Fabric Notebooks are too slow for real-time interactions, and Fabric SQL is expensive for simple write-back. This guide shows you how to bypass these architectural limitations. Learn to build a "Translytical" solution using User Data Functions (UDF) to write to Azure SQL Database instantly, securing everything with Private Endpoints and Key Vault.

This technical guide benchmarks performance differences between SharePoint.Files and SharePoint.Contents within Power BI. It further details a custom Power Query M script implementation using the SharePoint REST API to minimize network requests and optimize bulk file retrieval for faster dataset refreshes.

Struggling with slow SharePoint List refreshes in Power BI? This guide dives deep into the limitations of the native connector (Implementation 2.0) and how to fix common errors like RenderListDataAsStream. But we don't stop there. I share a custom, copy-paste M script that utilizes the SharePoint REST API to fetch 5,000 items per page, handles complex column types dynamically, and reduces refresh times from minutes to mere seconds.

Using a Lakehouse in Microsoft Fabric automatically gives you a SQL Analytics Endpoint, but its metadata sync is not always instant. This post explains why SAE can lag behind your Delta tables and how to wake it up, refresh it manually, or call the Refresh SQL Endpoint Metadata API from a pipeline or notebook.

Power Query is case sensitive, while DAX and the model are case insensitive by default. That mismatch can cause weird relationship key issues. Best fix: Avoid text columns as relationship keys. If you have to use them, normalize them in Power Query with Text.Lower or Text.Upper, or de-dup with a case-insensitive comparer like:  Table.Distinct(Source, {"letter"}, Comparer.OrdinalIgnoreCase).

Dataflows storage without guessing. Gen1 writes CSV snapshots into a CDM folder in your ADLS Gen2, visible only after you link the workspace to your lake. With Enhanced Compute Engine, refresh also fills a SQL cache you can use for DirectQuery. Gen2 with no destination lands data in the semi-hidden DataflowsStagingLakehouse and surfaces it through DataflowsStagingWarehouse, stored as Delta tables backed by Parquet. Where the files live, how to reach them, and what to watch ou

A hands-on guide to DAX user defined functions. See how to declare UDFx with DEFINE and FUNCTION, wire up parameters and modes, and pick between val and expr. Learn where they shine in measures, calc groups, and visuals, what to avoid in calculated columns, how to format them in TMDL, and how to list them via DMVs and Model view.

List.Contains scans and stalls at scale. This post benchmarks lookup patterns on real tables and shows why buffering only helps a little, why joins are solid, and how a record map built with Record.FromList plus Record.FieldOrDefault delivers quick, clean lookups. Learn when to keep the merge and when to build the map.

Explore how to connect Power BI to GraphQL APIs using M. This guide walks through GraphQL fundamentals like queries, variables, fragments, introspection, and pagination, then takes it further by auto-generating typed tables with dynamic schema mapping. All examples use public GraphQL APIs like Rick & Morty and Countries.

Comprehensive guide to Power Query M evaluation, covering lazy versus eager evaluation, immutable variables and streaming of tables, explicit buffering, query folding, function parameter evaluation and higher-order thunks, variable scope effects, and record-field laziness—illustrated with random-number, timestamp, and delayed-execution examples.

Still copying CALCULATE for every category? Time to break the habit. This post shows how DAX iterators like SUMX and AVERAGEX can replace that mess with cleaner, scalable logic using table expressions.

Power BI just got an upgrade. With User Data Functions and Translytical Flow Tasks in Microsoft Fabric, you can now write comments or updates directly to your SQL tables — straight from a report. No extra tools, no clunky workarounds. This guide walks you through building your first real-time write-back feature step by step.

While loop in M Language using List.Generate()

Refresh Semantic Model every minute in Fabric

Make your Power Query Dynamic with Parameters, examples like: synced date filter on queries, filter the same list of values, etc.

tl;dr DAX has probably hundreds of functions, however, several of them stand out. You will probably use them in every project. Normally I would add them here, but below there's table of contents. Disclaimer: If you don't see your favorite function here, it doesn't mean it's bad, this article covers my top pick of really core ones. Criteria explanation I won’t dive into DAX (Data Analysis Expressions) concepts or detailed explanations in this article. Instead, I’ll go straight

tl;dr "M" is a language used in Power Query to transform, filter, and combine data. Power Query automatically generates M code based on your "clickable" actions, but you can also write it manually for more complex operations. M is primarily written by calling pre-built functions that modify data step by step, making it intuitive and easy to learn. Disclaimer: This article is aimed for beginners and will not go into depths of M. Goal of the artcile is to introduce you to "M",

tl;dr Power Query provides a native file combiner, but it can feel complex and generates numerous queries. In this article, you'll learn two methods to create a simpler, more editable, and controllable file combiner with essentially just one-ish line of code. The guide is tailored for users who prefer a "click-based" approach. Native File Combiner You've likely encountered a feature in Power Query that allows you to combine files using a specific sample file as a reference. T

tl;dr když pracujete s API v Power Query, je důležité znát několik autentizačních metod: Anonymous, API Key, Bearer Token, Basic a OAuth...

tl;dr pagination is a form of dividing data into smaller chunks (pages). With this method we can achieve more efficient transfer of data....