How to Migrate Tableau Dashboards to Power BI Using Claude AI
Learn Tableau to Power BI Migration using Claude AI. Convert Tableau dashboards, LOD expressions, and data models into Power BI with this step-by-step guide. Why Migrate with AI Support? Migrating dashboards from Tableau to Power BI can be challenging, especially when translating complex calculations such as Level of Detail (LOD) expressions into DAX. This Tableau to Power BI Migration guide demonstrates how Claude AI simplifies the entire process by analyzing Tableau workbook files, generating migration documentation, and helping you recreate dashboards accurately in Power BI. Step-by-Step Migration Guide Step 1: Initialize Your Claude AI Project and Upload the Workbook The first order of business is setting up your migration workspace inside Claude AI. Instead of using a standard chat window, creating a dedicated “Project” lets you attach reference files that Claude will persistently use as context. 1. Log into your Claude AI account and select Projects from the dashboard. 2. Click Create Project and name it something descriptive, such as Tableau to Power BI Migration. 3. Click on the file upload button and select your Tableau workbook file. Note: Always upload the .twb (Tableau Workbook) file rather than a .twbx (packaged workbook). The .twb file is pure XML code, which contains the complete architectural map of your data sources, relationships, and metadata calculations that Claude needs to read. Step 2: Generate the Migration Documentation Script Once your file is uploaded, you need to prompt Claude to parse the XML layout and extract an execution plan. Enter the following prompt into your project chat: “Please prepare formal documentation that provides step-by-step guidance for converting this report into Power BI.” Claude will read the .twb file structure and build a detailed documentation artifact. This generated guide outlines: Tableau Properties Summary: A list of the parsed tables, data source paths (e.g., Excel or SQL servers), and visualization elements. Custom Calculation Audits: Automatic extraction of complex calculated fields (like your customer cohort LOD metrics). Data Model Architecture: The current join structures that must be restructured into a classic Power BI star schema. side-by-side view of the Claude AI project interface detailing the migration steps alongside the generated “Tableau to Power BI Conversion Guide” document artifact. Step 3: Establish the Data Model in Power BI According to the documentation generated by Claude, our example migration utilizes three core tables from the classic Sample Superstore dataset: Orders, People, and Returns. We need to pull these into Power BI and create an optimized data model. Open a blank report in Power BI Desktop. Go to the Home tab, click Excel workbook, select your data file, and check the boxes for Orders, People, and Returns. Click Transform Data to open the Power Query Editor. This is where you should verify column data types (e.g., ensuring date fields are explicitly recognized as Dates). Click Close & Apply to load the clean datasets into your model view. Next, navigate to the Model View tab on the left-hand sidebar to define your relationships. Drag and drop the corresponding keys to match your star schema design: Structuring a clean star schema data model in Power BI Desktop Link Orders to Regional Manager using the common Region column. Link Orders to Returns using the `Order ID` column. Step 4: Convert Tableau LOD Expressions to Power BI DAX This is where the transition gets interesting. In the original Tableau dashboard, a cohort retention metric relies on a Level of Detail expression to isolate the exact date a customer made their very first purchase: { FIXED [Customer ID] : MIN([Order Date]) } Power BI does not use a FIXED keyword. Instead, Claude translates this calculation into an explicit DAX calculated column using the CALCULATE and ALLEXCEPT modifiers. Go to the Data View or Report View in Power BI. Select the Orders table from your fields pane, right-click, and choose New Column. Paste the DAX calculation script provided by Claude: First Purchase Date = CALCULATE( MIN(Orders[Order Date]), ALLEXCEPT(Orders, Orders[Customer ID]) ) This expression instructs Power BI to evaluate the minimum order date while clearing all filters on the Orders table except for the Customer ID. This mirrors the Tableau behavior perfectly. Step 5: Build the Customer Retention Visualization With our data model ready and our DAX column calculated, we can now configure the final dashboard presentation. We will create a stacked column visual to review how historical customer cohorts continue to purchase goods across subsequent operational years. In the **Visualizations** pane, select the Stacked Column Chart icon. Drag the standard Order Date from your fields pane over to the X-axis well. (Right-click it and ensure it is displaying the date hierarchy so you can isolate just the Year value). Create a clean explicit measure for your metric tracking (e.g., Total Sales = SUM(Orders[Sales])) and drag it to the Y-axis well. Finally, grab your newly calculated First Purchase Date column and slide it directly into the Legend well. Make sure this field is filtered to display the Year as well. If the output initially looks like a grouped side-by-side bar layout, double-check that your visualization type is specifically switched to a Stacked Column Chart to properly layer your consumer retention categories. Replicating the original customer retention cohort visual cleanly in Power BI The resulting visualization dynamically mirrors the classic Tableau cohort tracking layout, accurately validating key historical totals down to individual matching data points (such as an identical baseline sales volume match at 1,496,640!). Key Takeaways & Migration Summary TWB is the Key: Always feed Claude pure XML .twb files rather than packaged .twbx files to allow successful extraction of dashboard logic and calculation metadata. LOD to DAX Equivalence: Tableau’s FIXED level of detail statements are effectively replaced in Power BI by combining CALCULATE() with scope-limiting modifiers like ALLEXCEPT(). AI as an Assistant, Not a Replacement: While Claude AI speeds up data model mappings and drafting DAX columns, you still need clean functional knowledge of both platforms to catch visual formatting changes and resolve edge cases. To understand more about PowerBI using Claude go through our
Power BI with Claude AI: Auto-Generate DAX, Visuals & Semantic Docs
A Complete Step-by-Step Free Alternative to Power BI Copilot Are you a Power BI developer or data analyst looking to leverage cutting-edge artificial intelligence to speed up your development workflow, but you don’t have a premium Microsoft Fabric or Power BI Copilot license? In this step-by-step tutorial, we will explore an incredibly disruptive integration between Power BI Desktop and Anthropic’s Claude AI. By exploiting Power BI’s text-based project formats, you can transform Claude into an expert co-developer that works directly on your local project files completely for free. Introduction If you have ever felt left out because your organization hasn’t provisioned a Power BI Copilot license, this workaround changes the entire development game. By pairing Power BI’s newer project saving capabilities with the custom workspace context of Claude Projects, you can automate your most tedious tasks seamlessly. Whether you need to generate exhaustive semantic model documentation for a migration project, inject fresh DAX calculations natively into your tables, or spin up fully configured visualizations out of thin air, this local file methodology handles it all without costing a dime in enterprise licensing fees. Why PBIP Files are the Secret Sauce Before diving into the setup, we must understand the underlying file technology that makes this magic trick possible. Traditionally, Power BI saves workbooks in a binary .pbix format. Because a .pbix file is a closed, compiled package, large language models (LLMs) cannot natively read its internal logic, trace its relationships, or modify its structure. To bridge this gap, we use the Power BI Project (.pbip) file format. When you save a workbook as a .pbip file, Power BI splits your single monolithic file into a human-readable folder structure. This launcher architecture separates your project into two distinct directories: The Semantic Model Folder: This contains your data model structure, data table definitions, relationships, columns, metadata, and existing DAX measures stored in standard text files using Tabular Model Definition Language (TMDL). The Report Folder: This houses your actual visual canvas layouts, page configurations, custom visual setups, and theme properties in structured JSON files. Because these folders contain raw, human-readable text configurations, Claude can easily scan them, interpret their logic, write to them, and modify your actual Power BI file architecture cleanly. Step-by-Step Guide: Integrating Power BI with Claude AI Follow these precise steps to connect your local files to Claude and start automating your data modeling workflow. Step 1: Export Your Project as a PBIP File First, open your target workbook inside Power BI Desktop. We need to save the file into the developer-friendly project format. 1.Navigate to the top left corner of the ribbon and click on File. 2. Select Save as from the dropdown options. 3. Choose your destination directory. 4. In the Save as type dropdown menu, select Power BI project files (*.pbip). 5. Click Save. Once saved, navigate to that directory using your file explorer. You will see a launcher file sitting in parallel with a dataset directory and a report directory. Step 2: Zip and Upload Your Files to Claude Claude needs to receive these configuration folders cleanly. The most efficient way to upload your project directory is by compressing the structural data folders into a single archive. Highlight both the Semantic Model folder and the Report folder inside your file explorer. Right-click the highlighted folders, select Compress to ZIP file, and name it (e.g., PowerBI_Project_Files.zip). Open your Claude Desktop client (or navigate to the web version). Select the Projects feature on the sidebar and click Create New Project. Name your project (e.g., Retail AI Analysis). Click Add Files and upload your newly created .zip folder into the project context window.Automating 3 Core Power BI Workflows with Claude Uploading the compressed Power BI folder structure into a Claude Project space. 1. Auto-Generating Complete Semantic Model Documentation Data analysts are frequently tasked with creating clean documentation for enterprise models. Manually mapping out relationships, tables, data types, and measures can take hours. With Claude hooked up to your text-based data model, this chore is reduced to a single prompt. User Prompt: “Please analyze the files inside this project zip archive and derive the semantic model information in the form of a structured document. Read the TMDL files and produce documentation detailing our tables, relationships, column metadata, storage modes, source types, and tags.” Claude will access your archive, parse the underlying Tabular Model Definition Language (.tmdl) files, and immediately understand how your data is wired together. It will generate a pristine, structured technical breakdown right in the chat window, including active/inactive table relationships, storage modes (Import vs. DirectQuery), and source metadata. This can be instantly copied into your internal technical handbooks or project handover reports. Claude parsing model schema data to auto-generate enterprise-grade documentation. 2. Injecting New DAX Measures via AI Code Generation Writing a complex DAX statement is easy enough, but manually opening Power BI, clicking ‘New Measure’, typing the code, and waiting for the model to compile can slow down your velocity. Let’s have Claude write and inject a new DAX measure straight into our model file. User Prompt: “I want to add a new DAX measure to calculate the number of days between ‘order date’ and ‘ship date’ using the PBIP files in this project. Scan the ‘order.tmdl’ file to find the correct table structure, place this new measure right after the existing ‘custom rank’ measure, and repack the files into an updated zip archive so the model is updated.” Claude searches the zipped archive, isolates order.tmdl, locates the exact line where the custom rank measure is defined, and cleanly appends your new aging calculation directly into the underlying code layer using proper TMDL syntax: measure ‘Orders'[Days to Ship] = DATEDIFF( SELECTEDVALUE(‘Orders'[Order Date]), SELECTEDVALUE(‘Orders'[Ship Date]), DAY ) Claude
How to enable Power BI Copilot, Is it free with Trial or Not?
How to enable Power BI Copilot, Is it free with Trial or Not? This document provides a decisive procedural guide to activate and enable the Power BI Copilot feature, directly addressing common user inquiries concerning workspace compatibility, trial version limitations, and essential tenant configurations. Power BI Copilot: A Core Feature Explained: Power BI Copilot leverages generative artificial intelligence to streamline data analysis and report generation. It integrates directly into both the Power BI Desktop application and the Power BI service platform, offering enhanced analytical capabilities. Trial Version and Free Usage: Key Limitations Highlighted: Crucially, paid Stock Keeping Units (SKUs) exclusively support Power BI Copilot. Neither free versions nor Power BI Pro subscriptions offer this feature. Specifically, organizations must deploy Fabric Capacity (F64 or higher) or Premium Capacity (P1 or higher) to enable Copilot. This requirement is clearly stated in official Microsoft documentation. Workspace Compatibility: Essential Requirements Defined: To effectively utilize Power BI Copilot, organizations must configure the designated workspace with either Premium Capacity or Fabric Capacity. Standard workspaces, including those using Premium Per User licenses, remain incompatible. Microsoft’s documentation explicitly outlines this prerequisite. Enabling Copilot within Microsoft Fabric: A Step-by-Step Approach: Assuming the workspace meets capacity requirements, organizations must then enable Copilot within the Microsoft Fabric environment. This is accomplished through the tenant settings in the Power BI admin portal. Access Tenant Settings: Initially, organizations navigate to the Power BI settings and subsequently access the admin portal. Activate Copilot Functionality: Next, they locate the “Copilot” settings and enable “Users can use Copilot and other features powered by Azure OpenAI.” Organizations can apply this setting to the entire domain or specific security groups, as per their policy. Below images show the settings location Geographic Region Constraints: Addressing Access Limitations: If the tenant or capacity resides outside the United States or France, organizations may encounter access limitations. Therefore, they must configure an additional tenant setting. Enable Cross-Geographic Data Processing: Specifically, they enable “Data sent to Azure OpenAI can be processed outside your capacity geographic region” within the tenant settings. Determine Geographic Region: You can determine Power BI geographic region via the “Help and Support” section in the Power BI application. Capacity-Specific Billing: Configuring Usage and Costs: Additionally, organizations can enable Copilot usage and billing on a single capacity through the tenant settings. Verification and Implementation: Enabling Operational Usage: Once organizations verify workspace capacity and tenant configurations, they can access Power BI Copilot within their reports. By entering edit mode and selecting Copilot, the chatbot interface becomes available. Conclusion: Maximizing Power BI Copilot Capabilities: In summary, organizations can effectively activate and configure Power BI Copilot by following these steps. They must adhere to workspace requirements and tenant configurations to ensure successful implementation. Organizations should consult official Microsoft documentation for the most up-to-date information. Check this blog to find Co-pilot like features with Tableau (Tableau Agent) Note: Watch the above You tube video for detailed step by step analysis Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp
How to set Power BI Date Slicer default to TODAY () in 5 mins?
How to set Power BI Date Slicer default to TODAY() in 5 mins ? This document outlines a robust methodology to implement dynamic “today” slicer within Power BI, thereby enabling users to efficiently filter reports by the current date while preserving the capacity to analyze data across all dates. Challenge Identification: Power BI’s standard date filtering mechanisms lack a dedicated “today” option within slicer visuals, which subsequently impedes user efficiency when focusing on current-day data. Consequently, this methodology addresses this limitation by providing a practical and adaptable solution. Solution Deployment: DAX and Calendar Table Construction (Check above Video for Step by Step) Calendar Table Generation: Initially, one generates a comprehensive calendar table using the DAX CALENDAR function. This action ensures the inclusion of all dates within the relevant data range, encompassing the minimum and maximum order dates. Subsequently, one establishes a relational link between the generated calendar table and the primary data table Dynamic “Today” Column Derivation: Next, one introduces a calculated column within the calendar table. This column conditionally displays “today” for the current date and retains the original date values for all other dates. Specifically, one employs the DAX functions IF and TODAY to implement this conditional logic. Furthermore, for sorting consistency and data type compatibility, one uses the FORMAT function to convert the date values to string format. Ultimately, this derived column serves as the basis for the slicer visual. Slicer Visual Implementation: Then, one integrates a slicer visual into the Power BI report. Following that, one populates the slicer with the newly created calculated column. Finally, one configures the slicer’s display as a dropdown or list and arranges it in descending order to prioritize the “today” option. Benefits Realized: Enhanced User Experience: The “today” option simplifies the selection of current-day data. Real-time Data Representation: The slicer automatically reflects the current date, thus ensuring real-time relevance. Flexible Data Exploration: Users retain the ability to examine data across all dates. Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp
Power BI Price Hike: What It Means for You and How Tableau Compares
Power BI Price Hike: What It Means for You and How Tableau Compares Microsoft has recently announced a price increase for Power BI, effective April 1, 2025. This marks the first price hike since the platform’s launch nearly a decade ago. The cost for Power BI Pro licenses will rise from $10 to $14 per user per month, while Power BI Premium Per User (PPU) licenses will increase from $20 to $24 per user per month. For many organizations relying on Power BI, this change could impact their budget. But what does this mean for you? Should you stick with Power BI, or is it time to consider alternatives? Tableau Pricing: A Comparative Look Tableau offers several licensing options tailored to different needs. Here are the last available prices (please note that the latest pricing is not visible online, and you may need to contact Tableau’s sales team for the most up-to-date information): Tableau Creator: $115 per user per month (billed annually) Tableau Explorer: $70 per user per month (billed annually) Tableau Viewer: $35 per user per month (billed annually) These rates apply to both Tableau Server and Tableau Cloud. For those seeking more advanced features, such as AI-powered analytics, Tableau’s premium package, Tableau+, might be a worthwhile investment. Should Tableau Enthusiasts Be Happier? Some Tableau enthusiasts claim that the price increase for Power BI could make Tableau a more appealing option for several reasons: Competitive Edge: With the hike in Power BI’s prices, organizations might find Tableau’s advanced features and capabilities more attractive, despite its higher entry-level cost. Feature-Rich: Tableau is known for its robust and user-friendly interface, which continues to be a strong selling point. The increased cost of Power BI could push some potential users to consider Tableau’s feature-rich environment. Market Share: The change could potentially enhance Tableau’s market position and user base, possibly influencing future Gartner ratings in Tableau’s favor. Cost Comparison: Tableau Still More Expensive However, it’s important to note that Tableau remains significantly more expensive than Power BI, especially when considering the need for a Tableau Creator license to develop dashboards. In contrast, Power BI Pro at $14 per user per month allows for comprehensive dashboard creation and data analysis, making it still a more cost-effective option for many users. (but not all, it will vary with the size of DB and no. of users) New Players in the Market This price hike might also create opportunities for other BI tools with more competitive pricing, such as Looker, to attract new users. Looker and similar platforms could become appealing alternatives for businesses looking for robust BI solutions without the higher costs associated with Power BI and Tableau. Making the Right Choice for Your Business Ultimately, the decision between Power BI and Tableau or any other tool will depend on your specific business needs, feature preferences, and budget constraints. All leading platforms have their unique strengths and dedicated user bases. Conclusion In summary, while Power BI has become more expensive, it still remains more affordable than Tableau’s equivalent/higher-tier licenses. The choice will ultimately depend on your specific requirements and budget. If you’re curious about the features or capabilities of Tableau or Power BI, feel free to explore further to make an informed decision. What’s Next? As businesses navigate these changes, staying informed and adaptable will be key. Whether you choose to stick with Power BI or consider switching to any other tool, understanding the full scope of each platform’s offerings will help you make the best decision for your organization.I hope this blog gives you a fairer picture of the current landscape between Power BI and Tableau. Let me know if you have any questions or need further insights! Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp Tableau Desktop Specialist Certification Course Unlock Your Potential with Tableau Desktop Specialist. Become a Tableau Desktop Specialist with our comprehensive certification course. Designed to enhance your data visualization skills, this program equips you with the expertise to create compelling visualizations and make data-driven decisions. Start Courses
Bookmarks as a Reset Button -Power BI
Bookmark With Power BI A bookmark records the current state of a report page. It includes the settings you’ve made to the filters, slicers, and visuals on that page. Once you customize the report page to your liking, give it a friendly name. You can now easily return to that state of the report page. When you click a bookmark, Power BI returns you to that view.https://youtu.be/0BgK3pBuvY4 Type of Bookmark: There are two sorts of bookmarks: Personal and Report To record the current state of a report page, you can add bookmarks when editing a report in Power BI Desktop and the Power BI service. The current filters, slicers, cross-highlighted visuals, sort order, etc. are saved in bookmarks. Personal and report bookmarks are the two different categories. Report bookmarks are created and used in this article. When you add bookmarks to a report, everyone who views the report can return to exactly where they left off by choosing the saved report bookmark. Let’s do it from scratch so that in order to do a redo and so if I want to do it from the sketch let me just remove it. Steps to follow in Power BI : Step: 1- Process to add bookmark The first step would be to go to View and create the bookmark. I will add a bookmark, which means the present status of my report over there will be saved at this bookmark. By default, it will give it a name called ‘Bookmark 4’, change this to a meaningful name that also describes the filter context. I’ll call this one reset-2. How to rename the bookmark button: Firstly, go to the view after that add the bookmark and click the bookmark in order to change the name. With the help of the option called rename, change the name to whatever relevant. In essence, a bookmark records the status of a report page, including any filtering modifications you may have made to the slicer and graphics there. Step: 2-Process to add a reset button The Second Step would be to go to insert and insert the button, we have picked an already available reset button. Go to the format > Action > and then pick Bookmark as Type and select the Reset 2 as the destination bookmark. Power of reset button: Now that you want to return as quickly as possible to the default state—the state in which everything is as it should be—this reset button enables you to do so. Remember in this case we are not going to give particular filters because we are creating a reset button so there is no need for that although you can save individual status as well. In this case, we are making the default button that will take you to all, so we won’t make any specific changes to the filter. You can have a button that will have a data set that represents corporate and home office only so you can have one button over there saying that the consumer and corporate you do have multiple users. When to Use Navigator instead: Please go to insert and select the button there is something called Navigator bookmark. If we have several bookmarks over there, in this case, we can even remove this one and this will serve as a wonderful button over there that will enable you to navigate It. The navigator looks really classic, glad to have such a button at the top. Reset button with tableau: With tableau, you have to create a separate sheet over there itself and then make use of action. I hope this post was helpful for you and it will help you to know more about the bookmark and its uses through Bookmark with Power BI. Check out the video on Reset Button with Tableau. Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp
Text/Sentiment Analysis with Power BI
In Power BI, you can use AI Insights to gain access to a collection of pre-trained machine learning models that enhance your data preparation efforts. AI Insights is accessed in the Power Query Editor, and its associated features and functions are accessed through the Home and Add Column tabs in Power Query Editor. Remember that Text analysis with Power BI is much easier with respect to Tableau. You don’t even need to install Python. The AI-enabled feature is embedded with Power Bi but not with Tableau. How to get Dataset For sentiment analysis, we need some feedback or comments. So, here, we’ll extract comments from my own youtube video. Copy the youtube video URL and paste it on export comments to extract the video’s comments. After pasting the URL, click on the export process, and now the file is ready to download. Click on the download file. Steps to follow in Power BI – Step 1: Let’s open Power BI and do it from scratch. Click on Get Data and select Excel workbook. Step 2: Select the table from the left bottom shown in fig. To transform the data, click on Transform Data. Step 3: Now, we’ll remove the top rows. Click on remove rows, and select remove top rows. Step 4: Specify the number of rows you want to remove. Here, we’ll write 2 to delete the top two rows. Step 5: Now, we’ll make the first row as headers. To transform, click on Transform and then select ‘use the first row as headers. Step 6: Click the columns which you want to remove. After that click the remove columns. Step 7: Click the field which you want to rename. Now we’ll rename the row as row ID. Step 8: Text analytics differences between Tableau and power bi. With tableau, you’ll have to connect that with Python and after that, you need to import packages and so on. But in power bi directly you have AI-backed powered features like text analytics, and vision as your machine learning AI insight section. Now we’ll click on Text Analytics. Step 9: Now, click on score sentiment. So, in the text we’ll select the column name and the column name over there would be the comment. Second one is the Language ISO code which is optional so we are not going with that for now. Step 10: Now, we’ll have a sentiment score for each comment. You can see that one new row has been added as a score sentiment. That is by AI only. And boom, you can see the score sentiment has been updated for every comment over there. We have got the numbers, but we’ll try to make it like a tableau where we have the color code as well. So, we go to the report and we’ll open a new page, a brand new page. After that click the lower one. So we’ll select comments and then we’ll select score sentiment. Remember that it will vary from zero to one. In Tableau, it will vary from -1 to 1. So if you are close to zero means a negative comment, close to one means a positive comment, and around 0.5 means neutral command. And after this, we can go to sentiment analysis, conditional formatting, and background color. It’s like orange to blue. We can sort this out so that we have positive comments at the top itself. Now, we’re done with sentiment analysis on Power BI. I hope this post was useful for you and it will help you to kick-start your journey in Text sentiment analysis with Power bi. Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp
Incremental Refresh-Power BI
Why do we need incremental refresh? By offering automated partition formation and maintenance for dataset tables that often load new and updated data, incremental refresh procedures expand planned refresh activities. For the majority of datasets, this is one or more tables, similar to a fact table in a relational or star database structure, that hold transaction data, which changes often and can increase enormously. The amount of data that needs to be refreshed can be greatly reduced while still ensuring that even the most recent changes at the data source are included in the query results. This can be achieved by using an incremental refresh policy to partition the table, refreshing only the most recent import partition(s), and optionally utilising an additional DirectQuery partition for real-time data. With the Zomato delivery system, I tried to make the incremental refresh notion simpler. The choice thus comes down to an incremental refresh vs. a full refresh. Consider Zomato’s operations while contemplating incremental refresh. I hope you might have ordered with Zomato at-least once An order may enter a number of states after being placed, including new order, in progress, pending, and closed, before quickly closing. Technically speaking, we comprehend that the entire procedure should be completed in around an hour for a business like Zomato where it is important to deliver on time. Sometimes they even offer you a free meal if the item is not delivered on time To be safe, we can prolong this for 24 hours (for example resolving any escalation or so). Therefore, you can forgo duplicating the static data and switch to incremental mode if you are running a reporting environment where you need to duplicate this data on a daily basis. Please watch the entire video to see a variety of additional examples that should make it easier for you to understand the complex idea of incremental refresh; we’ll shortly present the implementation portion of this video in the second part of this series, hence stay tuned Share Share Course Page Link Share on social media Facebook Twitter Linkedin Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp
Power BI Certification
In this video, I share my experience with Power BI PL 300 Certification, and I hope it will help you plan for the important event. The Power BI data analyst delivers actionable insights by leveraging available data and applying domain expertise. The Power BI data analyst collaborates with key stakeholders across verticals to identify business requirements, cleans and transforms the data, and then designs and builds data models using Power BI. Here is the certificate that I was looking for Skills measured Prepare the data (15-20%). Data modelling (30-35%) Visualize and analyse the data (25-30%) Asset deployment and maintenance (20-25%) Please note this learning path, which is a must to refer to or start with https://learn.microsoft.com/en-us/certifications/exams/pl-300 Training Material I am sharing some of the links that helped me on this journey of PL 300 certification. 1.Exam Guide: https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300?tab=tab-learning-paths 2.Course Download: https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RWREll 3.Learning Path: https://docs.microsoft.com/en-us/learn/certifications/examinations/pl-300?tab=tab-learning-paths 4. It makes sense to purchase a quick crash (10 hour or so) course from Udemy that is exclusively for PL-300; please check the ratings, which should be around 4.5 or higher. You can also purchase a mock-test for Power BI-PL-300. 5. My PL-300 Playlist Series Sample Questions – PL-300 – YouTube Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp
