Answer:

Power BI, a business analytics tool developed by Microsoft, consists of several building blocks that work together to enable data analysis, visualization, and reporting. The main building blocks of Power BI are:

• Power Pivot
• Power BI Service
• Power BI Gateway
• Power BI Desktop
• Power BI Mobile
• Power BI Report Server

Answer:

To connect JIRA to Power BI, you can use JIRA’s REST API to retrieve data and then import that data into Power BI. Here’s a step-by-step guide on how to do it:

• Generate JIRA API Token
• Open Power BI
• Get Data in the Home Tab
• Select Web Connector
• Enter JIRA REST API URL
• Set Authentication
• Select Data
• Transform and Analyze Data
• Visualize Data
• Refresh Data

Answer:

In Power BI, there are several data refresh options available, such as:

• Visual Container Refresh
• Model or Data Refresh
• Package Refresh
• Tile Refresh

Answer:

Power BI offers a wide range of visualizations that you can use to present and analyse your data. Here are some commonly used visualizations in Power BI:

• Column Chart
• Bar Chart
• Line Chart
• Area Chart
• Pie Chart
• Donut Chart
• Scatter Chart
• Map
• Table
• Matrix
• Card
• KPI
• Gauge
• Funnel

Answer:

To use a custom visual file in Power BI, you can follow these steps:

1. Open Power BI Desktop
2. Get the custom visual file that you want to use
3. Import the custom visual
4. Browse and select the custom visual file
5. Import & the custom visual

Once you’ve added and configured the custom visual, save your Power BI report. If you’re connected to a Power BI service or workspace, you can publish the report to make it accessible to others.

Answer:

DAX (Data Analysis Expressions) is a formula language used in Power BI, Power Pivot, and Analysis Services to perform calculations and create custom measures and calculations. Here are some commonly used DAX functions:

• SUM
• COUNT
• AVERAGE
• MAX
• MIN
• DISTINCTCOUNT
• CALCULATE
• FILTER
• ALL
• RELATED
• RANKX
• DATEADD

Answer:

DAX (Data Analysis Expressions) is a formula language used in Power BI, Power Pivot, and Analysis Services to perform calculations, create custom measures, and define relationships between data. Here are some perks of using DAX:

1. Flexibility: DAX provides a flexible and powerful syntax for defining calculations and aggregations in your data models.
2. Integration with Power BI: By learning DAX, you can leverage its capabilities to build interactive visualizations, perform complex calculations, and create insightful reports and dashboards.
3. Calculation capabilities: DAX offers a rich set of functions and operators to perform calculations on your data. It supports mathematical operations, statistical functions, conditional logic, text manipulation, time intelligence, etc.
4. In-memory processing: DAX operates in-memory, which means it leverages the capabilities of Power Pivot and Analysis Services to store and process data in RAM. This allows for fast and efficient calculations, even on large datasets.
5. Seamless modeling: DAX seamlessly integrates with the data modeling capabilities of Power BI and Analysis Services. It allows you to create relationships between tables, define hierarchies, and implement complex business logic within your data models.

Answer:

Power BI provides various data shaping techniques to transform and manipulate data. Some popular data shaping techniques in Power BI include:

• Grouping rows.
• Pivoting columns.
• Creating custom columns
• Implementing a sort order
• Adding an index column to a table
• Removing columns and rows from a table

Answer:

The FILTER function in Power BI is a powerful function used to filter data within a formula. It allows you to specify a table or a column as an input, and then define one or more conditions to filter the data based on specific criteria.

Answer:

In Power BI, the Query Editor is a powerful tool for transforming and shaping data before loading it into your data model. Here are some commonly used tasks in the Query Editor:

• Pivot columns
• Filtering rows
• Shaping and combining data
• Adding custom columns
• Managing Query Dependencies

Answer:

Data Management Gateway and Power BI Personal Gateway are both tools used in data integration and connectivity within the Power BI ecosystem. However, they serve different purposes and have distinct functionalities. Here’s a breakdown of their differences based on:

• Purpose: The Data Management Gateway is primarily used for data integration and on-premises data access in Power BI. It enables secure communication between cloud-based Power BI services and on-premises data sources. On the other hand, the Power BI Personal Gateway is intended for individual users or small-scale data integration scenarios. It enables users to refresh and update data from on-premises data sources in their Power BI reports and dashboards.
• Connectivity: It supports a wide range of data sources, including SQL Server, Oracle, SharePoint, and more. Whereas, Power BI personal gateway supports a limited set of data sources, commonly used by individual users, such as Excel files, Access databases, SQL Server databases, and SharePoint files.
• Scalability: The Data Management Gateway is designed to support enterprise-level data integration scenarios. It allows for centralized management of gateways across an organization, enabling multiple users and Power BI workspaces to access on-premises data sources. The Personal Gateway is designed for personal or small-scale usage, typically used by individuals or small teams. It does not provide centralized management capabilities.
• Configuration: The Data Management Gateway requires installation and configuration on a server or a dedicated machine within the organization’s network. The gateway machine must have a constant internet connection to communicate with Power BI services in the cloud while the personal gateway requires installation and configuration on the user’s local machine. The gateway machine must be powered on and connected to the internet for data refreshes to occur.

Answer:

MSBI (Microsoft Business Intelligence) and Power BI are both powerful business intelligence tools offered by Microsoft, but there are some key differences between them, such as:

1. Product Focus:
   • MSBI is a comprehensive suite of tools that includes several components such as SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), and SQL Server Reporting Services (SSRS). Each component focuses on a specific area of business intelligence.
   • Power BI is a cloud-based business analytics service that provides interactive visualizations and self-service BI capabilities. It primarily focuses on data visualization and reporting.
2. Deployment:
   • MSBI is primarily an on-premises solution. It is typically installed and deployed within an organization’s own infrastructure.
   • Power BI is a cloud-based service, although it also offers an on-premises option called Power BI Report Server. With the cloud-based version, data is stored and processed in the Microsoft Azure cloud.
3. Data Sources:
   • MSBI supports a wide range of data sources, including relational databases, data warehouses, and multidimensional databases.
   • Power BI also supports a variety of data sources, including databases, cloud services, files, and online services.
4. Self-Service Capabilities:
   • MSBI focuses more on IT-driven development and centralized control of data and reports. It requires technical expertise to design and develop solutions.
   • Power BI emphasizes self-service business intelligence, allowing users with limited technical skills to create their own reports, dashboards, and visualizations using an intuitive drag-and-drop interface.
5. Pricing and Licensing:
   • MSBI requires separate licenses for each component (SSIS, SSAS, SSRS) and is typically sold as part of the SQL Server licensing model.
   • Power BI offers several licensing options, including a free version with limited features, a pro version for individual users, and a premium version for organizations with advanced requirements.

Answer:

Here are some common formats in Power BI:

1. Visualizations: Power BI provides various visualizations to represent data, such as bar charts, column charts, line charts, pie charts, etc.
2. Filters: Power BI supports single-select and multi-select filters, as well as advanced filtering options like relative date filtering.
3. Conditional formatting: Conditional formatting allows you to highlight or format data based on specific conditions. It helps you visually emphasize certain values or patterns in your data.
4. Data types: Power BI recognizes different data types such as text, numbers, dates, times, and geographical data. Each data type has specific formatting options and behaviors when used in visualizations.
5. Color formatting: Power BI allows you to customize the color schemes used in visualizations, including background colors, font colors, and color palettes for categorical or continuous data.
6. Data labels: Data labels provide additional information about the values displayed in visualizations. You can format data labels to show values, percentages, or custom text, and adjust their position, font, size, and color.

Answer:

To connect SQL Server to Power BI, follow these steps:

1. Start by opening Power BI Desktop to create and design reports.
2. Click on “Get Data” located in the toolbar.
3. Select SQL Server from the available data sources.
4. After selecting SQL Server, click on the “Connect” button to proceed.
5. In the SQL Server database window, enter the server details to connect to your SQL Server.
6. Once connected to the server, select the database that contains the data you want to connect to Power BI.
7. After selecting the database, choose the tables or views you want to import into Power BI. You can select multiple tables if needed.
8. If you want to perform any data transformations or cleanups, you can use the Power Query Editor. You can access it by clicking on the “Transform Data” button. It allows you to filter, reshape, merge, or perform other transformations on your data before loading it into Power BI.
9. After selecting the tables and performing any necessary transformations, click on the “Load” button to import the data into Power BI. Alternatively, you can choose to “Load to” or “Edit” to perform additional transformations.
10. Once the data is loaded into Power BI, you can start creating visualizations and reports based on your SQL Server data.
11. Schedule automatic data refreshes from your SQL Server database. Provide the necessary credentials and configure the refresh frequency according to your needs.

Answer:

In Power BI, an OData feed refers to a data source that utilizes the OData (Open Data Protocol) standard for accessing and consuming data. OData is a protocol that allows for the creation and consumption of RESTful APIs, making it easier to access and manipulate data from various sources.

Answer:

A slicer is a visual element that allows users to filter data within a report or dashboard. It provides an interactive way to slice and dice the data, narrowing down the view to specific subsets of information.

Answer:

In Power BI, the term “GetData” refers to a process of retrieving and importing data from various sources into Power BI for analysis and visualization. The GetData feature allows users to connect to multiple data sources, such as databases, files, online services, and more, and extract the required data to create reports and dashboards.

VIF = Variance of model / Variance of the model with a single independent variable.

Answer:

Power BI supports a wide range of database sources. Here are some of the supported database sources for Power BI:

• Spark
• MariaDB
• Snowflake
• SQL Server
• BI Connector
• Azure Databriks
• Amazon Redshift
• Azure Data Explorer
• Azure Analysis Services
• Sap Business Warehouse Server

Answer:

SSAS (SQL Server Analysis Services) and Power BI are both powerful tools for data analysis and reporting, but they have different focuses and use cases. Here are the key differences between SSAS and Power BI:

1. Purpose and Focus:
   • SSAS (SQL Server Analysis Services) is a multidimensional and tabular data modeling tool provided by Microsoft as part of the SQL Server platform. It is designed for creating and managing enterprise-level analytical solutions. SSAS allows you to build complex data models and perform advanced analytics, including data mining, predictive analytics, and complex calculations.
   • Power BI is a BI and data visualization tool provided by Microsoft. Power BI is designed for creating interactive reports, dashboards, and visualizations from various data sources. Power BI emphasizes self-service analytics, enabling users to connect to data, transform it, and create compelling visualizations without extensive technical knowledge. It also offers collaboration and sharing capabilities.
2. Data Sources and Connectivity:
   • SSAS can connect to a wide range of data sources, including relational databases, multidimensional databases, Excel files, and other external data sources. It supports Extract, Transform, Load (ETL) processes for data integration and provides robust data modeling capabilities.
   • Power BI can connect to various data sources, including databases, Excel files, cloud-based services, and online services. It offers a wide range of built-in connectors and supports data transformation operations within its Power Query Editor.
3. Scalability and Deployment:
   • SSAS is typically deployed on-premises or in a private cloud environment. It supports large-scale deployments and can handle vast amounts of data and complex calculations. SSAS offers multidimensional and tabular modes, giving you flexibility in choosing the appropriate modeling approach.
   • Power BI can be used both in the cloud (Power BI service) and on-premises (Power BI Report Server). Power BI service is a cloud-based platform that provides centralized sharing, collaboration, and data refresh capabilities. It is suitable for small to medium-sized deployments. Power BI Report Server allows you to deploy reports on your own infrastructure, providing more control and security.
4. Advanced Analytics and Development:
   • SSAS offers advanced analytics capabilities, including data mining, predictive modeling, and statistical analysis. It provides support for MDX and DAX languages for querying and calculations. SSAS also allows developers to build custom solutions using Visual Studio and provides a rich development environment.
   • While Power BI offers some advanced analytics features, such as basic calculations and aggregations, its primary focus is on data visualization and interactive reporting. Power BI supports the DAX language for creating calculated columns and measures. It also allows for the integration of R and Python scripts to perform more advanced analytics.

Answer:

There are three ways to implement many-to-many relationships in Power BI, such as:

• Relate two fact-type tables
• Relate higher grain fact-type table
• Relate two dimension-type tables