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Joins in Tableau

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Joins in Tableau are essential for merging data from multiple tables into a single dataset, giving you a more complete view for analysis. Data in relational databases is often spread across different tables serving different purposes, and combining this data is crucial for integrated analysis. Tableau offers several types of joins, allowing you to connect data based on your specific needs. By understanding how these joins work, you can improve your data visualization and gain deeper insights from your combined datasets.

Types of Joins in Tableau

Inner Join

Returns only records with matching values ​​in both tables based on the join key. It excludes any rows without a match in the second table.

Use Case– When you only want to analyze data common to both tables, such as customers who made purchases and also filed complaints.

Left Join

Returns all records from the left table (primary table) and matching records from the right table. If there is no match, it fills in with null values ​​for missing records from the right table.

Use case

When you want to focus on the left table, such as analyzing all products and matching their sales, even if some products have no sales data.

Right join

Similar to left join, but this time keeping all records from the right table and matching them to the left table. Missing values ​​from the left will be null.

Use case

When you want to include all sales records, even if some products do not have corresponding information.

Full outer join

Returns all records where there is a match in either table. If there is no match, it includes null values ​​for missing fields.

Use case

When you want to see all data from both tables, such as combining customer data and sales records, even if some customers did not make a purchase or some sales lack customer information.

When to Use Joins in Tableau

Merging data from different sources

If your data is spread across tables in a relational database, joins help bring them together based on common fields like “order ID” or “customer ID.”

Creating complete datasets

When you need to enrich your main dataset with additional information from another table, joins make your data better suited for in-depth analysis.

Analyzing relationships

Use joins to identify relationships like understanding the connection between sales transactions and customer demographics.

Handling missing data

Choose different types of joins based on whether you need complete records or want to include missing data for your analysis.

Importance of Joins in Tableau

Data consolidation

Joins help bring data from multiple tables into a single view, allowing you to identify patterns that might be missed if the data is disjoint.

Advanced data analysis

By merging datasets, you can create richer visualizations and reveal deeper insights than you could with just a single data source.

Flexibility in data management

Tableau’s join types let you include or exclude specific records based on your analysis goals.

Efficiency

Joins save time by allowing you to combine tables directly in Tableau, eliminating the need to merge them beforehand. This makes analysis faster and more dynamic, especially with large datasets.

Better decision making

Combined data gives a complete picture of business performance, allowing better-informed decisions to be made by combining key metrics from different departments.

Steps to perform Joins 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

Step 4: Data has loaded

Inner Join

Step 5: Right click on Employee.csv and select Open

Step 6: Drag Department.csv and drop at the right side of Employee.csv

Step 7: Click on Join Icon (in between Employee.csv and Department.csv) and select Inner

Note:

  • If you have a column with the same name (Department ID) in both datasets, it will automatically join using that specific column from both.
  • If you don’t do this, you will need to specify the columns from both datasets (as shown in the join below)

Step 8: Now come to the sheet; drag-n-drop Employee ID (Employee.csv) to the Rows and Department ID (Department.csv) to the Labels (Marks Card)

Note:

  • Employee IDs (E008 and E010) are assigned to Department ID (D999) which does not exist in Department.csv. So they are excluded from the table.

Left Join

Step 9: Right click on Employee.csv and select Open

Step 10: Click on Join Icon (in between Employee.csv and Department.csv) and select Left

Step 11: Now come to the sheet; drag-n-drop Employee ID (Employee.csv) to the Rows and Department ID (Department.csv) to the Labels (Marks Card)

Note:

  • For employees E008 and E010, the Department fields will appear as NULL because D999 does not exist in Department_Data.csv

Right Join

Step 12: Right click on Employee.csv and select Open

Step 13: Click on Join Icon (in between Employee.csv and Department.csv) and select Right

Step 14: Now come to the sheet; drag-n-drop Employee ID (Employee.csv) to the Rows and Department ID (Department.csv) to the Labels (Marks Card)

Note:

  • Employees that do not match any departments (such as E008 and E010) will not be displayed.

Full Outer Join

Step 15: Right click on Employee.csv and select Open

Step 16: Click on Join Icon (in between Employee.csv and Department.csv) and select Full Outer

Step 17: Now come to the sheet; drag-n-drop Employee ID (Employee.csv) to the Rows and Department ID (Department.csv) to the Labels (Marks Card)

Note:

  • All employees and all departments will be included.
  • Employees E008 and E010 will appear with NULL values ​​for the Department field.

Conclusion

Joins in Tableau are crucial for merging datasets, enhancing analysis, and revealing deeper information. By providing flexibility with different join types (inner, left, right, and full outer), Tableau lets you customize data merging to suit your business needs. When used effectively, joins can consolidate data, support better decision making, and enable more comprehensive analysis. Incorporating joins into your workflow helps you create detailed and informative visualizations that fully capture the context of your data, enabling your organization to make data-driven decisions more effectively.

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