How to Query Power BI Dataset

When you need to go deeper than the visuals and slicers in a report, querying a Power BI dataset directly is your next step. This allows you to perform in-depth analysis, validate your data model, and integrate your Power BI data with other tools. This article will walk you through several methods for connecting to and querying your dataset, using tools like the popular DAX Studio, Python, and even trusty old Excel.

Why Go Beyond the Standard Report View?

Working within the Power BI interface is great for building and consuming reports, but sometimes you need more direct access. Direct queries empower you to move from simply viewing data to actively investigating it.

Perform Advanced Analysis: Execute complex calculations or multi-step data transformations that would be cumbersome or impossible to create with standard visuals and measures alone.
Validate and Troubleshoot Data: If a number on your dashboard looks off, a direct query is the fastest way to see the raw data behind it. This is invaluable for auditing calculations, checking data relationships, and confirming that your ETL process is working as expected.
Tune and Optimize Performance: Curious why a measure is slow to compute? By using tools like DAX Studio, you can analyze query plans and performance diagnostics to identify and fix bottlenecks in your DAX code.
Integrate with Other Applications: Pull your curated Power BI data model into other applications. You might want to use it in a Python script for a machine learning model, pull specific tables into a different reporting tool, or simply grab a quick data dump for some ad-hoc analysis in a spreadsheet.

The Connection Key: Understanding XMLA Endpoints

Before you can connect any external tool, you need the "address" for your dataset - and that address is its XMLA endpoint. Think of the XMLA (XML for Analysis) endpoint as a direct access point to the engine running your Power BI dataset, which is essentially an instance of SQL Server Analysis Services (SSAS).

It's important to know that this capability is a premium feature. To use XMLA endpoints, your dataset must be in a workspace on a Power BI Premium capacity, Premium Per User (PPU), or Power BI Embedded SKU.

Finding Your XMLA Endpoint URL

Locating the connection URL is straightforward. An admin for the workspace will first need to enable XMLA read/write access in the capacity settings, but once that's done, you can grab the URL.

Navigate to the Power BI Service and open the workspace containing your dataset.
Click on Settings for the workspace.
In the Settings panel, select the Premium tab.
At the bottom, you'll find the Workspace Connection settings. The URL displayed here is your XMLA endpoint. Copy it to your clipboard.

This URL works for all published datasets within that specific workspace. Now that you have the keys, let's unlock the data with a few popular methods.

Method 1: Querying with DAX Studio

For data analysts serious about working with Power BI data models, DAX Studio is an essential tool. It's a free, third-party application packed with features for writing, executing, and analyzing DAX queries against Power BI and Analysis Services Schemas. It's the most common and powerful way to interact with your data model outside of Power BI itself.

Step 1: Connect to Your Dataset

First, download and install DAX Studio from the official website. The setup is simple, and it integrates well with tools like Excel.

Open DAX Studio. The 'Connect' dialog box will immediately appear.
Select PBI / SSAS Tabular as your connection type.
In the text box, paste the Workspace Connection URL you copied from the Power BI Service.
Click Connect.
You'll likely be prompted to sign in with your Microsoft account to authenticate your access.
Once connected, the dialog box will expand to show a dropdown of available databases (your datasets). Select the specific dataset you want to query and click Connect again.

Step 2: Write and Execute a Query

Once connected, you'll see a metadata pane on the left with all your tables, columns, and measures, and a query pane in the center. Unlike SQL's SELECT statements, queries in DAX almost always start with the EVALUATE keyword.

Let's start with the simplest possible query: returning an entire table. Type this into the query window and either click the 'Run' button or press F5:

EVALUATE 'Sales'

This will return all the rows and columns from your 'Sales' table in the results grid at the bottom. It's a quick and dirty way to inspect raw data.

For something more useful, you can use functions like SUMMARIZECOLUMNS to create aggregations, similar to a GROUP BY in SQL. This function is highly optimized for performance and is the recommended way to generate summarized data.

Here's a query that calculates the total sales amount for each product category per year:

The results will be a clean, summarized table you can then export to CSV or copy directly into another program.

Method 2: Using Python for Programmatic Access

For data scientists, software developers, or anyone needing to automate data workflows, Python provides a powerful and flexible way to query a Power BI model. The most direct method is using the pyadomd library, a Python client for Analysis Services.

Step 1: Set Up Your Python Environment

First, you need to install the library. You can do this with a simple pip command in your terminal:

pip install pyadomd pandas

A frequent stumbling block is the need for the Analysis Services client drivers on your machine. The pyadomd library depends on them to communicate with the XMLA endpoint. You'll need to install the appropriate MSOLAP (Microsoft Analysis Services OLE DB Provider) driver. You can typically find it as part of the SQL Server Feature Pack installer.

Step 2: Connect and Query with Code

Once your environment is ready, writing a script to connect and query is quite simple. The key is correctly formatting the connection string, which uses the XMLA endpoint you found earlier.

Here's a sample Python script that connects to a dataset, runs a DAX query, and loads the result into a Pandas DataFrame for further analysis:

This approach opens up endless possibilities. You can schedule scripts to run on a regular basis, pull data for custom visualizations, or feed your model's outputs into advanced statistical analysis or machine learning pipelines.

Method 3: Connecting Your Power BI Dataset to Excel

Sometimes, all you want is to slice and dice your trusted Power BI data within the familiar confines of an Excel PivotTable. Microsoft has made this incredibly easy. While you're not writing a raw DAX query in a script, this method uses a live connection where Excel intelligently generates the necessary queries in the background as you interact with the PivotTable.

Creating a Live Connection

Open a new workbook in Microsoft Excel.
Go to the Data tab on the ribbon.
Click Get Data → From Power Platform → From Power BI.
If it's your first time, you may need to sign in to your Power BI account.
A pane will open on the right showing all the Power BI datasets you have access to.
Find the dataset you want to connect to and select it. Click Insert PivotTable at the bottom.

And that's it! Excel will create a new PivotTable that is live-connected to your Power BI dataset. You'll see all your tables, columns, and measures in the 'PivotTable Fields' pane. You can now drag and drop items just like any other pivot table, secure in the knowledge that your data and calculations are coming from the authoritative, centralized Power BI model.

Final Thoughts

Learning how to query a Power BI dataset via its XMLA endpoint unlocks a new level of control and flexibility. Whether you use the detailed diagnostic tools in DAX Studio, the automation power of Python, or the familiar interface of Excel, you can now analyze, validate, and integrate your data in ways that go far beyond standard dashboarding.

While these methods are incredibly effective for technical users, we believe accessing business insights shouldn't require that level of expertise. That's why we created Graphed, where we connect directly to your core business data platforms. There's no need to learn DAX or code in Python, just ask questions like "Which campaigns had the best ROI last month?" in plain English, and Graphed builds an interactive, real-time dashboard for you instantly.