How to Blend Data in Google Data Studio

By Zekeriya Mulbay

Data Blending is one of the marvels of Google Data Studio. This function helps you join information from multiple sources and gives you a unified view.

By default, charts in Data Studio get their information from a single data source. Blending lets you create charts based on multiple data sources, called a blended data source.

  • You can blend two different Google Analytics data sources to measure the performance of your app and website in a single visualization.
  • You can blend Google Ads and Facebook Ads data into one blended data source and you can see combined ad impressions, clicks and more.

Blending can reveal valuable relationships between your data sets. Creating blended charts directly in Data Studio removes the need to manipulate your data in other applications first, saving you time and effort.

Data blending tools let you combine multiple datasets into a single, new dataset, which you can then visualize in a dashboard or other visualization.

Enterprises collect data from a variety of sources, and they may want to temporarily bring together different datasets to compare data relationships or answer a specific question.

Why it is useful?

The amount of data available in any business is increasing. Blending data sources can enable you to discover insights faster and use that information to make better decisions.

💡Data blending is typically used for ad hoc reporting and rapid analysis.

Data blending tools let non-technical users do things they couldn't before. For example, marketing departments could easily blend data from their CRM system and a spreadsheet with product profitability information.

Advantages of data blending

  • Rapid analysis
  • Fewer data silos
  • Greater efficiency
  • No need to rely on data scientists
  • More revenue (as a bi-product of efficient evaluation of your data)

How Blending Works

Let's check the diagram below. Green and blue colours represent 2 different data sources. After blending we have the 3rd chart below. This 3rd chart brings region and user metrics from the first data source and represents them together with population metric from the second source.

To join the data, each data source in the blend must share a set of one or more dimensions, known as a joining key. Blended data sources include all the records from the leftmost data source in the Blend Data panel and the records from the data sources to the right that share the same values across the join key.

💡 You can join up to 4 data sources with one blended data source. Including the outer join (main) data source, a totally of 5 data sources can be blended.


  1. Click on Resource from the taskbar above.
  2. Choose Manage Blended Data
  3. Click on +Add a data view
  4. Choose data sources up to 4 and add them one by one. Click on Add a table to add new data sources. You can also blend the same data source with itself.
  5. Choose Join keys, at least one is mandatory. Usually, the date is a common metric.
  6. Choose dimensions and metrics which you plan to show on your charts.
  7. Click on Save and then Close.

Blending is a left outer join

In data science terms, a blended data source is the product of a left outer join operation. In a left outer join of table A and table B, the result is all the records of Data source A and those records in Data source B that share the same key values.

The diagram below illustrates a left outer join of data sources A and B. The blended data source includes all the records contained by the green circle.

Blend a data source with itself

You can blend a data source with itself. To do this, add the same data source more than once in the Blend Data panel.

For example, the Google Analytics connector contains metrics for 1-day active users, 7-day active users, and 28-day active users. But, due to a limitation of Analytics, you can only have one of these metrics in a chart at a time. By joining the same Analytics data source with itself, you can add each of these metrics to the blended data source. You can then compare each of these active users' metrics in the same chart.

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