Data Blending in Tableau enables users to add a secondary data source to the primary data source and displays them together.
What is Data Blending in Tableau?
Data Blending in Tableau is an approach to combine data from multiple varieties of sources and display them as a whole on one single screen.
For example, consider a scenario where a business analyst needs to work with sales data. Now, let us imagine, the customer data is stored at the Oracle Database, and the order details are stored in a SQL Server.
In such situations, the procedure of Data Blending comes in handy. Business analysts can combine the data from Oracle Database and SQL Server, treat it as a whole and extract business insights.
The next section will combine data from two different Excel Workbooks using Data Blending in Tableau for a better learning experience.
Example of Data Blending in Tableau
In the following example, consider two different datasets, namely.
Car Data Set
Bike Data Set
Here, the objective is to try to import these two datasets and combine them to implement Data Blending in Tableau.
So, the first step is to import the first dataset as shown below.
Since the file type here is Excel, choose Microsoft Excel.
Next, you have to choose the data option from the toolbar and import the second dataset.
The second dataset is selected now.
Now, both the datasets are visible on the tableau window.
Next, Tableau automatically generates the blend between the data, as shown below.
Also, the option of choosing the custom blend is available, as well. The two datasets have similarities between the Zone and Region columns. So, here you must perform the Custom Data Blending between the two columns as shown below.
Selecting the Custom option will open a new window. This new window will provide you with columns from both datasets. Next, you need to select the common columns and apply the Custom Blend as shown below.
The next step is to create some visualizations using the data available.
First, create a visualization that explains cars’ annual sales in different regions of a country. Here in this visualization, the Car Data Set acts as the Primary Data Source. The visualization looks like this, as shown below.
In the next visualization, you should calculate the highest horsepower amongst the cars available in the dataset. The visualizations look as follows.
According to the results, Dodge Viper is the one with the highest engine Horsepower.
Next, you need to find out the fuel efficiency of the different bikes available. Now, you must use the data obtained from Bikes Data Set. Tableau will change Bikes Data Set to Primary Source and convert the Car Data Set to Secondary Data Source in the current situation.
In short, Tableau will automatically convert the currently active dataset as a Primary Data Source.
The visualization for the Fuel Efficiency of bikes is shown below.
Next, you will need to find out the annual sales for bikes and cars together. The combined result will look as shown below.
Now, as it is evident there are null values in the second graph. To eliminate these null spaces, the agenda is to create a new calculated field and write the formula below.
Moving on, write in the formula below and select the OK button in the right bottom corner.
//Formula:
ZN(SUM([Annual_Sales])) + ZN(SUM([Car_sales (Sales-Car)].[Annual_Sales]))
Now, you can see the newly created calculated field in the measures section. Drag that into the columns section, and you can instantly see the newly created visualization with combined sales.
Now that you have executed an example based on Data Blending, you might have a question in mind, “We already have Table Joins; what makes Data Blending in Tableau Stand out?”
The answer to this question is in the next subheading, where you will explore the major differences between Joins and Data Blending in Tableau.
Joins v/s Data Blending in Tableau
The following table describes the differences between joins and data blending in Tableau.
|
Data Blending in Tableau |
Joins |
|
Data Blending Aggregates the data and then combines it. |
Joins combine the data and then aggregates it. |
|
Data Blending can combine data from different sources. |
Joins can combine data from the same sources only. |
|
Data Blending can execute Left-Join only. |
Joins can execute all four varieties of Joins. |
|
Data Blending offers data availability at different levels of granularity. |
Data has to be maintained at one single level granularity throughout the process while using Joins. |
|
Data Blending in the tableau can execute queries to the separate datasets, aggregate data, and then perform data blending. |
Joins can only perform the join operations at row level. |
Up next, you will learn the benefits of using Data Blending in Tableau
Benefits of Data Blending in Tableau
Following are a list of benefits of using Data Blending in Tableau
- Data Blending in Tableau provides the best in class solutions for multiple data granularity issues.
- The Data Collocation problems are resolved by using Data Blending in Tableau.
- The Data Blending Tableau is capable enough to adapt and satisfy the needs of Exploratory Visual Analytics.
Moving ahead, you will learn the Limitations of using Data Blending in Tableau.
Limitations of Data Blending in Tableau
Following are a list of few limitations of using Data Blending in Tableau
- Data Blending compromises the query’s execution speed in high granularity.
- Cube data sources are used as primary data sources to blend data in Tableau and cannot be used as secondary data sources.
- During Data Blending in Tableau, if the secondary data source has any LOD (Level of Detail) Expressions, they are taken down after the data blending process is finished.
- The SQL server data is a temporary data source; The tableau server will not support data blending and non-additive aggregates while using a previously published data source as the primary data source.
With that, you have reached the end of this “What Is Data Blending in Tableau and How to Implement It?” article.
Next Steps
Parameters in Tableau can be your next stop. Parameters in Tableau will help you create your own data manipulation operations and apply them to the entire workbook.
To move forward, the link to your next stage is here, Parameters in Tableau.
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