When to use Blending Vs When to use Joins in Tableau?
Data Blending -When to Blend & When to join
Tableau offers powerful ways to combine data from different sources, and two key methods are cross-DB joins and data blending. While both allow you to integrate data, they differ significantly in their approach and have distinct use cases. This blog post will delve into the intricacies of each method, helping you understand when to use which, and why data blending remains relevant even with the availability of cross-DB joins.
Understanding Joins
Before we dive into Tableau specifics, let’s recap what database joins are. In SQL, a join clause combines data from multiple tables based on common columns. Various types of joins exist, each serving a different purpose:
- Inner Join: Returns rows only when there’s a match in both tables.
- Left Join: Returns all rows from the left table and matching rows from the right.
- Right Join: Returns all rows from the right table and matching rows from the left.
- Full Outer Join: Returns all rows from both tables, regardless of matches.
And a few more specialized types. Understanding these foundational concepts is crucial when working with data in Tableau.
Data Blending in Tableau
Data blending in Tableau is a powerful feature that allows you to combine data from multiple sources directly within a visualization at the sheet level. This is particularly useful when your data resides in different databases or formats. Blending happens on the fly, linking data sources based on common fields.
Data blending offers flexibility when combining disparate datasets, making it a handy tool for creating rich visualizations without heavy data integration processes.
Aggregation-Based Combination: Tableau aggregates data from each source first, then combines it based on shared dimensions. This method prevents the duplication issues often seen with cross-database joins.
Cross-DB Joins vs. Blending: When to Use Each
While both methods combine data, they have distinct applications:
Cross-DB Joins:
- Ideal when your data sources are relational databases that Tableau supports for cross-database joins.
- Efficient for combining data at the same level of granularity.
- Offers more flexibility in terms of join types and calculations.
Data Blending:
- Essential when dealing with data sources that don’t support cross-DB joins, such as cubes or Salesforce.com.
- Crucial when combining data with different levels of detail (e.g., city-level data with state-level data).
- Can be more efficient than joins for very large datasets.
