Power BI is a powerful business intelligence tool developed by Microsoft. It allows you to transform raw data into interactive and visually appealing reports and dashboards. As a crash course, we'll cover essential topics you can learn day-wise with examples. Keep in mind that this is an introductory crash course, and Power BI has many advanced features to explore beyond this guide.

Power BI Architecture: An In-Depth Guide with Diagram Introduction: Power BI has gained widespread recognition as one of the leading business intelligence tools used by organizations to generate reports and dashboards, facilitating data-driven decision-making. To harness the full potential of Power BI's services and features, it is essential to understand its architecture. In this blog, we will provide a comprehensive tutorial on the Power BI Architecture, exploring its components and their interconnections. Microsoft Power BI Architecture: Power BI operates as a cohesive business platform, comprising various integrated technologies that together deliver exceptional business intelligence solutions. The Power BI Architecture can be delineated into four fundamental steps, each playing a crucial role in data transformation, report creation, and dashboard generation. Let us discuss these four steps Data Integration Data Transforming Report & Publish Creating and Dashboard 1. Data Integration: The process begins with the extraction of data from diverse sources, which may include various servers or databases. Data from different sources can often be in varying types and formats. Upon importing data into Power BI, the platform compresses datasets up to 1GB. If the compressed data exceeds this limit, a direct query is utilized. The integrated data is then standardized and stored in a staging area. For extensive datasets, Power BI offers two options: Azure Analytics Services or Power BI Premium. 2. Data Transforming: Integrated data, while available for analysis, is not yet ready for visualization. Before transforming data into insightful visuals, it requires cleaning and preprocessing. For instance, redundant or missing values are removed from datasets. Post-preprocessing, business rules are applied to further transform the data. The processed data is then loaded into the data warehouse for reporting and analysis. 3. Report & Publish: After sourcing and cleaning the data, you can create compelling reports. Reports visually represent data through slicers, graphs, and charts. Power BI offers an extensive library of custom visualizations to enhance report creation. Once reports are ready, they can be published to the Power BI services or an on-premises Power BI server, facilitating sharing and collaboration. 4. Creating Dashboards: With reports published to Power BI services, users can craft interactive dashboards by pinning individual elements. Dashboards retain filters when connected to live report pages, empowering users to interact with visuals using slicers and filters effectively. Components of Power BI Architecture: To fully grasp Power BI's functioning, let's delve into its core components and their interconnected roles within the architecture. #1. Data Sources Power BI efficiently supports numerous data sources, including online services, file types, and databases. From Excel and CSV files to SQL Server, SAP HANA, Google Analytics, and Salesforce, Power BI integrates seamlessly with various sources, ensuring data accessibility and visualization. Here is the list of Data Sources supported in Power BI. File Types: Power BI supports XML, txt/CSV, Excel, JSON, and Share point folder type files. Database: It supports SQL Server Analysis Services Database, SAP HANA Database, SQL Server Database, SAP Business Warehouse server, Access Database, Google BigQuery (Beta), Amazon Redshift, Snowflake, Impala, Oracle Database, IBM Informix database (Beta), Teradata Database, MySQL Database, IBM Netezza (Beta), Sybase Database, PostgreSQL Database. Azure: Azure SQL Data Warehouse, Azure Blob Storage, Azure Analysis Services database (Beta), Azure SQL Database, Azure Data Lake Store, Azure Table Storage, Azure HDInsight (HDFS), Azure Cosmos DB (Beta), Azure HDInsight Spark (Beta). Online Services: Power BI service, Dynamics 365 (online), Microsoft Exchange Online, Common Data Service (Beta), SharePoint Online List, Visual Studio Team Services (Beta), Dynamics 365 for Financials (Beta), Microsoft Azure Consumption Insights (Beta), Salesforce Objects, Salesforce Reports, Google Analytics, Dynamics 365 for Customer Insights (Beta), GitHub (Beta), appFigures (Beta), comScore Digital Analytix (Beta), Facebook, Kusto (Beta), Planview Enterprise (Beta), MailChimp (Beta), Mixpanel (Beta), QuickBooks Online, Projectplace (Beta). Other Services: Hadoop File (HDFS), Vertica (Beta), Web, OData Feed, SharePoint List, Microsoft Exchange, Active Directory, R Script, ODBC, Spark (Beta), Blank Query, OLE DB. #2. Power BI Desktop It is free software that enables you to connect, transform and visualize the data on your desktop. You can connect to various data sources with the help of Power BI Desktop and combine the data into a data model. This data model allows you to create a collection of images and graphics that make you share the information within the organization as records. The majority of the users who work on Business Intelligence projects use Power BI Desktop to create and share their reports with others. #3. Power BI Service Power BI Service is an On-Cloud service with a web-based platform and used to share and publish the reports made on Power BI Desktop. It collaborates the data with other users and creates dashboards. Power BI Service is also called “Power BI Workspace”, “Power BI Web Portal”, and “Power BI Site”. Power BI Service offers wonderful features like alerts and natural language Q&A. It is available in three versions. They are as follows: Premium version Pro version Free version #4. Power BI Report Server Power BI Report Server is similar to the Power BI Service. It is an On-Premises server platform. Using Power BI Report Server, organizations can secure their data. It enables the users to create reports and dashboards and allows you to share the reports with other users or organizations with proper security protocols. To use this service, you need to have a Power BI premium license. #5. Power BI Gateway Power BI Gateway is used to maintain fresh information by connecting to your on-site data sources without transferring the data. It provides secure data and allows you to transfer the data between Microsoft cloud services and on-premise services. Microsoft cloud services include PowerApps, Power BI, Azure Analysis Services, Microsoft Flow, and Azure logic apps. By using a gateway, organizations can maintain the databases and other data sources securely in cloud services. #6. Power BI Mobile Apps Using Power BI Mobile Apps, you can stay connected with on-premises data from anywhere. Power BI apps are available for iOS, Windows, and Android platforms. #7. Power BI Embedded Power BI Embedded is an On-premises service in Azure. It offers APIs for embedding the reports and dashboards into custom applications. Till now, we have been discussing major components of the Power BI, and now, we will talk about the remaining components of Power BI as well. Here is the list of the remaining Power BI Components. #8. Power BI Query Power Query is the data connectivity that enables the business users to access the data which is stored in multiple data sources and redesign it to satisfy their business requirements. Power Query offers custom connectors SDK so that third-party users can create their data connectors. #9. Power Maps Power BI Query is used to display how the values vary in proportion across the region. It also shows differences with the shadings that range from dark to light. It offers a 3D geospatial Data Visualization Tool. #10. Power Pivot Power Pivot is an element that stores the information in memory and allows highly compressed data storage and incredibly quick aggregation and calculation. It is also accessible as part of Excel and can be used within an Excel workbook to build a data model. Power Pivot can load information on its own, or Power Query can load information into it. It is highly comparable to the tabular model of SSAS (SQL Server Analysis Services), which is like a Power Pivot server-based variant. #11. Power View Power View offers interactive visualization that enables a drag-and-drop interface for users to create visualizations quickly and effectively in their Excel workbooks (using the Power Pivot data model). #12. Power Q&A Power Q & A is a feature of Power BI, and it enables you to explore your data in your own words. In other words, you can use natural language and ask a question to get an answer from your data. Power BI Architecture - Working We hope that you have understood the individual components of Power BI, and now, you will learn how these components work together. You will have a clear understanding of the Power BI Architecture with the help of the below image. In the above diagram, it is clear that the upper half part represents On-Cloud services, and the lower half part represents the On-Premise services. If you observe in the top of the image excel, web browsers and other sources are streaming into Power BI components, and they are called data sources. These data sources are authenticated users. Power BI has different data sources like On-Premise, Cloud databases, direct connections, etc. On-Premise: Power BI Desktop is accomplished with the authenticating, development and publishing tools. You can transfer the data from data sources to Power BI Desktop. And also, it allows users to create and publish reports on the Power BI Report Server or Power BI Service. Power BI Publisher allows you to publish the Excel workbooks to the Power BI Report Server. Report Publisher and SQL server Data tools help in creating the KPIs, datasets, paginated reports, mobile reports, etc. All kinds of reports are published at the Power BI Report Server, and from there, reports are distributed to the end-users. On-Cloud: Power BI Gateway is the essential component in the Power BI architecture. The Power BI Gateway acts as a bridge or secure channel to transfer the data from On-premise data to On-cloud data sources or apps. Cloud side architecture consists of a lot of components including Power suite having datasets, dashboards, reports, Power BI Premium, Power BI Embedded, etc. Users can embed the dashboards, reports into applications, SharePoint, Teams, etc. There are Cloud data sources and they are connected to the Power BI tools. Power BI Service Architecture In the previous section, you have learned how to publish the created reports in the Power BI Service. Power BI Service enables the users to create and access the reports, dashboards from the client platforms like mobile devices, websites, etc. User needs to interact with the Power BI Service whenever they want to access the data that is created on the Power BI. So, now, we will learn how the Power BI Service works. Power BI Service Architecture consists of two clusters. The following are the two clusters. · Front End Cluster · Back End Cluster Now, we will discuss the two clusters in detail. 1. Front End Cluster: Front end cluster acts as an intermediate between the back end cluster and the clients. It is also called a Web Front End Cluster. It establishes the initial connection and authenticates the users or clients using the Azure Active Directory. After user authentication, Azure Traffic Manager directs the user requests to the nearest data centers and Azure Content Delivery Network (CDN) allocates the statice files/content to the users or clients based on the geographical locations. 2. Back End Cluster: It manages the datasets, reports, storage, visualizations, data refreshing, data connections, and other services in the Power BI. At the back end cluster, the web client has only two direct points to interact with the data, i.e., Gateway Role and Azure API Management. These two components are responsible for authorizing, load balancing, routing, authentication, etc. Working Of Power BI Service Power BI stores the data in two leading repositories, i.e., Azure SQL Database and Azure Block Storage. Azure Block Storage enables the users to store the datasets, and all system-related data and metadata are stored in the Azure SQL database. It authenticates the user requests and sends them to the Gateway Role. It processes the requests and assigns them to the appropriate components like Background Job Processing Role, Data Movement Role, Presentation Role, and Data Role. The presentation role manages all the associated visualization queries like reports and dashboards. Presentation Role sends requests to the Gateway Role to the Data Movement Role or Data Role for all relevant datasets. Azure Service Bus is used to connect and fetch the data from the On-Premises data sources with the cloud. It sends a request to execute the queries On-Premises data source and retrieve the data from its cloud service. The Azure Service Fabric allows all components and microservices which are related to the Power BI Service. Azure Cache helps in reporting the data that is stored in the in-memory of the Power BI system. Conclusion: In this course, we gave detailed information about the Power BI Architecture, its working, and its components. And also, we have explained the Power BI service and its works. If you still have queries regarding Power BI Architecture? Got any questions? Leave a comment below and we will get back to you

Power BI is a powerful tool for visualizing and analyzing data. Let's break down its main components using relatable examples: Data Source: Think of this as the place where your data lives. It could be an Excel spreadsheet, a database, an online service like Excel Online or SharePoint, or even a web API. Imagine you have a spreadsheet with sales data, where each row represents a sale with information like date, product, and amount. Data Transformation: This is like preparing your ingredients before cooking. In Power BI, you can clean, filter, and shape your data using the Power Query Editor. For instance, you might want to remove duplicate rows, filter out old sales, or combine data from multiple sheets into one. Data Model: This is where you organize your data into tables and establish relationships between them. Picture this like arranging different types of LEGO bricks. In our sales example, you'd have tables for sales, products, and maybe customers, with relationships connecting them. Measures and Calculated Columns: Think of measures as calculations you perform on your data. For example, you can create a measure that calculates the total sales amount. Calculated columns are like custom fields you add to your data. For instance, you might create a calculated column that calculates the profit for each sale. Visualizations: Imagine turning your data into colorful and meaningful charts and graphs. For the sales data, you could create a bar chart to show which products sold the most and a line chart to display sales trends over time. Dashboards: A dashboard is like a summary page that brings together different visualizations. It's like arranging your favorite photos on a pinboard. You can put your sales charts, product insights, and other visuals on a dashboard to see the big picture at a glance. Reports: Reports are like interactive documents where you can explore your data in-depth. You can think of them as pages in a book. In a report about sales data, you might have different pages showcasing various aspects like regional sales comparisons, top customers, and monthly trends. Sharing and Collaboration: Just like sharing a YouTube video link, you can share your Power BI reports and dashboards with others. This is great for teamwork and presentations. For instance, you could share your sales report with your team to discuss strategies. Power BI Service: This is like the cloud version of Power BI. It allows you to publish and share your reports online. You can view your reports in a web browser or the Power BI mobile app. It's as if your data can be accessed from anywhere. Power BI Desktop: This is the tool you use to create reports and dashboards. Imagine it as your art studio where you craft your data visualizations before showcasing them to others. Here's how Power Pivot works: Gathering Data: Imagine you have information about people and their ages in one table, and you have another table with their favorite colors. Power Pivot helps you bring these two tables together. Creating Relationships: Power Pivot lets you say, "This person's age is connected to this person's favorite color." It's like building a bridge between two parts of your castle. Making Calculations: Let's say you want to find out the average age of people who like blue. Power Pivot can do this easily because it understands the connections between data. It's like having a magic wand that quickly gives you answers. Faster Analysis: When you have lots of data, regular Excel might slow down. But with Power Pivot, it's like having a super-fast flying carpet. It handles big amounts of information without getting tired.

Overview of Power BI: Understand what Power BI is and its key features. Data Sources: Learn how to connect to various data sources like Excel, SQL Server, and CSV files. Creating Your First Report: Build a simple report using a single data source.

Introduction to Visualizations: Visualizations are graphical representations of your data that help you understand patterns, trends, and relationships more easily. Power BI offers a wide range of visualizations, such as bar charts, pie charts, line charts, maps, and more. Each type of visualization serves a specific purpose, and choosing the right one depends on the nature of your data and the insights you want to convey. Creating Basic Visuals: Let's begin by building some basic visualizations using a sample dataset. For this example, we'll use a CSV file containing sales data. Open Power BI Desktop and click "Get Data" to connect to your CSV file. After loading the data, go to the "Visualizations" pane on the right-hand side. Drag and drop the "Bar chart" visualization onto the canvas. Choose the relevant fields for the X-axis (e.g., product categories) and the Y-axis (e.g., total sales). Power BI will automatically generate a bar chart representing the total sales for each product category. Formatting Visuals: Formatting plays a crucial role in making your visuals more appealing and easier to understand. Here are some formatting options you can explore: Select the visualization and go to the "Format" pane on the right-hand side. Adjust colors, fonts, and background to match your report's theme. Add data labels to display exact values on the visuals. Experiment with different chart styles, such as stacked or clustered bars. Building Reports: Now that we have our first visualization, let's create a report that combines multiple visuals into a cohesive view. Click on the blank canvas to deselect any visuals. Drag and drop additional visualizations onto the canvas, such as a line chart showing sales trends over time and a pie chart displaying sales by region. Arrange the visuals to create a well-organized layout for your report. Use the "Page view" option to add more pages to your report for different insights. Interactive Elements: Power BI allows users to interact with visuals, making reports more engaging. We can add slicers and filters to enable users to explore data on their terms. Go to the "Visualizations" pane and select the "Slicer" icon. Choose the relevant field (e.g., date or region) to use as a slicer. When you select a specific date or region on the slicer, all visuals on the report page will adjust accordingly. Best Practices: Remember these best practices when creating visualizations and reports in Power BI: Keep it Simple: Avoid cluttering your report with too many visuals or unnecessary details. Use Colors Wisely: Use colors that enhance readability and convey meaning, such as green for positive values and red for negative ones. Consistent Design: Maintain a consistent design across all pages of your report for a professional look. Storytelling: Arrange visuals in a logical order to tell a compelling data-driven story. Congratulations on completing Day 3! You've learned the basics of visualizations and report-building in Power BI. Tomorrow, we'll explore more advanced topics, including dashboards and interactivity. Keep practicing and experimenting to become a Power BI pro!

Introduction to DAX: DAX is a formula language that works with Power BI's data model. It resembles Excel formulas but operates differently as it works with tables, columns, and relationships. DAX is designed for data analysis and is essential for performing calculations that go beyond simple aggregations. Syntax and Functions: DAX uses functions to perform calculations. A DAX formula typically follows this structure: FunctionName(Arguments). Functions can be as simple as adding two numbers or as complex as creating time-intelligence calculations. Calculated Columns: Calculated columns are new columns you add to a table using DAX. These columns are computed row by row and become part of the underlying data model. You can use DAX to perform computations based on existing columns and values. Example: Suppose you have a sales table with "Unit Price" and "Quantity" columns. You can create a calculated column "Total Sales" using the following DAX formula: cssCopy code Total Sales = Sales[Unit Price] * Sales[Quantity] Measures: Measures are used for aggregations and calculations based on the data displayed in your visuals. Unlike calculated columns, measures do not add new columns to the table; instead, they provide dynamic calculations based on context. Example: Let's say you want to calculate the total sales for a given period. You can create a measure called "Total Sales" using the following DAX formula: mathematicaCopy code Total Sales = SUM(Sales[Total Sales]) Filter Context and Row Context: DAX calculations are affected by two types of context: filter context and row context. Filter context is created when you use filters to limit the data displayed in your visuals. Row context is created when DAX operates on a specific row in a table. Understanding these contexts is crucial for creating accurate calculations. Time Intelligence Functions: DAX provides powerful time intelligence functions to analyze data based on time periods. These functions allow you to perform year-to-date calculations, rolling averages, and more. Example: To calculate year-to-date sales, you can use the DAX function: mathematicaCopy code YTD Sales = TOTALYTD(SUM(Sales[Total Sales]), 'Date'[Date]) Advanced DAX Concepts: DAX also includes advanced concepts like iterating functions (SUMX, AVERAGEX), filtering functions (FILTER, ALL), and handling blanks (BLANK, IFERROR) to perform sophisticated data analysis. Practice Exercise: Create a calculated column to categorize sales into "Low," "Medium," and "High" based on the "Total Sales" value. Then, create a measure to calculate the average "Total Sales" for each category. Congratulations! You've completed Day 6 of our Power BI crash course on DAX. DAX is a powerful tool for performing data analysis, and as you practice more, you'll become proficient in creating complex calculations and measures to gain valuable insights from your data. Keep exploring and experimenting with DAX to take your Power BI skills to the next level!

Here's a list of DAX (Data Analysis Expressions) queries categorized from beginner to advanced level How to face a Power BI developer job interview successfully.

Welcome to Day 4 of our Power BI crash course! Today, we'll delve into creating interactive dashboards in Power BI. Dashboards are a collection of visuals and reports that provide a consolidated view of your data, allowing users to gain valuable insights and make data-driven decisions. Let's get started!

Welcome to Day 6 of our Power BI crash course! Today, we'll dive into Data Analysis Expressions (DAX), an essential language used in Power BI for creating calculated columns, measures, and more. DAX allows you to perform powerful calculations and data analysis on your data model. Let's get started!

Welcome to Day 7 of our Power BI crash course! Today, we'll focus on the Power BI Service and sharing your Power BI reports and dashboards with others. The Power BI Service is a cloud-based platform where you can publish, share, and collaborate on your Power BI content.

Day 1 - Introduction to Power BI • Power BI is a powerful visualization and reporting tool developed by Microsoft. • It provides business intelligence capabilities to create interactive reports and dashboards. • Key advantages of Power BI include its cost-effectiveness and user-friendly interface. • Power BI consists of several components, including Power Query, Power View, Power Pivot, and Power BI Service. Day 2 - Power BI Connection Methods Power BI offers three main connection methods: 1. Import: Data is imported offline from sources like Excel, text files, or CSV. It is suitable for smaller datasets. 2. Direct: Data is connected directly to the source database (e.g., SQL Server, PostgreSQL, Oracle). Suitable for larger datasets. 3. Live: Data is connected to online sources like Azure or SSAS (SQL Server Analysis Services). Allows real-time analysis but may have performance limitations. Day 3 - Power Query: Append and Merge • Power Query is an ETL (Extract, Transform, Load) tool in Power BI used for data transformations. • Append operation combines rows from multiple tables (or sheets). • Merge operation combines columns from multiple tables based on a common key. Day 4 - Power Query: Conditional Column and Column from Examples • Conditional Column: Allows creating new columns based on specific conditions (IF-ELSE logic). • Column from Examples: Automatically generates data in a new column based on given examples. Day 5 - Power Query: Manage Parameters and Advanced Editor (M Language) • Manage Parameters: Allows users to create dynamic filters at the source level. • Advanced Editor: Provides access to M Language code for advanced data transformations. Day 6 - Power View: Introduction to Data Visualization • Power View is a reporting tool in Power BI used to create interactive visualizations. • It supports various chart types like bar charts, line charts, pie charts, maps, etc. • Power View allows users to explore and analyze data through interactive features. Day 7 - Power Pivot: Data Modeling • Power Pivot is a data modeling tool in Power BI used to create relationships between tables. • Fact tables contain transactional data, and dimension tables contain descriptive data. • Data modeling ensures accurate data representation for meaningful reporting. Day 8 - Data Modeling: Relationships • Relationships define how tables are related to each other in a Power BI data model. • Cardinality (One-to-One, One-to-Many, Many-to-One, Many-to-Many) describes the number of related records between tables. • Cross filter direction (Single, Both) determines how tables filter each other in visuals. Day 9 - Data Modeling: Active and Inactive Relationships • Power BI allows having multiple relationships between tables, but only one can be active at a time. • Active relationships are used for calculations and filtering in visuals and reports. • Inactive relationships are used for specific scenarios where different relationships are needed. 1. Create Relationship: Manually create a relationship between two tables in Power BI. 2. Auto-Detect Relationship: Power BI can automatically detect relationships based on column names and data types. 3. Edit Relationship: Modify the relationship between tables by changing the columns used or cardinality. 4. Delete Relationship: Remove a relationship between tables. Cardinalities: 1. One to Many: This relationship connects a single unique value in one table with multiple matching values in another table. Symbol: (1 -> *). 2. Many to One: This relationship connects multiple values in one table to a single matching value in another table. Symbol: (* -> 1). 3. One to One: A unique value in one table is related to a single matching value in another table. Symbol: (1 -> 1). 4. Many to Many: Multiple values in one table are related to multiple values in another table. Symbol: (* -> *). Cross Filter: Determine how filters on one side of the relationship affect the other side. • Single: Filters are propagated in a single direction, from the active to the inactive table. • Both (Bi-Directional): Filters are propagated in both directions, active to inactive and vice versa. Note: For One to One relationships, they are always Bi-Directional, and we cannot change them to Single Direction. Inactive and Active Relationships: When you have multiple relationships between two tables, you can choose which one to make active. By default, all relationships are active. Day 10 - Multiple Relationships Example Example: Consider three tables - Sales, Budget, and Date. 1. Create an active relationship between Sales[DateID] and Date[DateID], representing the actual sales date. 2. Create another inactive relationship between Budget[DateID] and Date[DateID], representing the budgeted date. By making the relationship inactive, we can prevent unintended filtering effects. For example, if we filter the Date table based on a specific date, the inactive relationship ensures that only the Budget table, not the Sales table, is affected. Day 11 - Time Intelligence Functions Time intelligence functions in Power BI enable users to perform calculations relative to dates. 1. TOTALYTD: Calculates the total value year-to-date based on a specific measure. 2. TOTALQTD: Calculates the total value quarter-to-date based on a specific measure. 3. TOTALMTD: Calculates the total value month-to-date based on a specific measure. 4. SAMEPERIODLASTYEAR: Returns the equivalent period in the previous year based on a specific date. These functions are useful for creating various time-based reports, such as year-to-date sales, quarter-to-date expenses, etc. Day 12 - Calculated Tables Calculated tables in Power BI are derived tables created using DAX expressions. Unlike measures, calculated tables store data and are computed when data is loaded. Use Cases: 1. Time Tables: Creating a date table with columns for year, month, quarter, etc. 2. Currency Conversion: Calculating a new table with values converted to a different currency. 3. Product Categories: Grouping products into categories based on specific criteria. Calculated tables help optimize performance and simplify report creation by pre-calculating data in the model. Day 13 - Hierarchies and Drill-Downs Hierarchies in Power BI allow users to organize data into levels of granularity, enabling drill-down analysis. Example: A Date hierarchy with levels - Year, Quarter, Month, and Day. Drill-Downs allow users to expand and collapse hierarchy levels, focusing on specific details or summarizing data. Day 14 - Advanced Visualizations Power BI offers a wide range of visualizations beyond basic charts. 1. Treemaps: Hierarchical view of data using nested rectangles. 2. KPIs (Key Performance Indicators): Visual representation of business metrics with target values and status indicators. 3. Gauges: Representing a single value within a predefined range. 4. Cards: Displaying a single value as a prominent figure. 5. Matrix: Table with row and column grouping and subtotals. These advanced visualizations add versatility and enhance data storytelling. Day 15 - Dashboard Creation and Publishing Creating a dashboard involves selecting relevant visualizations, arranging them logically, and adding filters. Publishing to Power BI service allows sharing and collaboration with others. With Power BI's interactive dashboards, users can slice and dice data, gaining deeper insights into their business. This completes the Power BI training. You are now equipped to explore and utilize the full potential of Power BI for data visualization and reporting. If you wish to pursue certification, consider the PL-300 certification for Power BI. provide the explanation clearly on this notes