While building dashboards, we must be cautious with the data that we are using. If data is incorrect, the whole objective fails. There will be times when we have to consume different data sets either from the same source or a different one. That’s where Data Joining and Data Blending concept plays a key role. How do we determine when to use Blending v/s Joining?

In this blog, we will look at the usage of these two in Tableau:

Data Joining

  1. It is primarily used when you have to merge data sets from the same source. For example, you want to look at Student details where his grades are coming from one source and personal information from another source. Here, the key will be the student roll number which will be unique.

There are different types of join;

1. Left join

Merging contents between 2 tables and also retaining left table details.

Tableau – Data Blending vs Data Joining

2. Right Join

Merging contents between 2 tables and also retaining right table details.

Tableau – Data Blending vs Data Joining

3. Inner join

Only the common matching data between 2 tables are displayed.

Tableau – Data Blending vs Data Joining

4. Full Outer Join

Result set from both the tables are merged and displayed.

Tableau – Data Blending vs Data Joining

Data Blending

Data Blending sounds similar to Join but it is not the same. Let us look at its usage:

1.  It should be used when you want to analyze data available in different sources.

  • For example, Actual Sales for geography like Region Name can come from one source (relational) and Month details from a different source (Excel File).
  • In case you wish to visualize monthly sales for a region, you will be using Data Blending.

2. Ensure that there is a common field called “Linking field” while performing Data Blending.

3. Data Blending performs like a left join operation and does not accept or perform any other type of join.

4. Since data is coming from different sources, it is not mandatory that both data sets have the same level of detail. Data blending operation is independent of granularity.

Let us quickly summarize what we just learnt:

S. No.Data JoiningData Blending
1Used when the data set is from the same sourceUsed when the dataset is from a different data source
2Ability to use different types of join (left join, right join, inner join and full outer join)Uses only left join.
3Data has to be maintained at the same level of granularityData can be available in different level of granularity.
4Joins data at a row-levelSends separate query to each dataset, aggregates and then performs blending

Data Joining & Blending have their own importance and should be wisely chosen based on the requirement. It is also possible to join tables from different data sources using “Cross-Database Joins” and will be covered in the sequel blog.

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