Data Blending in Tableau: Definition and Key Concepts

by | Jan 8, 2019 | Data Analytics

8 Min Read. |

What is exactly Data Blending by Tableau?

Are you also looking for an answer to the same question?


Well! keep reading to get the answer.

Data Blending in Tableau is a powerful feature of Tableau that has gained immense popularity in the last few years. It is the process of combining data from various sources.

Being the undisputed leader in the BI space, Tableau has grown its customer base from 11,000 in 2012 to over 70,000 in 2017, a compounded annual rate of almost 45% over the last five years.

A couple of years ago, Tableau decided to move from a perpetual licensing model to a subscription-based licensing model for its offerings.

In 1Q’17, the company’s ratable license bookings as a percentage of total license bookings stood at merely 26%. However, with the increased demand for cloud-based products and Tableau’s consistent efforts, its ratable license bookings rate had improved to 67% at the end of 2Q’18.

What is Data Blending in Tableau: Definition

What is Data Blending in Tableau?

Data Blending in Tableau

Data Blending in Tableau

Data Blending in Tableau may be defined as the operation of combining multiple data sources into the same view by finding common fields between them to join on. Data Blending, unlike other cases of joining, is capable of joining data sources after aggregation is performed on the individual sources.

As a consequence, this limits the number of records that are joined together and maximizing computational efficiency.

Data Blending in Tableau gives you the option of blending and joining the data sources. However, blending in the tableau and joining in Tableau are different. We will discuss that in detail. Our discussion will include an elaborate explanation of Data Blending in Tableau with examples.

A classic example of Data Blending in Tableau with examples can be of sales information that is available in a social database and sales target information in an excel spreadsheet. The two sources engaged with information mixing have alluded as primary and auxiliary information sources.

A left join is made between the essential information source and the auxiliary information source with every one of the information columns from essential and coordinating information lines from an optional information source.

See video on data blending in Tableau with an example.


Data Blending does not create row-level joins and is not a way to add new dimensions or rows to your data. You should use Data blending when you have related data in multiple data sources that you want to analyze together in a single view. If you want to integrate data, you must first add one of the common dimensions from the primary data source to the view.

For example, when blending Actual and Target sales data, the two data sources may have a Date field in common. The Date field must be used on the sheet. When you switch to the secondary data source in the Data window, Tableau automatically links fields that have the same name. If they are of the same name, you can define a custom relationship that creates the correct mapping between fields.

For each data source that is used on the sheet, a query is sent to the database and the results are processed. Then all the results are left joined on the common dimensions. The join is done on the member aliases of the common dimensions so if the underlying values aren’t an exact match, you can fix it up in Tableau.

Data Blending in Tableau

Data Blending in Tableau: Introduction

In general, a good test is required to see whether data can be integrated smoothly is to drag the dimensions from the primary data source into a text table on one sheet. Then on another sheet, drag the same fields from the secondary data source into a text table. If the two tables match up then the data is most likely going to blend correctly in Tableau.

Why Data Blending Required in Tableau?

Data Blending in Tableau becomes imperative in certain situations. Let us analyze how and when.

 When your Data Needs Cleaning?

Data comes from multiple sources and requires to sifted or processed for use. If you find that tables do not match up with each other correctly after a join, you can set up data sources for each table, and make any necessary customizations (such as rename columns, change column data types, create groups, use calculations, etc.). Thereafter, you may have to use data blending to combine the data. We will explain this by describing data blending in Tableau with an example.

(i) Joins Cause Duplicate Data

This is pretty common. You may find duplicate data after a join. It is a symptom of data at different levels of detail. If you notice the duplicate data, then you might find that instead of creating a join, data blending is a better option to blend on a common dimension instead.

(ii) Data Load is High

Joins are mainly recommended for combining data from the same database. Joins are handled by the database, which allows joins to leverage some of the database’s native capabilities. However, if you are working with large sets of data, joins can put a strain on the database and significantly affect performance.

In this case, data blending might help. Tableau handles combining the data after the data is aggregated; there are fewer data to combine. When there are fewer data to combine, generally, performance is better.

Data Blending in Tableau

Data Blending in Tableau: When Data Needs Cleaning

How to Create Data Blending

For data blending in Tableau, you must understand Primary and secondary data sources, first. Data blending requires a primary data source and at least one secondary data source. When you designate a primary data source, it serves as the main table or main data source.

Any subsequent data sources that you use on the sheet may be treated as a secondary data source. Only columns from the secondary data source that have corresponding matches in the primary data source appear in the view.

Alluding to the same example from above, you may designate the transactional data as the primary data source and the quota data as the secondary data source.

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After designating primary and secondary data sources, you must define the common dimension or dimensions between the two data sources. This common dimension is called the linking field. When you blend transactional and quota data, the date field might be the linking field between the primary and secondary data sources.

If the date field in the primary and secondary data sources have the same name, Tableau creates the relationship between the two fields and shows a link icon () next to the date field in the secondary data source when the field is in the view.

If the two dimensions don’t have the same name, you can define a relationship that creates the correct mapping between the date fields in the primary and secondary data sources.

Data Blending in Tableau

Create Data Blending in Tableau

What is the Basic Difference Between Joins & Data Blending?

Even though joining and blending are quite similar, there are differences between the two.


Depending on our requirements we have the option of changing the joining ”Left join”, ”Right join”, “Inner join”, “Full Outer Join”. Say, for example, you can use a left join to combine data; this query is sent to the database where it can be performed. By using left join return all rows present in the left table and if any other row present in the right table that has a corresponding match in their left table.

Data Blending

You may, for example, use a data blending to mix the data; this query is sent to the database for each and every data source used in the sheet. The views use the aggregated row from the secondary data source, the right table, and all rows from the primary data source, left table, based on the dimensions of the linking field.

Market Prospects

Are you passionate about Data blending? Does a career in data blending excite you?  You may start your career as a data scientist or ETL developer and then with some years of experience take a leap further to Data Visualization.

Data Blending in Tableau is very much in demand among enterprise as one of the top Business Intelligence tools. In order to further capitalize on this demand, Tableau has been expanding its product offering. In April of this year, the company launched three new subscription offerings – Tableau Creator, Explorer, and Viewer.

These are aimed at making Tableau’s capabilities easy to scale and customize and are receiving positive feedback from the company’s clients. Consequently, we expect its customer base to grow to over 82,000 by the end of 2018, which will drive its top-line growth as well as valuation.

Data Blending in Tableau

Data Blending in Tableau: Market Prospects

The success of Business Intelligence projects depends largely upon organizations’ choice of BI solutions and vendors’ ability to deliver successful and relatively rapid implementations, along with supportive follow-up and maintenance.

Therefore, choosing the right Business Intelligence tool for your business is the primary condition for ensuring better business values. With a ready list of Business Intelligence tools, you may shortlist the best one for your business, however, big, or small.

Many data analysts today are switching to business analytics tools, for more flexible solutions. Try Digital Vidya Data Analytics courses for a better understanding of Data Analytics in business processes.

Digital Vidya’s industry-relevant curriculum, pragmatic market-ready approach, hands-on Capstone Project is some of the best reasons for choosing Digital Vidya.

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  1. Yoel Garber

    This is such a great post with a valuable source.

    • Niharika Mahendra

      Hi Yoel,

      Thanks for the appreciation.


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