transformation. Drawing inspiration from the familiar PivotTable in Excel , we aim to build new features and capabilities upon a stronger foundation. In our May update, we’re introducing ‘ Layouts for Matrix .’ Now, you can select from compact , outline , or tabular layouts to alter the arrangement of components in a manner akin to Excel.

As an extension of the new layout options, report creators can now craft custom layout patterns by repeating row headers. This powerful control, inspired by Excel’s PivotTable layout, enables the creation of a matrix that closely resembles the look and feel of a table. This enhancement not only provides greater flexibility but also brings a touch of Excel’s intuitive design to Power BI’s matrix visuals. Only available for Outline and Tabular layouts.

To further align with Excel’s functionality, report creators now have the option to insert blank rows within the matrix. This feature allows for the separation of higher-level row header categories, significantly enhancing the readability of the report. It’s a thoughtful addition that brings a new level of clarity and organization to Power BI’s matrix visuals and opens a path for future enhancements for totals/subtotals and rows/column headers.

We understand your eagerness to delve deeper into the matrix layouts and grasp how these enhancements fulfill the highly requested features by our community. Find out more and join the conversation in our dedicated blog , where we unravel the details and share the community-driven vision behind these improvements.

Following last month’s introduction of the initial line enhancements, May brings a groundbreaking set of line capabilities that are set to transform your Power BI experience:

Hide/Show lines : Gain control over the visibility of your lines for a cleaner, more focused report.
Customized line pattern : Tailor the pattern of your lines to match the style and context of your data.
Auto-scaled line pattern : Ensure your line patterns scale perfectly with your data, maintaining consistency and clarity.
Line dash cap : Customize the end caps of your customized dashed lines for a polished, professional look.
Line upgrades across other line types : Experience improvements in reference lines, forecast lines, leader lines, small multiple gridlines, and the new card’s divider line.

These enhancements are not to be missed. We recommend visiting our dedicated blog for an in-depth exploration of all the new capabilities added to lines, keeping you informed and up to date.

This May release, we’re excited to introduce on-object formatting support for Small multiples , Waterfall , and Matrix visuals. This new feature allows users to interact directly with these visuals for a more intuitive and efficient formatting experience. By double-clicking on any of these visuals, users can now right-click on the specific visual component they wish to format, bringing up a convenient mini-toolbar. This streamlined approach not only saves time but also enhances the user’s ability to customize and refine their reports with ease.

We’re also thrilled to announce a significant enhancement to the mobile reporting experience with the introduction of the pane manager for the mobile layout view. This innovative feature empowers users to effortlessly open and close panels via a dedicated menu, streamlining the design process of mobile reports.

We recently announced a public preview for folders in workspaces, allowing you to create a hierarchical structure for organizing and managing your items. In the latest Desktop release, you can now publish your reports to specific folders in your workspace.

When you publish a report, you can choose the specific workspace and folder for your report. The interface is simplistic and easy to understand, making organizing your Power BI content from Desktop better than ever.

To publish reports to specific folders in the service, make sure the “Publish dialogs support folder selection” setting is enabled in the Preview features tab in the Options menu.

Learn more about folders in workspaces.

We’re excited to preview a new capability for Power BI Copilot allowing you to ask questions about the data in your model! You could already ask questions about the data present in the visuals on your report pages – and now you can go deeper by getting answers directly from the underlying model. Just ask questions about your data, and if the answer isn’t already on your report, Copilot will then query your model for the data instead and return the answer to your question in the form of a visual!

We’re starting this capability off in both Edit and View modes in Power BI Service. Because this is a preview feature, you’ll need to enable it via the preview toggle in the Copilot pane. You can learn more about all the details of the feature in our announcement post here! (will link to announcement post)

We are excited to announce the general availability of DAX query view. DAX query view is the fourth view in Power BI Desktop to run DAX queries on your semantic model.

DAX query view comes with several ways to help you be as productive as possible with DAX queries.

Quick queries. Have the DAX query written for you from the context menu of tables, columns, or measures in the Data pane of DAX query view. Get the top 100 rows of a table, statistics of a column, or DAX formula of a measure to edit and validate in just a couple clicks!
DirectQuery model authors can also use DAX query view. View the data in your tables whenever you want!
Create and edit measures. Edit one or multiple measures at once. Make changes and see the change in action in a DA query. Then update the model when you are ready. All in DAX query view!
See the DAX query of visuals. Investigate the visuals DAX query in DAX query view. Go to the Performance Analyzer pane and choose “Run in DAX query view”.
Write DAX queries. You can create DAX queries with Intellisense, formatting, commenting/uncommenting, and syntax highlighting. And additional professional code editing experiences such as “Change all occurrences” and block folding to expand and collapse sections. Even expanded find and replace options with regex.

Learn more about DAX query view with these resources:

Deep dive blog: https://powerbi.microsoft.com/blog/deep-dive-into-dax-query-view-and-writing-dax-queries/
Learn more: https://learn.microsoft.com/power-bi/transform-model/dax-query-view
Video: https://youtu.be/oPGGYLKhTOA?si=YKUp1j8GoHHsqdZo

DAX query view includes an inline Fabric Copilot to write and explain DAX queries, which remains in public preview. This month we have made the following updates.

Run the DAX query before you keep it . Previously the Run button was disabled until the generated DAX query was accepted or Copilot was closed. Now you can Run the DAX query then decide to Keep or Discard the DAX query.

2. Conversationally build the DAX query. Previously the DAX query generated was not considered if you typed additional prompts and you had to keep the DAX query, select it again, then use Copilot again to adjust. Now you can simply adjust by typing in additional user prompts.

3. Syntax checks on the generated DAX query. Previously there was no syntax check before the generated DAX query was returned. Now the syntax is checked, and the prompt automatically retried once. If the retry is also invalid, the generated DAX query is returned with a note that there is an issue, giving you the option to rephrase your request or fix the generated DAX query.

4. Inspire buttons to get you started with Copilot. Previously nothing happened until a prompt was entered. Now click any of these buttons to quickly see what you can do with Copilot!

Learn more about DAX queries with Copilot with these resources:

Deep dive blog: https://powerbi.microsoft.com/en-us/blog/deep-dive-into-dax-query-view-with-copilot/
Learn more: https://learn.microsoft.com/en-us/dax/dax-copilot
Video: https://www.youtube.com/watch?v=0kE3TE34oLM

We are excited to introduce you to the redesigned ‘Manage relationships’ dialog in Power BI Desktop! To open this dialog simply select the ‘Manage relationships’ button in the modeling ribbon.

Once opened, you’ll find a comprehensive view of all your relationships, along with their key properties, all in one convenient location. From here you can create new relationships or edit an existing one.

Additionally, you have the option to filter and focus on specific relationships in your model based on cardinality and cross filter direction.

Learn more about creating and managing relationships in Power BI Desktop in our documentation .

Ever since we released composite models on Power BI semantic models and Analysis Services , you have been asking us to support the refresh of calculated columns and tables in the Service. This month, we have enabled the refresh of calculated columns and tables in Service for any DirectQuery source that uses single sign-on authentication. This includes the sources you use when working with composite models on Power BI semantic models and Analysis Services.

Previously, the refresh of a semantic model that uses a DirectQuery source with single-sign-on authentication failed with one of the following error messages: “Refresh is not supported for datasets with a calculated table or calculated column that depends on a table which references Analysis Services using DirectQuery.” or “Refresh over a dataset with a calculated table or a calculated column which references a Direct Query data source is not supported.”

Starting today, you can successfully refresh the calculated table and calculated columns in a semantic model in the Service using specific credentials as long as:

You used a shareable cloud connection and assigned it and/or.
Enabled granular access control for all data connection types.

Here’s how to do this:

Create and publish your semantic model that uses a single sign-on DirectQuery source. This can be a composite model but doesn’t have to be.
In the semantic model settings, under Gateway and cloud connections , map each single sign-on DirectQuery connection to a specific connection. If you don’t have a specific connection yet, select ‘Create a connection’ to create it:

If you are creating a new connection, fill out the connection details and click Create , making sure to select ‘Use SSO via Azure AD for DirectQuery queries:

Finally, select the connection for each single sign-on DirectQuery source and select Apply :

2. Either refresh the semantic model manually or plan a scheduled refresh to confirm the refresh now works successfully. Congratulations, you have successfully set up refresh for semantic models with a single sign-on DirectQuery connection that uses calculated columns or calculated tables!

We are excited to announce the general availability of Model Explorer in the Model view of Power BI, including the authoring of calculation groups. Semantic modeling is even easier with an at-a-glance tree view with item counts, search, and in context paths to edit the semantic model items with Model Explorer. Top level semantic model properties are also available as well as the option to quickly create relationships in the properties pane. Additionally, the styling for the Data pane is updated to Fluent UI also used in Office and Teams.

A popular community request from the Ideas forum, authoring calculation groups is also included in Model Explorer. Calculation groups significantly reduce the number of redundant measures by allowing you to define DAX formulas as calculation items that can be applied to existing measures. For example, define a year over year, prior month, conversion, or whatever your report needs in DAX formula once as a calculation item and reuse it with existing measures. This can reduce the number of measures you need to create and make the maintenance of the business logic simpler.

Available in both Power BI Desktop and when editing a semantic model in the workspace, take your semantic model authoring to the next level today!

Learn more about Model Explorer and authoring calculation groups with these resources:

Use Model explorer in Power BI (preview) – Power BI | Microsoft Learn
Create calculation groups in Power BI (preview) – Power BI | Microsoft Learn

Data connectivity

We’re happy to announce that the Oracle database connector has been enhanced this month with the addition of Single Sign-On support in the Power BI service with Microsoft Entra ID authentication.

Microsoft Entra ID SSO enables single sign-on to access data sources that rely on Microsoft Entra ID based authentication. When you configure Microsoft Entra SSO for an applicable data source, queries run under the Microsoft Entra identity of the user that interacts with the Power BI report.

We’re pleased to announce the new and updated connectors in this release:

[New] OneStream : The OneStream Power BI Connector enables you to seamlessly connect Power BI to your OneStream applications by simply logging in with your OneStream credentials. The connector uses your OneStream security, allowing you to access only the data you have based on your permissions within the OneStream application. Use the connector to pull cube and relational data along with metadata members, including all their properties. Visit OneStream Power BI Connector to learn more. Find this connector in the other category.
[New] Zendesk Data : A new connector developed by the Zendesk team that aims to go beyond the functionality of the existing Zendesk legacy connector created by Microsoft. Learn more about what this new connector brings.
[New] CCH Tagetik
[Update] Azure Databricks

Are you interested in creating your own connector and publishing it for your customers? Learn more about the Power Query SDK and the Connector Certification program .

Last May, we announced the integration between Power BI and OneDrive and SharePoint. Previously, this capability was limited to only reports with data in import mode. We’re excited to announce that you can now seamlessly view Power BI reports with live connected data directly in OneDrive and SharePoint!

When working on Power BI Desktop with a report live connected to a semantic model in the service, you can easily share a link to collaborate with others on your team and allow them to quickly view the report in their browser. We’ve made it easier than ever to access the latest data updates without ever leaving your familiar OneDrive and SharePoint environments. This integration streamlines your workflows and allows you to access reports within the platforms you already use. With collaboration at the heart of this improvement, teams can work together more effectively to make informed decisions by leveraging live connected semantic models without being limited to data only in import mode.

Utilizing OneDrive and SharePoint allows you to take advantage of built-in version control, always have your files available in the cloud, and utilize familiar and simplistic sharing.

While you told us that you appreciate the ability to limit the image view to only those who have permission to view the report, you asked for changes for the “Public snapshot” mode.

To address some of the feedback we got from you, we have made a few more changes in this area.

Add-ins that were saved as “Public snapshot” can be printed and will not require that you go over all the slides and load the add-ins for permission check before the public image is made visible.
You can use the “Show as saved image” on add-ins that were saved as “Public snapshot”. This will replace the entire add-in with an image representation of it, so the load time might be faster when you are presenting your presentation.

Many of us keep presentations open for a long time, which might cause the data in the presentation to become outdated.

To make sure you have in your slides the data you need, we added a new notification that tells you if more up to date data exists in Power BI and offers you the option to refresh and get the latest data from Power BI.

Developers

Direct Lake semantic models are now supported in Fabric Git Integration , enabling streamlined version control, enhanced collaboration among developers, and the establishment of CI/CD pipelines for your semantic models using Direct Lake.

Learn more about version control, testing, and deployment of Power BI content in our Power BI implementation planning documentation: https://learn.microsoft.com/power-bi/guidance/powerbi-implementation-planning-content-lifecycle-management-overview

Visualizations

Editor’s pick of the quarter .

– Animator for Power BI Innofalls Charts SuperTables Sankey Diagram for Power BI by ChartExpo Dynamic KPI Card by Sereviso Shielded HTML Viewer Text search slicer

New visuals in AppSource

Mapa Polski – Województwa, Powiaty, Gminy Workstream Income Statement Table

Gas Detection Chart

Seasonality Chart PlanIn BI – Data Refresh Service

Chart Flare

PictoBar ProgBar

Counter Calendar Donut Chart image

Financial Reporting Matrix by Profitbase

Making financial statements with a proper layout has just become easier with the latest version of the Financial Reporting Matrix.

Users are now able to specify which rows should be classified as cost-rows, which will make it easier to get the conditional formatting of variances correctly:

Selecting a row, and ticking “is cost” will tag the row as cost. This can be used in conditional formatting to make sure that positive variances on expenses are a bad for the result, while a positive variance on an income row is good for the result.

The new version also includes more flexibility in measuring placement and column subtotals.

Measures can be placed either:

Default (below column headers)
Above column headers

Conditionally hide columns
+ much more

Highlighted new features:

Measure placement – In rows
Select Column Subtotals
New Format Pane design
Row Options

Get the visual from AppSource and find more videos here !

Horizon Chart by Powerviz

A Horizon Chart is an advanced visual, for time-series data, revealing trends and anomalies. It displays stacked data layers, allowing users to compare multiple categories while maintaining data clarity. Horizon Charts are particularly useful to monitor and analyze complex data over time, making this a valuable visual for data analysis and decision-making.

Key Features:

Horizon Styles: Choose Natural, Linear, or Step with adjustable scaling.
Layer: Layer data by range or custom criteria. Display positive and negative values together or separately on top.
Reference Line : Highlight patterns with X-axis lines and labels.
Colors: Apply 30+ color palettes and use FX rules for dynamic coloring.
Ranking: Filter Top/Bottom N values, with “Others”.
Gridline: Add gridlines to the X and Y axis.
Custom Tooltip: Add highest, lowest, mean, and median points without additional DAX.
Themes: Save designs and share seamlessly with JSON files.

Other features included are ranking, annotation, grid view, show condition, and accessibility support.

Business Use Cases: Time-Series Data Comparison, Environmental Monitoring, Anomaly Detection

🔗 Try Horizon Chart for FREE from AppSource

📊 Check out all features of the visual: Demo file

📃 Step-by-step instructions: Documentation

💡 YouTube Video: Video Link

📍 Learn more about visuals: https://powerviz.ai/

✅ Follow Powerviz : https://lnkd.in/gN_9Sa6U

Exciting news! Thanks to your valuable feedback, we’ve enhanced our Milestone Trend Analysis Chart even further. We’re thrilled to announce that you can now switch between horizontal and vertical orientations, catering to your preferred visualization style.

The Milestone Trend Analysis (MTA) Chart remains your go-to tool for swiftly identifying deadline trends, empowering you to take timely corrective actions. With this update, we aim to enhance deadline awareness among project participants and stakeholders alike.

In our latest version, we seamlessly navigate between horizontal and vertical views within the familiar Power BI interface. No need to adapt to a new user interface – enjoy the same ease of use with added flexibility. Plus, it benefits from supported features like themes, interactive selection, and tooltips.

What’s more, ours is the only Microsoft Certified Milestone Trend Analysis Chart for Power BI, ensuring reliability and compatibility with the platform.

Ready to experience the enhanced Milestone Trend Analysis Chart? Download it from AppSource today and explore its capabilities with your own data – try for free!

We welcome any questions or feedback at our website: https://visuals.novasilva.com/ . Try it out and elevate your project management insights now!

Sunburst Chart by Powerviz

Powerviz’s Sunburst Chart is an interactive tool for hierarchical data visualization. With this chart, you can easily visualize multiple columns in a hierarchy and uncover valuable insights. The concentric circle design helps in displaying part-to-whole relationships.

Arc Customization: Customize shapes and patterns.
Color Scheme: Accessible palettes with 30+ options.
Centre Circle: Design an inner circle with layers. Add text, measure, icons, and images.
Conditional Formatting: Easily identify outliers based on measure or category rules.
Labels: Smart data labels for readability.
Image Labels: Add an image as an outer label.
Interactivity: Zoom, drill down, cross-filtering, and tooltip features.

Other features included are annotation, grid view, show condition, and accessibility support.

Business Use Cases:

Sales and Marketing: Market share analysis and customer segmentation.
Finance : Department budgets and expenditures distribution.
Operations : Supply chain management.
Education : Course structure, curriculum creation.
Human Resources : Organization structure, employee demographics.

🔗 Try Sunburst Chart for FREE from AppSource

Stacked Bar Chart with Line by JTA

Clustered bar chart with the possibility to stack one of the bars

Stacked Bar Chart with Line by JTA seamlessly merges the simplicity of a traditional bar chart with the versatility of a stacked bar, revolutionizing the way you showcase multiple datasets in a single, cohesive display.

Unlocking a new dimension of insight, our visual features a dynamic line that provides a snapshot of data trends at a glance. Navigate through your data effortlessly with multiple configurations, gaining a swift and comprehensive understanding of your information.

Tailor your visual experience with an array of functionalities and customization options, enabling you to effortlessly compare a primary metric with the performance of an entire set. The flexibility to customize the visual according to your unique preferences empowers you to harness the full potential of your data.

Features of Stacked Bar Chart with Line:

Stack the second bar
Format the Axis and Gridlines
Add a legend
Format the colors and text
Add a line chart
Format the line
Add marks to the line
Format the labels for bars and line

If you liked what you saw, you can try it for yourself and find more information here . Also, if you want to download it, you can find the visual package on the AppSource .

We have added an exciting new feature to our Combo PRO, Combo Bar PRO, and Timeline PRO visuals – Legend field support . The Legend field makes it easy to visually split series values into smaller segments, without the need to use measures or create separate series. Simply add a column with category names that are adjacent to the series values, and the visual will do the following:

Display separate segments as a stack or cluster, showing how each segment contributed to the total Series value.
Create legend items for each segment to quickly show/hide them without filtering.
Apply custom fill colors to each segment.
Show each segment value in the tooltip

Read more about the Legend field on our blog article

Drill Down Combo PRO is made for creators who want to build visually stunning and user-friendly reports. Cross-chart filtering and intuitive drill down interactions make data exploration easy and fun for any user. Furthermore, you can choose between three chart types – columns, lines, or areas; and feature up to 25 different series in the same visual and configure each series independently.

📊 Get Drill Down Combo PRO on AppSource

🌐 Visit Drill Down Combo PRO product page

Documentation | ZoomCharts Website | Follow ZoomCharts on LinkedIn

We are thrilled to announce that Fabric Core REST APIs are now generally available! This marks a significant milestone in the evolution of Microsoft Fabric, a platform that has been meticulously designed to empower developers and businesses alike with a comprehensive suite of tools and services.

The Core REST APIs are the backbone of Microsoft Fabric, providing the essential building blocks for a myriad of functionalities within the platform. They are designed to improve efficiency, reduce manual effort, increase accuracy, and lead to faster processing times. These APIs help with scale operations more easily and efficiently as the volume of work grows, automate repeatable processes with consistency, and enable integration with other systems and applications, providing a streamlined and efficient data pipeline.

The Microsoft Fabric Core APIs encompasses a range of functionalities, including:

Workspace management: APIs to manage workspaces, including permissions.
Item management: APIs for creating, reading, updating, and deleting items, with partial support for data source discovery and granular permissions management planned for the near future.
Job and tenant management: APIs to manage jobs, tenants, and users within the platform.

These APIs adhere to industry standards and best practices, ensuring a unified developer experience that is both coherent and easy to use.

For developers looking to dive into the details of the Microsoft Fabric Core APIs, comprehensive documentation is available. This includes guidelines on API usage, examples, and articles managed in a centralized repository for ease of access and discoverability. The documentation is continuously updated to reflect the latest features and improvements, ensuring that developers have the most current information at their fingertips. See Microsoft Fabric REST API documentation

We’re excited to share an important update we made to the Fabric Admin APIs. This enhancement is designed to simplify your automation experience. Now, you can manage both Power BI and the new Fabric items (previously referred to as artifacts) using the same set of APIs. Before this enhancement, you had to navigate using two different APIs—one for Power BI items and another for new Fabric items. That’s no longer the case.

The APIs we’ve updated include GetItem , ListItems , GetItemAccessDetails , and GetAccessEntities . These enhancements mean you can now query and manage all your items through a single API call, regardless of whether they’re Fabric types or Power BI types. We hope this update makes your work more straightforward and helps you accomplish your tasks more efficiently.

We’re thrilled to announce the public preview of the Microsoft Fabric workload development kit. This feature now extends to additional workloads and offers a robust developer toolkit for designing, developing, and interoperating with Microsoft Fabric using frontend SDKs and backend REST APIs. Introducing the Microsoft Fabric Workload Development Kit .

The Microsoft Fabric platform now provides a mechanism for ISVs and developers to integrate their new and existing applications natively into Fabric’s workload hub. This integration provides the ability to add net new capabilities to Fabric in a consistent experience without leaving their Fabric workspace, thereby accelerating data driven outcomes from Microsoft Fabric.

By downloading and leveraging the development kit , ISVs and software developers can build and scale existing and new applications on Microsoft Fabric and offer them via the Azure Marketplace without the need to ever leave the Fabric environment.

The development kit provides a comprehensive guide and sample code for creating custom item types that can be added to the Fabric workspace. These item types can leverage the Fabric frontend SDKs and backend REST APIs to interact with other Fabric capabilities, such as data ingestion, transformation, orchestration, visualization, and collaboration. You can also embed your own data application into the Fabric item editor using the Fabric native experience components, such as the header, toolbar, navigation pane, and status bar. This way, you can offer consistent and seamless user experience across different Fabric workloads.

This is a call to action for ISVs, software developers, and system integrators. Let’s leverage this opportunity to create more integrated and seamless experiences for our users.

We’re excited about this journey and look forward to seeing the innovative workloads from our developer community.

We are proud to announce the public preview of external data sharing. Sharing data across organizations has become a standard part of day-to-day business for many of our customers. External data sharing, built on top of OneLake shortcuts, enables seamless, in-place sharing of data, allowing you to maintain a single copy of data even when sharing data across tenant boundaries. Whether you’re sharing data with customers, manufacturers, suppliers, consultants, or partners; the applications are endless.

How external data sharing works

Sharing data across tenants is as simple as any other share operation in Fabric. To share data, navigate to the item to be shared, click on the context menu, and then click on External data share . Select the folder or table you want to share and click Save and continue . Enter the email address and an optional message and then click Send .

The data consumer will receive an email containing a share link. They can click on the link to accept the share and access the data within their own tenant.

Click here for more details about external data sharing .

Following the release of OneLake data access roles in public preview, the OneLake team is excited to announce the availability of APIs for managing data access roles. These APIs can be used to programmatically manage granular data access for your lakehouses. Manage all aspects of role management such as creating new roles, editing existing ones, or changing memberships in a programmatic way.

Do you have data stored on-premises or behind a firewall that you want to access and analyze with Microsoft Fabric? With OneLake shortcuts, you can bring on-premises or network-restricted data into OneLake, without any data movement or duplication. Simply install the Fabric on-premises data gateway and create a shortcut to your S3 compatible, Amazon S3, or Google Cloud Storage data source. Then use any of Fabric’s powerful analytics engines and OneLake open APIs to explore, transform, and visualize your data in the cloud.

Try it out today and unlock the full potential of your data with OneLake shortcuts!

Data Warehouse

We are excited to announce Copilot for Data Warehouse in public preview! Copilot for Data Warehouse is an AI assistant that helps developers generate insights through T-SQL exploratory analysis. Copilot is contextualized your warehouse’s schema. With this feature, data engineers and data analysts can use Copilot to:

Generate T-SQL queries for data analysis.
Explain and add in-line code comments for existing T-SQL queries.
Fix broken T-SQL code.
Receive answers regarding general data warehousing tasks and operations.

There are 3 areas where Copilot is surfaced in the Data Warehouse SQL Query Editor:

Code completions when writing a T-SQL query.
Chat panel to interact with the Copilot in natural language.
Quick action buttons to fix and explain T-SQL queries.

Learn more about Copilot for Data Warehouse: aka.ms/data-warehouse-copilot-docs. Copilot for Data Warehouse is currently only available in the Warehouse. Copilot in the SQL analytics endpoint is coming soon.

Unlocking Insights through Time: Time travel in Data warehouse (public preview)

As data volumes continue to grow in today’s rapidly evolving world of Artificial Intelligence, it is crucial to reflect on historical data. It empowers businesses to derive valuable insights that aid in making well-informed decisions for the future. Preserving multiple historical data versions not only incurred significant costs but also presented challenges in upholding data integrity, resulting in a notable impact on query performance. So, we are thrilled to announce the ability to query the historical data through time travel at the T-SQL statement level which helps unlock the evolution of data over time.

The Fabric warehouse retains historical versions of tables for seven calendar days. This retention allows for querying the tables as if they existed at any point within the retention timeframe. Time travel clause can be included in any top level SELECT statement. For complex queries that involve multiple tables, joins, stored procedures, or views, the timestamp is applied just once for the entire query instead of specifying the same timestamp for each table within the same query. This ensures the entire query is executed with reference to the specified timestamp, maintaining the data’s uniformity and integrity throughout the query execution.

From historical trend analysis and forecasting to compliance management, stable reporting and real-time decision support, the benefits of time travel extend across multiple business operations. Embrace the capability of time travel to navigate the data-driven landscape and gain a competitive edge in today’s fast-paced world of Artificial Intelligence.

We are excited to announce not one but two new enhancements to the Copy Into feature for Fabric Warehouse: Copy Into with Entra ID Authentication and Copy Into for Firewall-Enabled Storage!

Entra ID Authentication

When authenticating storage accounts in your environment, the executing user’s Entra ID will now be used by default. This ensures that you can leverage A ccess C ontrol L ists and R ole – B ased a ccess c ontrol to authenticate to your storage accounts when using Copy Into. Currently, only organizational accounts are supported.

How to Use Entra ID Authentication

Ensure your Entra ID organizational account has access to the underlying storage and can execute the Copy Into statement on your Fabric Warehouse.
Run your Copy Into statement without specifying any credentials; the Entra ID organizational account will be used as the default authentication mechanism.

Copy into firewall-enabled storage

The Copy Into for firewall-enabled storage leverages the trusted workspace access functionality ( Trusted workspace access in Microsoft Fabric (preview) – Microsoft Fabric | Microsoft Learn ) to establish a secure and seamless connection between Fabric and your storage accounts. Secure access can be enabled for both blob and ADLS Gen2 storage accounts. Secure access with Copy Into is available for warehouses in workspaces with Fabric Capacities (F64 or higher).

To learn more about Copy into , please refer to COPY INTO (Transact-SQL) – Azure Synapse Analytics and Microsoft Fabric | Microsoft Learn

We are excited to announce the launch of our new feature, Just in Time Database Attachment, which will significantly enhance your first experience, such as when connecting to the Datawarehouse or SQL endpoint or simply opening an item. These actions trigger the workspace resource assignment process, where, among other actions, we attach all necessary metadata of your items, Data warehouses and SQL endpoints, which can be a long process, particularly for workspaces that have a high number of items.

This feature is designed to attach your desired database during the activation process of your workspace, allowing you to execute queries immediately and avoid unnecessary delays. However, all other databases will be attached asynchronously in the background while you are able to execute queries, ensuring a smooth and efficient experience.

Data Engineering

We are advancing Fabric Runtime 1.3 from an Experimental Public Preview to a full Public Preview. Our Apache Spark-based big data execution engine, optimized for both data engineering and science workflows, has been updated and fully integrated into the Fabric platform.

The enhancements in Fabric Runtime 1.3 include the incorporation of Delta Lake 3.1, compatibility with Python 3.11, support for Starter Pools, integration with Environment and library management capabilities. Additionally, Fabric Runtime now enriches the data science experience by supporting the R language and integrating Copilot.

We are pleased to share that the Native Execution Engine for Fabric Runtime 1.2 is currently available in public preview. The Native Execution Engine can greatly enhance the performance for your Spark jobs and queries. The engine has been rewritten in C++ and operates in columnar mode and uses vectorized processing. The Native Execution Engine offers superior query performance – encompassing data processing, ETL, data science, and interactive queries – all directly on your data lake. Overall, Fabric Spark delivers a 4x speed-up on the sum of execution time of all 99 queries in the TPC-DS 1TB benchmark when compared against Apache Spark. This engine is fully compatible with Apache Spark™ APIs (including Spark SQL API).

It is seamless to use with no code changes – activate it and go. Enable it in your environment for your notebooks and your SJDs.

This feature is in the public preview, at this stage of the preview, there is no additional cost associated with using it.

We are excited to announce the Spark Monitoring Run Series Analysis features, which allow you to analyze the run duration trend and performance comparison for Pipeline Spark activity recurring run instances and repetitive Spark run activities from the same Notebook or Spark Job Definition.

Run Series Comparison: Users can compare the duration of a Notebook run with that of previous runs and evaluate the input and output data to understand the reasons behind prolonged run durations.
Outlier Detection and Analysis: The system can detect outliers in the run series and analyze them to pinpoint potential contributing factors.
Detailed Run Instance Analysis: Clicking on a specific run instance provides detailed information on time distribution, which can be used to identify performance enhancement opportunities.
Configuration Insights : Users can view the Spark configuration used for each run, including auto-tuned configurations for Spark SQL queries in auto-tune enabled Notebook runs.

You can access the new feature from the item’s recent runs panel and Spark application monitoring page.

We are excited to announce that Notebook now supports the ability to tag others in comments, just like the familiar functionality of using Office products!

When you select a section of code in a cell, you can add a comment with your insights and tag one or more teammates to collaborate or brainstorm on the specifics. This intuitive enhancement is designed to amplify collaboration in your daily development work.

Moreover, you can easily configure the permissions when tagging someone who doesn’t have the permission, to make sure your code asset is well managed.

We are thrilled to unveil a significant enhancement to the Fabric notebook ribbon, designed to elevate your data science and engineering workflows.

In the new version, you will find the new Session connect control on the Home tab, and now you can start a standard session without needing to run a code cell.

You can also easily spin up a High concurrency session and share the session across multiple notebooks to improve the compute resource utilization. And you can easily attach/leave a high concurrency session with a single click.

The “ View session information ” can navigate you to the session information dialog, where you can find a lot of useful detailed information, as well as configure the session timeout. The diagnostics info is essentially helpful when you need support for notebook issues.

Now you can easily access the powerful “ Data Wrangler ” on Home tab with the new ribbon! You can explore your data with the fancy low-code experience of data wrangler, and the pandas DataFrames and Spark DataFrames are all supported.

We recently made some changes to the Fabric notebook metadata to ensure compliance and consistency:

Notebook file content:

The keyword “trident” has been replaced with “dependencies” in the notebook content. This adjustment ensures consistency and compliance.
Notebook Git format:
The preface of the notebook has been modified from “# Synapse Analytics notebook source” to “# Fabric notebook source”.
Additionally, the keyword “synapse” has been updated to “dependencies” in the Git repo.

The above changes will be marked as ‘uncommitted’ for one time if your workspace is connected to Git. No action is needed in terms of these changes , and there won’t be any breaking scenario within the Fabric platform . If you have any further updates or questions, feel free to share with us.

We are thrilled to announce that the environment is now a generally available item in Microsoft Fabric. During this GA timeframe, we have shipped a few new features of Environment.

Git support

The environment is now Git supported. You can check-in the environment into your Git repo and manipulate the environment locally with its YAML representations and custom library files. After updating the changes from local to Fabric portal, you can publish them by manual action or through REST API.

Deployment pipeline

Deploying environments from one workspace to another is supported. Now, you can deploy the code items and their dependent environments together from development to test and even production.

With the REST APIs, you can have the code-first experience with the same abilities through Fabric portal. We provide a set of powerful APIs to ensure you the efficiency in managing your environment. You can create new environments, update libraries and Spark compute, publish the changes, delete an environment, attach the environment to a notebook, etc., all actions can be done locally in the tools of your choice. The article – Best practice of managing environments with REST API could help you get started with several real-world scenarios.

Resources folder

Resources folder enables managing small resources in the development cycle. The files uploaded in the environment can be accessed from notebooks once they’re attached to the same environment. The manipulation of the files and folders of resources happens in real-time. It could be super powerful, especially when you are collaborating with others.

Sharing your environment with others is also available. We provide several sharing options. By default, the view permission is shared. If you want the recipient to have access to view and use the contents of the environment, sharing without permission customization is the best option. Furthermore, you can grant editing permission to allow recipients to update this environment or grant share permission to allow recipients to reshare this environment with their existing permissions.

We are excited to announce the REST api support for Fabric Data Engineering/Science workspace settings. Data Engineering/Science settings allows users to create/manage their Spark compute, select the default runtime/default environment, enable or disable high concurrency mode or ML autologging.

Now with the REST api support for the Data Engineering/Science settings, you would be able to

Choose the default pool for a Fabric Workspace
Configure the max nodes for Starter pools
Create/Update/Delete the existing Custom Pools, Autoscale and Dynamic allocation properties
Choose Workspace Default Runtime and Environment
Select a default runtime
Select the default environment for the Fabric workspace
Enable or Disable High Concurrency Mode
Enable or Disable ML Auto logging.

Learn more about the Workspace Spark Settings API in our API documentation Workspace Settings – REST API (Spark) | Microsoft Learn

We are excited to give you a sneak peek at the preview of User Data Functions in Microsoft Fabric. User Data Functions gives developers and data engineers the ability to easily write and run applications that integrate with resources in the Fabric Platform. Data engineering often presents challenges with data quality or complex data analytics processing in data pipelines, and using ETL tools may present limited flexibility and ability to customize to your needs. This is where User data functions can be used to run data transformation tasks and perform complex business logic by connecting to your data sources and other workloads in Fabric.

During preview, you will be able to use the following features:

Use the Fabric portal to create new User Data Functions, view and test them.
Write your functions using C#.
Use the Visual Studio Code extension to create and edit your functions.
Connect to the following Fabric-native data sources: Data Warehouse, Lakehouse and Mirrored Databases.

You can now create a fully managed GraphQL API in Fabric to interact with your data in a simple, flexible, and powerful way. We’re excited to announce the public preview of API for GraphQL, a data access layer that allows us to query multiple data sources quickly and efficiently in Fabric by leveraging a widely adopted and familiar API technology that returns more data with less client requests. With the new API for GraphQL in Fabric, data engineers and scientists can create data APIs to connect to different data sources, use the APIs in their workflows, or share the API endpoints with app development teams to speed up and streamline data analytics application development in your business.

You can get started with the API for GraphQL in Fabric by creating an API, attaching a supported data source, then selecting specific data sets you want to expose through the API. Fabric builds the GraphQL schema automatically based on your data, you can test and prototype queries directly in our graphical in-browser GraphQL development environment (API editor), and applications are ready to connect in minutes.

Currently, the following supported data sources can be exposed through the Fabric API for GraphQL:

Microsoft Fabric Data Warehouse
Microsoft Fabric Lakehouse via SQL Analytics Endpoint
Microsoft Fabric Mirrored Databases via SQL Analytics Endpoint

Click here to learn more about how to get started.

Data Science

As you may know, Copilot in Microsoft Fabric requires your tenant administrator to enable the feature from the admin portal. Starting May 20th, 2024, Copilot in Microsoft Fabric will be enabled by default for all tenants. This update is part of our continuous efforts to enhance user experience and productivity within Microsoft Fabric. This new default activation means that AI features like Copilot will be automatically enabled for tenants who have not yet enabled the setting.

We are introducing a new capability to enable Copilot on Capacity level in Fabric. A new option is being introduced in the tenant admin portal, to delegate the enablement of AI and Copilot features to Capacity administrators. This AI and Copilot setting will be automatically delegated to capacity administrators and tenant administrators won’t be able to turn off the delegation.

We also have a cross-geo setting for customers who want to use Copilot and AI features while their capacity is in a different geographic region than the EU data boundary or the US. By default, the cross-geo setting will stay off and will not be delegated to capacity administrators automatically. Tenant administrators can choose whether to delegate this to capacity administrators or not.

Figure 1. Copilot in Microsoft Fabric will be auto enabled and auto delegated to capacity administrators.

Capacity administrators will see the “Copilot and Azure OpenAI Service (preview)” settings under Capacity settings/ Fabric Capacity / <Capacity name> / Delegated tenant settings. By default, the capacity setting will inherit tenant level settings. Capacity administrators can decide whether to override the tenant administrator’s selection. This means that even if Copilot is not enabled on a tenant level, a capacity administrator can choose to enable Copilot for their capacity. With this level of control, we make it easier to control which Fabric workspaces can utilize AI features like Copilot in Microsoft Fabric.

To enhance privacy and trust, we’ve updated our approach to abuse monitoring: previously, we retained data from Copilot in Fabric, including prompt inputs and outputs, for up to 30 days to check for misuse. Following customer feedback, we’ve eliminated this 30-day retention. Now, we no longer store prompt related data, demonstrating our unwavering commitment to your privacy and security. We value your input and take your concerns seriously.

Real-Time Intelligence

This month includes the announcement of Real-Time Intelligence, the next evolution of Real-Time Analytics and Data Activator. With Real-Time Intelligence, Fabric extends to the world of streaming and high granularity data, enabling all users in your organization to collect, analyze and act on this data in a timeline manner making faster and more informed business decisions. Read the full announcement from Build 2024.

Real-Time Intelligence includes a wide range of capabilities across ingestion, processing, analysis, transformation, visualization and taking action. All of this is supported by the Real-Time hub, the central place to discover and manage streaming data and start all related tasks.

Read on for more information on each capability and stay tuned for a series of blogs describing the features in more detail. All features are in Public Preview unless otherwise specified. Feedback on any of the features can be submitted at https://aka.ms/rtiidea

Ingest & Process

Introducing the Real-Time hub
Get Events with new sources of streaming and event data
Source from Real-Time Hub in Enhanced Eventstream
Use Real-Time hub to Get Data in KQL Database in Eventhouse
Get data from Real-Time Hub within Reflexes
Eventstream Edit and Live modes
Default and derived streams
Route data streams based on content

Analyze & Transform

Eventhouse GA
Eventhouse OneLake availability GA
Create a database shortcut to another KQL Database
Support for AI Anomaly Detector
Copilot for Real-Time Intelligence
Tenant-level private endpoints for Eventhouse

Visualize & Act

Visualize data with Real-Time Dashboards
New experience for data exploration
Create triggers from Real-Time Hub
Set alert on Real-time Dashboards
Taking action through Fabric Items

Ingest & Process

Real-Time hub is the single place for all data-in-motion across your entire organization. Several key features are offered in Real-Time hub:

1. Single place for data-in-motion for the entire organization

Real-Time hub enables users to easily discover, ingest, manage, and consume data-in-motion from a wide variety of sources. It lists all the streams and KQL tables that customers can directly act on.

2. Real-Time hub is never empty

All data streams in Fabric automatically show up in the hub. Also, users can subscribe to events in Fabric gaining insights into the health and performance of their data ecosystem.

3. Numerous connectors to simplify data ingestion from anywhere to Real-Time hub

Real-Time hub makes it easy for you to ingest data into Fabric from a wide variety of sources like AWS Kinesis, Kafka clusters, Microsoft streaming sources, sample data and Fabric events using the Get Events experience.

There are 3 tabs in the hub:

Data streams : This tab contains all streams that are actively running in Fabric that user has access to. This includes all streams from Eventstreams and all tables from KQL Databases.
Microsoft sources : This tab contains Microsoft sources (that user has access to) and can be connected to Fabric.
Fabric events : Fabric now has event-driven capabilities to support real-time notifications and data processing. Users can monitor and react to events including Fabric Workspace Item events and Azure Blob Storage events. These events can be used to trigger other actions or workflows, such as invoking a data pipeline or sending a notification via email. Users can also send these events to other destinations via Event Streams.

Learn More

You can now connect to data from both inside and outside of Fabric in a mere few steps. Whether data is coming from new or existing sources, streams, or available events, the Get Events experience allows users to connect to a wide range of sources directly from Real-Time hub, Eventstreams, Eventhouse and Data Activator.

This enhanced capability allows you to easily connect external data streams into Fabric with out-of-box experience, giving you more options and helping you to get real-time insights from various sources. This includes Camel Kafka connectors powered by Kafka connect to access popular data platforms, as well as the Debezium connectors for fetching the Change Data Capture (CDC) streams.

Using Get Events, bring streaming data from Microsoft sources directly into Fabric with a first-class experience. Connectivity to notification sources and discrete events is also included, this enables access to notification events from Azure and other clouds solutions including AWS and GCP. The full set of sources which are currently supported are:

Microsoft sources : Azure Event Hubs, Azure IoT hub
External sources : Google Cloud Pub/Sub, Amazon Kinesis Data Streams, Confluent Cloud Kafka
Change data capture databases : Azure SQL DB (CDC), PostgreSQL DB (CDC), Azure Cosmos DB (CDC), MySQL DB (CDC)
Fabric events : Fabric Workspace Item events, Azure Blob Storage events

Learn More

With enhanced Eventstream, you can now stream data not only from Microsoft sources but also from other platforms like Google Cloud, Amazon Kinesis, Database change data capture streams, etc. using our new messaging connectors. The new Eventstream also lets you acquire and route real-time data not only from stream sources but also from discrete event sources, such as: Azure Blob Storage events, Fabric Workspace Item events.

To use these new sources in Eventstream, simply create an eventstream with choosing “Enhanced Capabilities (preview)”.

You will see the new Eventstream homepage that gives you some choices to begin with. By clicking on the “Add external source”, you will find these sources in the Get events wizard that helps you to set up the source in a few steps. After you add the source to your eventstream, you can publish it to stream the data into your eventstream.

Using Eventstream with discrete sources to turn events into streams for more analysis. You can send the streams to different Fabric data destinations, like Lakehouse and KQL Database. After the events are converted, a default stream will appear in Real-Time Hub. To turn them, click Edit on ribbon, select “Stream events” on the source node, and publish your eventstream.

To transform the stream data or route it to different Fabric destinations based on its content, you can click Edit in ribbon and enter the Edit mode. There you can add event processing operators and destinations.

With Real-Time hub embedded in KQL Database experience, each user in the tenant can view and add streams which they have access to and directly ingest it to a KQL Database table in Eventhouse.

This integration provides each user in the tenant with the ability to access and view data streams they are permitted to. They can now directly ingest these streams into a KQL Database table in Eventhouse. This simplifies the data discovery and ingestion process by allowing users to directly interact with the streams. Users can filter data based on the Owner, Parent and Location and provides additional information such as Endorsement and Sensitivity.

You can access this by clicking on the Get Data button from the Database ribbon in Eventhouse.

This will open the Get Data wizard with Real-Time hub embedded.

You can use events from Real-Time hub directly in reflex items as well. From within the main reflex UI, click ‘Get data’ in the toolbar:

This will open a wizard that allows you to connect to new event sources or browse Real-Time Hub to use existing streams or system events.

Search new stream sources to connect to or select existing streams and tables to be ingested directly by Reflex.

You then have access to the full reflex modeling experience to build properties and triggers over any events from Real-Time hub.

Eventstream offers two distinct modes, Edit and Live, to provide flexibility and control over the development process of your eventstream. If you create a new Eventstream with Enhanced Capabilities enabled, you can modify it in an Edit mode. Here, you can design stream processing operations for your data streams using a no-code editor. Once you complete the editing, you can publish your Eventstream and visualize how it starts streaming and processing data in Live mode .

In Edit mode, you can:

Make changes to an Eventstream without implementing them until you publish the Eventstream. This gives you full control over the development process.
Avoid test data being streamed to your Eventstream. This mode is designed to provide a secure environment for testing without affecting your actual data streams.

For Live mode, you can :

Visualize how your Eventstream streams, transforms, and routes your data streams to various destinations after publishing the changes.
Pause the flow of data on selected sources and destinations, providing you with more control over your data streams being streamed into your Eventstream.

When you create a new Eventstream with Enhanced Capabilities enabled, you can now create and manage multiple data streams within Eventstream, which can then be displayed in the Real-Time hub for others to consume and perform further analysis.

There are two types of streams:

Default stream : Automatically generated when a streaming source is added to Eventstream. Default stream captures raw event data directly from the source, ready for transformation or analysis.
Derived stream : A specialized stream that users can create as a destination within Eventstream. Derived stream can be created after a series of operations such as filtering and aggregating, and then it’s ready for further consumption or analysis by other users in the organization through the Real-Time Hub.

The following example shows that when creating a new Eventstream a default stream alex-es1-stream is automatically generated. Subsequently, a derived stream dstream1 is added after an Aggregate operation within the Eventstream. Both default and derived streams can be found in the Real-Time hub.

Customers can now perform stream operations directly within Eventstream’s Edit mode, instead of embedding in a destination. This enhancement allows you to design stream processing logics and route data streams in the top-level canvas. Custom processing and routing can be applied to individual destinations using built-in operations, allowing for routing to distinct destinations within the Eventstream based on different stream content.

These operations include:

Aggregate : Perform calculations such as SUM, AVG, MIN, and MAX on a column of values and return a single result.
Expand : Expand array values and create new rows for each element within the array.
Filter : Select or filter specific rows from the data stream based on a condition.
Group by : Aggregate event data within a certain time window, with the option to group one or more columns.
Manage Fields : Customize your data streams by adding, removing, or changing data type of a column.
Union : Merge two or more data streams with shared fields (same name and data type) into a unified data stream.

Analyze & Transform

Eventhouse, a cutting-edge database workspace meticulously crafted to manage and store event-based data, is now officially available for general use. Optimized for high granularity, velocity, and low latency streaming data, it incorporates indexing and partitioning for structured, semi-structured, and free text data. With Eventhouse, users can perform high-performance analysis of big data and real-time data querying, processing billions of events within seconds. The platform allows users to organize data into compartments (databases) within one logical item, facilitating efficient data management.

Additionally, Eventhouse enables the sharing of compute and cache resources across databases, maximizing resource utilization. It also supports high-performance queries across databases and allows users to apply common policies seamlessly. Eventhouse offers content-based routing to multiple databases, full view lineage, and high granularity permission control, ensuring data security and compliance. Moreover, it provides a simple migration path from Azure Synapse Data Explorer and Azure Data Explorer, making adoption seamless for existing users.

Engineered to handle data in motion, Eventhouse seamlessly integrates indexing and partitioning into its storing process, accommodating various data formats. This sophisticated design empowers high-performance analysis with minimal latency, facilitating lightning-fast ingestion and querying within seconds. Eventhouse is purpose-built to deliver exceptional performance and efficiency for managing event-based data across diverse applications and industries. Its intuitive features and seamless integration with existing Azure services make it an ideal choice for organizations looking to leverage real-time analytics for actionable insights. Whether it’s analyzing telemetry and log data, time series and IoT data, or financial records, Eventhouse provides the tools and capabilities needed to unlock the full potential of event-based data.

We’re excited to announce that OneLake availability of Eventhouse in Delta Lake format is Generally Available.

Delta Lake is the unified data lake table format chosen to achieve seamless data access across all compute engines in Microsoft Fabric.

The data streamed into Eventhouse is stored in an optimized columnar storage format with full text indexing and supports complex analytical queries at low latency on structured, semi-structured, and free text data.

Enabling data availability of Eventhouse in OneLake means that customers can enjoy the best of both worlds: they can query the data with high performance and low latency in their Eventhouse and query the same data in Delta Lake format via any other Fabric engines such as Power BI Direct Lake mode, Warehouse, Lakehouse, Notebooks, and more.

To learn more, please visit https://learn.microsoft.com/en-gb/fabric/real-time-analytics/one-logical-copy

A database shortcut in Eventhouse is an embedded reference to a source database. The source database can be one of the following:

(Now Available) A KQL Database in Real-Time Intelligence
An Azure Data Explorer database

The behavior exhibited by the database shortcut is similar to that of a follower database

The owner of the source database, the data provider, shares the database with the creator of the shortcut in Real-Time Intelligence, the data consumer. The owner and the creator can be the same person. The database shortcut is attached in read-only mode, making it possible to view and run queries on the data that was ingested into the source KQL Database without ingesting it.

This helps with data sharing scenarios where you can share data in-place either within teams, or even with external customers.

AI Anomaly Detector is an Azure service for high quality detection of multivariate and univariate anomalies in time series. While the standalone version is being retired October 2026, Microsoft open sourced the anomaly detection core algorithms and they are now supported in Microsoft Fabric. Users can leverage these capabilities in Data Science and Real-Time Intelligence workload. AI Anomaly Detector models can be trained in Spark Python notebooks in Data Science workload, while real time scoring can be done by KQL with inline Python in Real-Time Intelligence.

We are excited to announce the Public Preview of Copilot for Real-Time Intelligence. This initial version includes a new capability that translates your natural language questions about your data to KQL queries that you can run and get insights.

Your starting point is a KQL Queryset, that is connected to a KQL Database, or to a standalone Kusto database:

Simply type the natural language question about what you want to accomplish, and Copilot will automatically translate it to a KQL query you can execute. This is extremely powerful for users who may be less familiar with writing KQL queries but still want to get the most from their time-series data stored in Eventhouse.

Stay tuned for more capabilities from Copilot for Real-Time Intelligence!

Customers can increase their network security by limiting access to Eventhouse at a tenant-level, from one or more virtual networks (VNets) via private links. This will prevent unauthorized access from public networks and only permit data plane operations from specific VNets.

Visualize & Act

Real-Time Dashboards have a user-friendly interface, allowing users to quickly explore and analyze their data without the need for extensive technical knowledge. They offer a high refresh frequency, support a range of customization options, and are designed to handle big data.

The following visual types are supported, and can be customized with the dashboard’s user-friendly interface:

You can also define conditional formatting rules to format the visual data points by their values using colors, tags, and icons. Conditional formatting can be applied to a specific set of cells in a predetermined column or to entire rows, and lets you easily identify interesting data points.

Beyond the support visual, Real-Time Dashboards provide several capabilities to allow you to interact with your data by performing slice and dice operations for deeper analysis and gaining different viewpoints.

Parameters are used as building blocks for dashboard filters and can be added to queries to filter the data presented by visuals. Parameters can be used to slice and dice dashboard visuals either directly by selecting parameter values in the filter bar or by using cross-filters.
Cross filters allow you to select a value in one visual and filter all other visuals on that dashboard based on the selected data point.
Drillthrough capability allows you to select a value in a visual and use it to filter the visuals in a target page in the same dashboard. When the target page opens, the value is pushed to the relevant filters.

Real-Time Dashboards can be shared broadly and allow multiple stakeholders to view dynamic, real time, fresh data while easily interacting with it to gain desired insights.

Directly from a real-time dashboard, users can refine their exploration using a user-friendly, form-like interface. This intuitive and dynamic experience is tailored for insights explorers craving insights based on real-time data. Add filters, create aggregations, and switch visualization types without writing queries to easily uncover insights.

With this new feature, insights explorers are no longer bound by the limitations of pre-defined dashboards. As independent explorers, they have the freedom for ad-hoc exploration, leveraging existing tiles to kickstart their journey. Moreover, they can selectively remove query segments, and expand their view of the data landscape.

Dive deep, extract meaningful insights, and chart actionable paths forward, all with ease and efficiency, and without having to write complex KQL queries.

Data Activator allows you to monitor streams of data for various conditions and set up actions to be taken in response. These triggers are available directly within the Real-Time hub and in other workloads in Fabric. When the condition is detected, an action will automatically be kicked off such as sending alerts via email or Teams or starting jobs in Fabric items.

When you browse the Real-Time Hub, you’ll see options to set triggers in the detail pages for streams.

Selecting this will open a side panel where you can configure the events you want to monitor, the conditions you want to look for in the events, and the action you want to take while in the Real-Time hub experience.

Completing this pane creates a new reflex item with a trigger that monitors the selected events and condition for you. Reflexes need to be created in a workspace supported by a Fabric or Power BI Premium capacity – this can be a trial capacity so you can get started with it today!

Data Activator has been able to monitor Power BI report data since it was launched, and we now support monitoring of Real-Time Dashboard visuals in the same way.

From real-time dashboard tiles you can click the ellipsis (…) button and select “Set alert”

This opens the embedded trigger pane, where you can specify what conditions, you are looking for. You can choose whether to send email or Teams messages as the alert when these conditions are met.

When creating a new reflex trigger, from Real-time Hub or within the reflex item itself, you’ll notice a new ‘Run a Fabric item’ option in the Action section. This will create a trigger that starts a new Fabric job whenever its condition is met, kicking off a pipeline or notebook computation in response to Fabric events. A common scenario would be monitoring Azure Blob storage events via Real-Time Hub, and running data pipeline jobs when Blog Created events are detected.

This capability is extremely powerful and moves Fabric from a scheduled driven platform to an event driven platform.

Pipelines, spark jobs, and notebooks are just the first Fabric items we’ll support here, and we’re keen to hear your feedback to help prioritize what else we support. Please leave ideas and votes on https://aka.ms/rtiidea and let us know!

Real-Time Intelligence, along with the Real-Time hub, revolutionizes what’s possible with real-time streaming and event data within Microsoft Fabric.

Learn more and try it today https://aka.ms/realtimeintelligence

Data Factory

Dataflow gen2 .

We are thrilled to announce that the Power Query SDK is now generally available in Visual Studio Code! This marks a significant milestone in our commitment to providing developers with powerful tools to enhance data connectivity and transformation.

The Power Query SDK is a set of tools that allow you as the developer to create new connectors for Power Query experiences available in products such as Power BI Desktop, Semantic Models, Power BI Datamarts, Power BI Dataflows, Fabric Dataflow Gen2 and more.

This new SDK has been in public preview since November of 2022, and we’ve been hard at work improving this experience which goes beyond what the previous Power Query SDK in Visual Studio had to offer.

The latest of these biggest improvements was the introduction of the Test Framework in March of 2024 that solidifies the developer experience that you can have within Visual Studio Code and the Power Query SDK for creating a Power Query connector.

The Power Query SDK extension for Visual Studio will be deprecated by June 30, 2024, so we encourage you to give this new Power Query SDK in Visual Studio Code today if you haven’t.

To get started with the Power Query SDK in Visual Studio Code, simply install it from the Visual Studio Code Marketplace . Our comprehensive documentation and tutorials are available to help you harness the full potential of your data.

Join our vibrant community of developers to share insights, ask questions, and collaborate on exciting projects. Our dedicated support team is always ready to assist you with any queries.

We look forward to seeing the innovative solutions you’ll create with the Power Query SDK in Visual Studio Code. Happy coding!

Introducing a convenient enhancement to the Dataflows Gen2 Refresh History experience! Now, alongside the familiar “X” button in the Refresh History screen, you’ll find a shiny new Refresh Button . This small but mighty addition empowers users to refresh the status of their dataflow refresh history status without the hassle of exiting the refresh history and reopening it. Simply click the Refresh Button , and voilà! Your dataflow’s refresh history status screen is updated, keeping you in the loop with minimal effort. Say goodbye to unnecessary clicks and hello to streamlined monitoring!

[New] OneStream : The OneStream Power Query Connector enables you to seamlessly connect Data Factory to your OneStream applications by simply logging in with your OneStream credentials. The connector uses your OneStream security, allowing you to access only the data you have based on your permissions within the OneStream application. Use the connector to pull cube and relational data along with metadata members, including all their properties. Visit OneStream Power BI Connector to learn more. Find this connector in the other category.

Data workflows

We are excited to announce the preview of ‘Data workflows’, a new feature within the Data Factory that revolutionizes the way you build and manage your code-based data pipelines. Powered by Apache Airflow, Data workflows offer seamless authoring, scheduling, and monitoring experience for Python-based data processes defined as Directed Acyclic Graphs (DAGs). This feature brings a SaaS-like experience to running DAGs in a fully managed Apache Airflow environment, with support for autoscaling , auto-pause , and rapid cluster resumption to enhance cost-efficiency and performance.

It also includes native cloud-based authoring capabilities and comprehensive support for Apache Airflow plugins and libraries.

To begin using this feature:

Access the Microsoft Fabric Admin Portal.
Navigate to Tenant Settings.

Under Microsoft Fabric options, locate and expand the ‘Users can create and use Data workflows (preview)’ section. Note: This action is necessary only during the preview phase of Data workflows.

2. Create a new Data workflow within an existing or new workspace.

3. Add a new Directed Acyclic Graph (DAG) file via the user interface.

4. Save your DAG(s).

5. Use Apache Airflow monitoring tools to observe your DAG executions. In the ribbon, click on Monitor in Apache Airflow.

For additional information, please consult the product documentation . If you’re not already using Fabric capacity, consider signing up for the Microsoft Fabric free trial to evaluate this feature.

Data Pipelines

We are excited to announce a new feature in Fabric that enables you to create data pipelines to access your firewall-enabled Azure Data Lake Storage Gen2 (ADLS Gen2) accounts. This feature leverages the workspace identity to establish a secure and seamless connection between Fabric and your storage accounts.

With trusted workspace access, you can create data pipelines to your storage accounts with just a few clicks. Then you can copy data into Fabric Lakehouse and start analyzing your data with Spark, SQL, and Power BI. Trusted workspace access is available for workspaces in Fabric capacities (F64 or higher). It supports organizational accounts or service principal authentication for storage accounts.

How to use trusted workspace access in data pipelines

Create a workspace identity for your Fabric workspace. You can follow the guidelines provided in Workspace identity in Fabric .

Configure resource instance rules for the Storage account that you want to access from your Fabric workspace. Resource instance rules for Fabric workspaces can only be created through ARM templates. Follow the guidelines for configuring resource instance rules for Fabric workspaces here .

Create a data pipeline to copy data from the firewall enabled ADLS gen2 account to a Fabric Lakehouse.

To learn more about how to use trusted workspace access in data pipelines, please refer to Trusted workspace access in Fabric .

We hope you enjoy this new feature for your data integration and analytics scenarios. Please share your feedback and suggestions with us by leaving a comment here.

Introducing Blob Storage Event Triggers for Data Pipelines

A very common use case among data pipeline users in a cloud analytics solution is to trigger your pipeline when a file arrives or is deleted. We have introduced Azure Blob storage event triggers as a public preview feature in Fabric Data Factory Data Pipelines. This utilizes the Fabric Reflex alerts capability that also leverages Event Streams in Fabric to create event subscriptions to your Azure storage accounts.

Parent/Child pipeline pattern monitoring improvements

Today, in Fabric Data Factory Data Pipelines, when you call another pipeline using the Invoke Pipeline activity, the child pipeline is not visible in the monitoring view. We have made updates to the Invoke Pipeline activity so that you can view your child pipeline runs. This requires an upgrade to any pipelines that you have in Fabric that already use the current Invoke Pipeline activity. You will be prompted to upgrade when you edit your pipeline and then provide a connection to your workspace to authenticate. Another additional new feature that will light up with this invoke pipeline activity update is the ability to invoke pipeline across workspaces in Fabric.

We are excited to announce the availability of the Fabric Spark job definition activity for data pipelines. With this new activity, you will be able to run a Fabric Spark Job definition directly in your pipeline. Detailed monitoring capabilities of your Spark Job definition will be coming soon!

To learn more about this activity, read https://aka.ms/SparkJobDefinitionActivity

We are excited to announce the availability of the Azure HDInsight activity for data pipelines. The Azure HDInsight activity allows you to execute Hive queries, invoke a MapReduce program, execute Pig queries, execute a Spark program, or a Hadoop Stream program. Invoking either of the 5 activities can be done in a singular Azure HDInsight activity, and you can invoke this activity using your own or on-demand HDInsight cluster.

To learn more about this activity, read https://aka.ms/HDInsightsActivity

We are thrilled to share the new Modern Get Data experience in Data Pipeline to empower users intuitively and efficiently discover the right data, right connection info and credentials.

In the data destination, users can easily set destination by creating a new Fabric item or creating another destination or selecting existing Fabric item from OneLake data hub.

In the source tab of Copy activity, users can conveniently choose recent used connections from drop down or create a new connection using “More” option to interact with Modern Get Data experience.

Microsoft Fabric March 2024 Update

Welcome to the March 2024 update. We have a lot of great features this month including OneLake File Explorer, Autotune Query Tuning, Test Framework for Power Query SDK in VS Code, and many more! Earn a free Microsoft Fabric certification exam! We are thrilled to announce the general availability of Exam DP-600, which leads to … Continue reading “Microsoft Fabric March 2024 Update”

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MCO-03 Research Methodology and Statistical analysis in English Solved
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Visual presentation of statistical data has become more popular
MCO - 03 Research Methodology And Statistical Analysis Solved Assignment for 2018-19. Question 3. Briefly comment on the following: a) Visual presentation of statistical data has become more popular. ANSWER: Visual presentation of statistical data has become more popular. Click here to DOWNLOAD answer: Prev Question <<.
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2. Create an infographic that visually represents data on a recent social or economic trend in India. Focus on clarity, accuracy, and aesthetic appeal in your design. Explain how your infographic makes complex data more accessible and engaging to the general public, and discuss the importance of visual data representation in journalism.
Data Visualization in Presentations: Types and Advantages
Data visualization is the representation of data through visual displays such as charts, histograms, maps, tables, dashboards, graphs, and infographics. Integrating data visualization into your presentation makes it easy for your audience to digest, absorb, and remember complex information and data. The American Management Association says ...
Q. 3 Briefly comment on the following: a) "The research has ...
The statement "Visual presentation of data makes comparison easy" is an accurate assessment of the importance of data visualization in research and decision-making processes. Visualizations can be used to represent complex data sets in a way that is easily interpretable and understandable to a wide range of audiences.
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MCO-03 Research Methodology and Statistical analysis in English Solved
MCO-03 Solved Assignment 2024: University: IGNOU: Service Type: Solved Assignment (Soft copy/PDF) Course: MCOM: Language : ENGLISH: Semester: 2023 Course: MCOM: Session: For July 2024 and January 2024 admission cycle: Short Name : MCO-03: Assignment Code: MCO - 03 /TMA/2024: Product: Assignment of MCOM 2024 (IGNOU) Submission Date: July ...
Microsoft Fabric May 2024 Update
Welcome to the May 2024 update. Here are a few, select highlights of the many we have for Fabric. You can now ask Copilot questions about data in your model, Model Explorer and authoring calculation groups in Power BI desktop is now generally available, and Real-Time Intelligence provides a complete end-to-end solution for ingesting, processing, analyzing, visualizing, monitoring, and acting ...
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MCO-03: Research Methodology and Statistical Analysis

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Explain the significance of diagrammatic representation of data

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