Data Blending in Tableau | A Complete Guide with Best Practices | Free Guide Tutorial [ OverView ]

• In this article you will learn:
• 1.What is a Data Blending in Tableau?
• 2.Example of a Data Blending in Tableau.
• 3.Joins v/s Data Blending in Tableau.
• 4.Benefits of a Data Blending in Tableau.
• 5.Limitations of a Data Blending in Tableau.
• 6.Next Steps.
• 7.Conclusion.

What is Data Blending in Tableau?

Data blending in Tableau is a technique for combining data from multiple sources and displaying it as a whole on a single screen.For example consider the scenario where a business analyst needs to work with a sales data. Now let us imagine customer data is stored at a Oracle Datamultiple base and the order details are be stored in the SQL Server.In such situations procedure of a Data Blending comes in handy. Business analysts can combine a data from an Oracle Database and SQL Server treat it as whole and an extract business insights.The next section will combine a data from two various Excel Workbooks using Data Blending in a Tableau for better learning experience.

Example of Data Blending in a Tableau:

In a following example consider two various datasets namely:

• Car Data Set
• Bike Data Set

Herean objective is to try to import these are two datasets and combine them to be implement Data Blending in a Tableau.So a first step is to import a first dataset .Since a file type here is Excel choose a Microsoft Excel.Next have to choose a data option from toolbar and import a second dataset.

The second dataset is a selected now:

• Now both datasets are visible on a tableau window.
• Next Tableau automatically generates a blend between data.
• Also an option of choosing a custom blend is available as well. The two datasets have a similarities between the Zone and Region columns. So here can must perform a Custom Data Blending between two columns as shown below.
• Selecting a Custom option will open a new window. This new window will offer with columns from the both datasets. Following that select the common columns and apply a Custom Blend.
• Next step is to create a some visualizations using a data available.
• First create the visualization that explains cars’ annual sales in various regions of a country. Here in this visualization a Car Data Set acts as a Primary Data Source.
• The highest horsepower among cars in a dataset should be calculated in a subsequent visualisation.
• According to results Dodge Viper is the one with highest engine Horsepower.
• Next need to find out a fuel efficiency of the various bikes available. Now, must use a data obtained from Bikes Data Set. In the current situation Tableau will change the Bikes Data Set to Primary Source and the Car Data Set to Secondary Data Source.
• In short Tableau will be automatically convert a currently active dataset as a Primary Data Source.
• Next will need to find out annual sales for a bikes and cars together.
• Now as it is evident there are the null values in a second graph. To eliminate these null spaces, an agenda is to create new calculated field and write a formula below.
• Moving on write in a formula below and select a OK button in a right bottom corner.

Formula:

Now can see the newly created calculated field in a measures section. Drag that into a columns section, and and can instantly see the newly created visualization with the combined salesNow that have executed an example based on a Data Blending might have a question in mind already have a Table Joins; what makes a Data Blending in a Tableau Stand out?The answer to this question is in a next sub heading where will explore the major differences between the Joins and Data Blending in a Tableau.

Joins v/s Data Blending in Tableau:

The following table are explains the differences between the joins and data blending in Tableau:

Data Blending in a Tableau	Joins
Data Blending Aggregates a data and then combines it.	Joins combine a data and then aggregates it.
Data Blending can combine a data from various sources.	Joins can combine a data from a same sources only.
Data Blending can be execute a Left-Join only.	Joins can execute all the four varieties of a Joins.
Data Blending provides a data availability at various levels of granularity.	Data has to be maintained at a one single level granularity throughout a process while using a Joins.
Data Blending in a tableau can execute queries to separate datasets, aggregate data, and then perform a data blending.	Joins can only perform a join operations at a row level.

Benefits of a Data Blending in Tableau:

Following are the list of benefits of using a Data Blending in Tableau:

• Data Blending in a Tableau provides a best in class solutions for the multiple data granularity issues.
• The Data Collocation problems are resolved by using a Data Blending in Tableau.
• The Data Blending Tableau is be capable enough to adapt and satisfy a needs of an Exploratory Visual Analytics.
• Moving ahead will learn the Limitations of using a Data Blending in Tableau.

Limitations of a Data Blending in Tableau:

• Data Blending compromises a query’s execution speed in a high granularity.
• Cube data sources are used as a primary data sources to blend data in a Tableau and cannot be used as a secondary data sources.
• During Data Blending in a Tableau if a secondary data source has any LOD (Level of Detail) Expressions they are taken down after a data blending process is be finished.
• The SQL server data is the temporary data source The tableau server will not be support data blending and non-additive aggregates while using a previously published data source as a primary data source.

Next Steps:

• Parameters in a Tableau can be a next stop.
• Parameters in Tableau will help to create a own data manipulation operations and apply them to the entire workbook.
• To move forward the link to the next stage is here Parameters in a Tableau.

Conclusion:

Data Blending in a Tableau enables users to add the secondary data source to the primary data source and also displays them together.