We have a new contest, officially launching Monday, March 16th. 

Build a clear, end-to-end Fabric solution that uses a Medallion architecture: ingest raw data with pipelines, manage both data and metadata, transform it into Silver and Gold layers in a Fabric Data Warehouse (using a dbt job), and finish with an AI-ready Power BI semantic model. The goal is to show a repeatable pattern where well-designed pipelines and warehouse models lead to trustworthy Copilot answers in Power BI.

In the previous blog of this series, we explored how modern data platforms use columnar file formats like Apache Parquet to store data efficiently and enable faster analytics.

While Parquet significantly improves performance and storage efficiency, it still has limitations when it comes to managing data changes, maintaining data consistency, and handling concurrent operations.

To solve these challenges, modern platforms introduced Delta Tables.

In this blog, we will explore what Delta Tables are, why they were created, and how they improve reliability in modern data platforms such as Microsoft Fabric and Databricks.

In today’s data-driven world, organizations generate and process massive volumes of data every day. Traditional file formats such as CSV and Excel were once sufficient for storing and analyzing business data. However, as data volumes grew and analytics became more advanced, these formats started showing limitations.

Modern data platforms like Microsoft Fabric, Apache Spark, and Databricks rely on optimized data formats designed for large-scale analytics. One of the most widely used formats today is Apache Parquet.

In this blog, we will explore what Parquet is, why it was created, and why it has become the foundation of many modern data platforms.

While working with a mature Power BI semantic model, we faced a simple yet critical question:

Where is the data for each table actually coming from?

To solve this problem, we built a simple automated approach that extracts the data source information for every table directly from the semantic model.

In this article, I will walk through the concept and implementation.

In modern data engineering, organizations deal with multiple data sources such as Excel files, SQL databases, APIs, and cloud storage systems. The challenge is not just collecting the data, but transforming and storing it efficiently for analytics.

Microsoft Fabric simplifies this process using Dataflow Gen2, a low-code data ingestion and transformation tool.

In this blog, we walk through how to ingest data from any source and store it in a Lakehouse using Dataflow Gen2.

For many years, SQL Server Reporting Services (SSRS) has been a reliable solution for creating detailed, paginated reports. Organizations have used it extensively for operational reporting, invoices, financial statements, and structured data outputs.

However, as businesses increasingly demand interactive dashboards, real-time insights, and cloud integration, Power BI has emerged as the modern analytics platform of choice.

Migrating reports from SSRS to Power BI allows organizations to preserve their existing data logic while enabling interactive visualizations, self-service analytics, and scalable cloud-based reporting.

The Power BI Model Context Protocol (MCP) servers let AI tools interact with Power BI using natural language. There are two flavors of MCP server: a Local (Modeling) MCP server and a Remote MCP server . The local Modeling server runs on your own machine and provides rich semantic-model editing capabilities, whereas the Remote server is a hosted cloud endpoint that lets AI agents query existing Power BI models. In practice, the local Modeling server is used for development and modelmanagement scenarios, while the Remote server is aimed at analytical and insights scenarios. In this blog we will see various usecases of  Remote MCP Server.

🚨 “Unable to save document – Power BI Desktop ran out of memory trying to save the data model” 😩

This weekend I faced a problem in PowerBI: Unable to save document – Power BI Desktop ran out of memory trying to save the data model. The solution looks easy but took me 72 hrs to find it, in this article is what I did to solve it and everything was related to RAM memory.

What’s your go-to fix for these RAM nightmares? Let me know 👇

The Power BI Model Context Protocol (MCP) servers let AI tools interact with Power BI using natural language. There are two flavors of MCP server: a Local (Modeling) MCP server and a Remote MCP server . The local Modeling server runs on your own machine and provides rich semantic-model editing capabilities, whereas the Remote server is a hosted cloud endpoint that lets AI agents query existing Power BI models. In practice, the local Modeling server is used for development and modelmanagement scenarios, while the Remote server is aimed at analytical and insights scenarios. In this blog we will see various usecases of Local MCP Server

One-click tenant-wide solution for Power BI and Fabric Impact Analysis + Governance for Tenant Admins. Temporarily grants access into any workspace to automate the backups of reports, models, and dataflows while extracting complete metadata for impact analysis, refresh tracking, and full visual-level lineage across your entire tenant.

Power BI limits you to a small set of fonts. In my latest blog, I show how to unlock unlimited fonts in Power BI using SVG and embedded WOFF2, no installs, no custom visuals, no theme modifications.

Check it out and let me know your thoughts.

If you are new to Power BI and you hear the term “Power BI MCP Server”, it may sound very technical and confusing.

Let’s simplify it.

Imagine this situation:

• You open Power BI.
• You build reports.
• You create measures using DAX.

Now imagine instead of writing DAX manually, you simply type:

“Create a measure for Total Sales for the last 30 days.”

And it automatically does it for you.

That ability, where AI can directly talk to your Power BI model, is possible because of something called a Power BI MCP Server.

Let’s understand this slowly.

Step 1: First Understand the Problem

Today AI tools like:

• GitHub Copilot
• ChatGPT
• Other AI assistants

Can answer questions.

But they don’t automatically understand:

• Your Power BI model
• Your tables
• Your measures
• Your relationships

They don’t know your dataset structure unless you manually explain it.

So the problem is:

• How can AI directly talk to Power BI models safely and in a structured way?

That’s where MCP comes in.

Step 2: What Is MCP?

MCP stands for: Model Context Protocol

Don’t worry about the big name.

In simple words:

• MCP is a standard communication method that allows AI tools to talk to software systems in a structured and secure way.

Think of it like:

• REST API → For applications
• SQL → For databases
• XMLA → For semantic models

And now: MCP → For AI interacting with systems

Step 3: So What Is a Power BI MCP Server?

A Power BI MCP Server is a special service that allows AI tools to:

• Connect to your Power BI semantic model
• Read model structure
• Create or modify measures
• Rename columns
• Run DAX queries
• Answer business questions
• Validate calculations

All using natural language. Instead of:

• You → Writing DAX
• You → Manually updating model

It becomes:

• You → Ask in plain English
• AI → Talks to Power BI through MCP
• Power BI → Executes safely

Two Types of Power BI MCP Servers

Microsoft currently provides two types. Let’s explain both in beginner language.

Modeling MCP Server (Local)

This is mainly for developers. It helps AI:

• Create tables
• Create measures
• Rename columns
• Update relationships
• Validate DAX

Example:

• You type: “Rename all revenue measures to follow proper naming convention.”

AI:

• Connects to your model
• Identifies measures
• Renames them

This works on:

• Power BI Desktop models
• Fabric semantic models

You install it locally (usually through VS Code).

Remote MCP Server (Cloud Based)

This one is mainly for querying.

It allows AI to:

• Ask questions about your dataset
• Automatically generate DAX
• Run the DAX
• Return results in natural language

Example:

You type: “What were the top 5 products by sales last quarter?”

AI:

• Reads your model structure
• Generates DAX
• Runs the query
• Returns the answer
• You didn’t write any DAX.

That’s powerful for business users.

Simple Analogy

Let’s make it very simple.

Before MCP:

• AI: “I don’t know your dataset.”
• You: Copy paste schema.
• You: Write custom code.
• Maintenance: Complicated.

After MCP:

• AI: “Here are the tools available.”
• AI: “Here is the model structure.”
• AI: “Let me handle it.”

It’s like giving AI a proper login and instruction manual for your Power BI model.

Why Is This Important for Beginners?

If you are starting your career in:

• Power BI Development
• Data Analytics
• Data Engineering

You must understand one thing:

• AI is not replacing Power BI.
• AI is integrating with Power BI.

MCP is the bridge.

In the future:

• Analysts may ask questions directly via AI
• Developers may model using AI assistance
• Data engineers may expose datasets for AI agents

Understanding MCP early gives you a future advantage.

What It Is NOT

Let’s clear confusion.

A Power BI MCP Server is NOT:

• A replacement for Power BI
• A new visualization tool
• A new report builder
• A new database

It is:

• A communication layer between AI and Power BI

How It Works in Very Simple Flow

• You ask a question in natural language.
• AI reads available tools from MCP server.
• AI decides which Power BI action to use.
• MCP server executes the action.
• Result is returned to you.
• Everything happens in a structured and secure way.

Why This Matters in the Real World

Think about large organizations.

Today:

• Developers write DAX.
• Analysts manually test measures.
• Model changes take time.

With MCP:

• AI can assist in modeling.
• AI can validate measures.
• AI can automate repetitive tasks.
• AI can answer business questions directly.

This increases productivity.

Final Thoughts

A Power BI MCP Server is:

A smart bridge that allows AI tools to directly interact with Power BI models in a structured, secure, and standardized way.

If you are a fresher, don’t panic.

• You don’t need to implement it today.

But you should:

• Understand what it is
• Understand why it exists
• Understand that AI + Power BI integration is the future

Because the next generation of Power BI development will not just be:

• DAX + Reports

It will be:

• DAX + Reports + AI + MCP

Join Power BI Corner Group: https://chat.whatsapp.com/KqsU8HUXcsaIoUEyq0zCIq

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There has been a lot of hype around Model Context Protocol (MCP) lately.

If you are a Power BI Developer, Data Analyst, or Data Engineer, you might be wondering:

• Is this just another AI buzzword?
• Or is this something that will actually impact how we build data solutions?

In this blog, I’ll explain MCP in extremely simple terms, then go deeper technically so you understand why it matters, especially if you’re building AI-powered data applications.

This article examines the two types of User-Defined Functions (UDFs) supported in Power BI—DAX UDFs and Fabric UDFs. It outlines their key differences, typical use cases, creation approaches, and capabilities, offering practical guidance on how each can be used to enhance data modeling and enable deeper integration across Power BI and Microsoft Fabric environments 

Designing high-performance reports in Microsoft Power BI isn’t just about visuals it’s about architecture.

When connecting to semantic models, the choice between Live Connection and DirectQuery can dramatically impact speed, scalability, and user experience. Why does Live Connection feel so fast? What really happens behind the scenes when you combine multiple semantic models? And why does performance change when Power BI switches to DirectQuery over Analysis Services?

In this article, we break down:

• How Live Connection leverages the in-memory power of VertiPaq
• How DirectQuery pushes queries back to sources like SQL Server Analysis Services, Azure Analysis Services, or Azure Synapse Analytics
• Why using multiple semantic models forces an architectural shift
• And how to choose the right connection mode for enterprise BI solutions

If you’re building governed, scalable Power BI platforms, understanding this distinction is critical. The right connection strategy doesn’t just improve performance it defines the long-term success of your reporting architecture.