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In a world where data is spread across spreadsheets, databases, cloud services and more, combining all that information can feel like a headache. Power BI, however, makes data consolidation both straightforward and powerful. So, the big question is: Can Power BI combine data from multiple sources? The short answer is yes, it absolutely can and we’re going to walk through exactly how.
“Combine Data Sources” Can Refer to Many Things, So Let’s Explore 6 Options!
The first option we are going to explore might be what comes to mind first for everyone. However, there are so many more ways in which Power BI can combine data, and we will continue looking at those in from option two and onwards.
1. Over 150+ Native Data Connectors
Power BI boasts a long list of 150+ native data connectors, which means you can pull data from SQL databases, Excel files, cloud applications, APIs and so much more. Once those connections are set up, Power BI allows you to shape, model and transform your data into a consolidated, report-ready format.
Why It Matters: With so many options out of the box, you’re not stuck searching for workarounds or plugins every time you need to connect a new system or source. Once your data is in Power BI, you can transform it, model it and shape it to match your reporting needs. One thing to consider is that Power BI offers various storage modes such as: Import, Direct Query, Live Connection, Direct Lake and Composite (Mixed), which all handle data differently. If you don’t want any restrictions on blending data, sticking to Import Mode for everything is usually the simplest route. But as you get more advanced, you’ll see how Direct Query and Live Connection can also open up real-time scenarios and large-scale data solutions. By the way, if you are not familiar with storage modes, please read our blog here which is one of the best in understanding the differences: Power BI Storage Modes Demystified.
Example: Imagine you’re using Excel for monthly sales records and a cloud-based CRM for customer information. By connecting to both sources in Power BI, you can join them into one consolidated semantic model (dataset) no more juggling multiple dashboards or manually updating spreadsheets.
2. Data Transformation & Shaping with Power Query
Before the data even lands in your final report, Power BI’s Power Query (accessible by clicking “Transform Data” from the Home ribbon) gives you all the tools you need to clean, merge and reshape your data. Think of it as the staging area where messy datasets become polished and ready for analysis.
Append vs Merge
- Append Queries is perfect when you have multiple tables or files that share the same structure, like monthly CSV exports for different regions. It stacks them on top of each other to create a single “master” table.
- Merge Queries is your go-to for joining columns from different tables, based on a common key. For example, linking a Product table with a Product Category table, using something like ProductID.
Data Transformation on Steroids: Removing duplicates, filtering out erroneous entries and pivoting columns are just a few of the transformations you can perform. This step ensures your data is consistent and reliable before it’s loaded into the final semantic model.
Example: Say you get a weekly CSV of regional sales data and a monthly Excel file of product info. With Merge Queries, you can instantly fuse product details (like product names and categories) with sales figures by matching on ProductID. This saves you tons of manual spreadsheet work and ensures your analytics are always up to date.
3. Data Modelling
Data modelling might not be the first thing that comes to mind when you think about combining data, but it’s actually a big part of the puzzle. Also known as the Power Pivot component of Power BI, data modelling is all about setting up relationships between different tables.
By defining relationships (like linking a Date table to a Sales table through a DateKey), you’re effectively telling Power BI how different datasets connect. Once those relationships are set, you can seamlessly analyse data from multiple sources in a single report or dashboard.
Example: Imagine you have ERP data for actual sales and a CSV with budget targets. Both sets of data might share a Date column or a ProductID. By creating a shared dimension table, like a central Date table or Product table and establishing relationships, you end up with a unified semantic model, despite each table originating from different systems.
4. DAX (Data Analysis Expressions)
Beyond just relationships, DAX lets you combine data. It’s the formula language within Power BI, allowing you to create calculated columns, measures or even entire tables based on existing data.
Formula Magic: DAX can reference fields from various tables to produce anything from a single computed value to a complex measure. Whether you’re calculating year-over-year growth or converting currencies on the fly, DAX steps up to the plate.
Example: Let’s say your main Sales table has figures in Euros, but you keep your exchange rates in a Currency table. With a DAX measure, you can multiply sales by the appropriate exchange rate for each day. This will make all your sales able to be viewed in USD (or any currency you like) without manually converting each row beforehand.
5. Composite Models: Mixing Storage Modes
Composite Models let you blend data from Import (stored in Power BI) and DirectQuery (queried on the fly) within a single report. You can also leverage “DirectQuery for Power BI Semantic Models and Azure Analysis Services,” which essentially converts a Live Connection source into DirectQuery so you can unify everything in one semantic model.
Why Bother? You might import older historical data for lightning-fast lookups, while keeping real-time queries in DirectQuery for the latest numbers - no refresh needed.
Example: Imagine pulling in several years of sales history through Import, then connecting to a live SQL database for current transactions. The result? Fast queries on past data and up-to-the-minute insights on new sales, all in the same Power BI report.
6. Dataflows: Power Query in the Cloud
Finally, let’s talk about Dataflows. Think of Dataflows as a cloud-based version of Power Query, where you can build reusable ETL pipelines in the Power BI Service. If multiple reports or teams need the same cleaned and combined dataset, Dataflows ensure there’s a single source of truth that everyone can tap into.
Why It’s Useful: Instead of recreating transformations in each Power BI file, you do the heavy lifting once in a Dataflow. Future reports can simply connect to that Dataflow, instantly accessing the processed data.
Example: Your marketing team wants to merge data from Facebook Ads, Google Analytics and your CRM. By setting up a Dataflow that unifies these sources, every new report your analysts create can pull from the same integrated dataflow, ensuring consistent metrics and fewer data discrepancies.
Summary
So, there you have it: Can Power BI combine data from multiple sources? The answer is a resounding YES, thanks to native connectors, transformations, data modelling and DAX capabilties, composite models and dataflows. Whether you’re pulling data from a legacy ERP, a cloud-based CRM or multiple Excel workbooks, Power BI is more than equipped to unify your insights into a single, centralized reporting hub.
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