In Tableau, blending and joining in Tableau are the two main methods for merging data from multiple sources, each of which is suitable for different scenarios. Knowing when to blend or join is important for accurate analysis. Joins merge data from tables within the same source using a common field, while blending is used to link data from different sources where relationships are harder to define. Your choice affects the structure of your data and the depth of your analysis.
When to use Joins
Joins work best when you need to merge data from multiple tables within the same source, such as a relational database (SQL, Excel). Joins require a common field to link the datasets.
Scenario
You have a sales dataset in a relational database. One table contains customer details (customer ID, name), and the other stores transactions (order ID, product, amount). You want to combine the two.
Solution
Use an inner join to merge the customer and transaction tables by shared customer ID. This will show only customers with purchases. If you want all customers, including those who did not make a transaction, opt for a left join.
Main scenarios for Joins
Combining data from the same source
When all your data is in one database, joins provide a straightforward way to merge tables using common keys.
Structured relational databases
For logically separate tables (e.g., customer and sales), joins based on common fields are ideal.
Performance optimization
Joins are processed at the database level, making them efficient for large datasets stored in relational systems.
When to use Blends
Blending is ideal when working with data from different sources, such as Excel and Salesforce. Unlike joins, blending sends separate queries to each data source, dynamically combining them during analysis. This is useful when data sources cannot be combined at the database level.
Scenario
You have sales revenue data by region in Excel and sales team performance data in Salesforce. These cannot be combined directly.
Solution
Use blending to link these datasets using a common field, such as “region” or “salesperson”. Excel becomes the primary source, and Salesforce becomes the secondary source. Tableau blends the results during analysis.
Main scenarios for Blending
Different data sources
When working with data from different systems (Excel, SQL, cloud services), blending is the best option.
No common database platform
If data sources do not support direct joins, blending helps combine relevant data.
Blending aggregate and granular data
Blends let you link aggregate data (e.g., sales by region) to granular data (e.g., customer transactions) without restructuring.
On-the-fly linking
Blending is dynamic, done at the time of analysis, making it perfect for ad-hoc analysis.
Importance of choosing the right method
Choosing the right method between joins and blends ensures that your data is merged accurately, avoiding misleading information. Joins provide a robust way to merge data within the same source and are processed efficiently at the database level. Blends are flexible for combining data from different systems, but may not perform as well with large datasets.
Step to distinguish between blending and joining in Tableau
Step 1: Open Tableau
Step 2: Click on Text file to connect with Tableau
Step 3: Browse csv file (or other file format as your need) and click on Open
Steps for Join
Step 4: Data has loaded
Step 5: Right click on customer_data.csv and select Open
Step 6: Drag sales_data.csv and drop at the right side of customer_data.csv; Select Join Type
Step 7: Now come to the sheet; drag-n-drop Transaction Date (sales_data.csv) to Columns and Sales Amount (sales_data.csv) to Rows
Step 8: Right click over Transaction Date (Columns) and select Month
Step 9: Click on dropdown with Automatic and select Bar
Step 10: Drag Region (customer_data.csv) and drop over Color (Marks card)
Note:
- Data from two tables (sales_data and customer_data) is combined at the row level before visualization. Tableau creates a single table by merging data from these sources using a common field (e.g., Customer ID).
Steps for Blend
Step 11: Data (Primary) has loaded
Step 12: Click on Data tab and select New Data Source or Ctrl+D
Step 13: Click on Text File (or you may choose other also)
Step 14: Browse csv file (or whatever file type, you have chosen) and click on Open
Step 15: Secondary data has loaded (customer_data.csv)
Step 16: Now come to the Sheet 1
Step 17: Click on Data tab and select Edit Blend Relationships…
Step 18: Verify that you have chosen correct Primary and Secondary data source
Step 19: Drag-n-drop Customer ID (sales_data) to Columns and Sales Amount (sales_data) to Rows
Step 20: Drag Region (customer_data) and drop over Color (Marks card)
Note:
- Unlike joins, data blending happens at the visualization level. Tableau pulls data from the primary data source (sales_data), and then, depending on the need in the visualization, it queries the secondary data source (customer_data), joining them by customer ID.
Conclusion
Deciding between joins and blends depends on your data. Joins are best when merging tables from the same source, providing speed and efficiency for structured datasets. Blends are essential for dealing with multiple data sources that cannot be combined directly. They provide flexibility, allowing you to dynamically combine data from different systems.
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