Power Query in the Trenches: 3 Solutions to a Data Problem
Power Query in the Trenches: 3 Solutions to a Data Problem Data often needs to be reshaped before it can be used for analysis. And just like there are usually three or more ways to do things in Excel, there are often multiple routes to convert ugly data into the required tabular format. It’s fascinating how many different approaches you can take! In this session, Melissa de Korte will explore three different solutions that can be used to unwind a messy data set using practical problem solving with Power Query/M. Agenda: Introductions (5 mins) Presentation (55 mins) Q&A session (15 mins) About Melissa de Korte: Melissa de Korte is an all-round Power Platform enthusiast and Enterprise DNA Expert. On a mission to share what she's learned, in particular her passion for solving practical problems with Power Query/M. This event is online. You may need to install Zoom: https://zoom.us/download Space is limited so reserve your seat.Allocation and Visualization of Budgets with Seasonality | Power Query and DAX
In this session, we will look at a common budget allocation problem that involves seasonality. Seasonality has a major influence on many businesses. The ability to factor in seasonality in budgets and forecasts could help to create clear and meaningful comparisons with actuals. Allocating a high-level budget to lower levels of granularity, on the other hand, can be difficult. I'll demonstrate how to split seasonal budgets utilizing Power Query. This will allow us to compare the budget on a quarterly, monthly, and even daily basis. At the end, I'll demonstrate how to use dynamic DAX calculations for effective budget-to-actual comparisons.Efficient Data Management Using Delta Tables in Apache Spark - Microsoft Fabric
NextGen Data Aspirants Community (NDAC) Microsoft Fabric Data Connect - An Interactive Knowledge Sharing Event The NextGen Data Aspirants Community (NDAC) is delighted to invite you to its upcoming knowledge-sharing session that explores the cutting edge of data management within the Microsoft Fabric ecosystem. Date: 7th June 2025 Time: 5:00 PM IST Duration: 1 Hour 30 Minutes Session Overview: In an era where data is the new currency, efficient data handling is paramount. This session delves into how Delta Tables in Apache Spark, integrated within Microsoft Fabric, empower data engineers and analysts to design robust, scalable, and high-performing data pipelines. You’ll discover how Delta Lake simplifies: -Streaming + batch ETL pipelines -Managing slowly changing dimensions (SCDs) -Performing GDPR-compliant deletes -Enhancing query performance and reliability What You’ll Gain: -Hands-on strategies to optimize data pipelines -Practical tips to reduce operational overhead -Real-world scenarios showing improved performance -A live walkthrough of Microsoft Fabric in action -Audience Q&A – Engage directly with our expert speaker Whether you're a budding data engineer or a seasoned analytics professional, this session offers actionable insights to modernize your data stack with confidence. Come learn how to modernize your data stack with confidence. Let’s Explore, Learn, and Innovate—Together! Join us at WhatsApp: https://chat.whatsapp.com/GHBzZ7rKqylLyXTGJqyTxz Follow us at LinkedIn: NextGen Data Aspirants Community: Overview | LinkedIn Join us at teams: https://teams.live.com/l/community/FEAVIPJvr_9CQiYFgI Thank you! #MicrosoftFabric #DataAnalytics #TechEvent #KnowledgeSharing #DataInnovation #PowerBI #FabricConnect #CloudComputing #DataWarehouse #LakehouseArchitecture #BIAnalytics #DigitalTransformation #TechLeaders #SmartDataSolutionsDay2: Ingest and Transform Data (Arabic)
Continue your Fabric journey with Day 2 of our Fabric Data Engineer Associate (DP-700) study series — hosted by the Sudan Microsoft Fabric Study Group, under Fabric Data Days and supported by the global Microsoft Fabric community. This session dives into one of the most essential DP-700 skills: data ingestion and transformation. Learn how to efficiently design, load, and transform data in Microsoft Fabric using both batch and streaming techniques. Topics: • Ingest and transform batch data • Ingest and transform streaming data • Design and implement loading patterns Date: 16th November 2025 Time: 7:00 PM (Sudan) | 8:00 PM (Saudi Arabia) | 12:00 PM (CST) Speakers: Arwa Aldoud Esraa kamal Abdallah Host: Aala Omer Taha Fabric Data Days | Sudan Microsoft Fabric Study Group Empowering our community to learn, share, and get certified together — as part of the global Microsoft Fabric initiative. Join us: - User Group - LinkedIn - Join us on WhatsApp
Migrating Azure Function with Queue Storage Trigger to Microsoft Fabric for ETL Processing
Hello, I'm currently facing a challenge in my project and looking for advice or best practices on how to transition to a new setup. **Current Setup:** I have an Azure Function that is triggered by Queue Storage, which is continuously listening for incoming messages. Upon receiving a message, the function reads it and then asynchronously calls another function to process the message and perform ETL (Extract, Transform, Load) operations. This setup ensures that messages are processed in real-time, as they arrive. **Goal:** My objective is to migrate this continuously listening and processing scenario to Microsoft Fabric. I'm interested in understanding the best way to implement this to maintain or even enhance the efficiency of real-time message processing, while leveraging the scalability, management, and orchestration capabilities that Fabric offers. **Specific Questions:** 1. How can I achieve a continuous listening mechanism for incoming messages within Fabric, similar to the Queue Storage trigger in Azure Functions? 2. What are the best practices for implementing asynchronous and real-time message processing in Fabric? 3. Are there any recommended patterns or services in Fabric specifically suited for real-time ETL operations? 4. How does Fabric facilitate the orchestration of these microservices, particularly in handling errors and implementing retry logic for uninterrupted processing? I would greatly appreciate any insights, documentation links, or examples of similar setups. Thank you for your support and guidance. Best regards, YoelParquet, ADLS Gen2, ETL, and Incremental Refresh in one Power BI Dataset
A year ago, I was developing a solution for collecting and analyzing usage data of a Power BI premium capacity. There were not only some simple log files, but also data that I had to convert into a slowly changing dimension type 2. Therefore, I decided for the following architecture - Azure Data Factory pipelines collect data on daily basis, the raw data is stored in a data lake forever, and the cleansed data is then moved to a SQL Server database. Because the data is stored on a SQL Server, I can use incremental refresh in Power BI service. It works perfectly. But the times are changing, new requirements are coming, and I have found a new straight way, how to load logs directly from the data lake into a Power BI dataset. And all that incrementally! Where I need a SCD, a SQL Server database will stay in the middle, whereas for all other data I can use a new mindset. In this article I want to show you how you can load parquet files stored in an Azure data lake direct into your Power BI dataset. It involves an incremental refresh and an ETL process, too!
