In Databricks, you can perform various joins to combine data from tables based on standard columns or conditions. Here’s an overview:

Types of Joins

• INNER JOIN: Returns rows that have matching values in both tables.
• LEFT OUTER JOIN: Returns all rows from the left table and the matched rows from the right table. If there is no match, the result is NULL on the side of the right table.
• RIGHT OUTER JOIN: Returns all rows from the right table and the matched rows from the left table. If there is no match, the result is NULL on the side of the left table.
• FULL OUTER JOIN: Returns all rows when there is a match in the left or right table records.
• LEFT SEMI JOIN Returns only the rows from the left table where there is a match in the right table.
• LEFT ANTI JOIN: Returns only the rows from the left table where there is no match in the right table.

How to Perform Joins in Databricks

You can use SQL or PySpark (DataFrame API) to perform joins in Databricks:

SQL

SQL

SELECT * 

FROM table1 

JOIN table2 ON table1.column = table2.column

PySpark

Python

df1 = spark.table(“table1”)

df2 = spark.table(“table2”)

joined_df = df1.join(df2, df1.column == df2.column, “inner”)

Important Considerations

• Join Conditions: Specify the columns or conditions you want to join the tables using the ON clause in SQL or join expressions in PySpark.
• Join Types: Choose the appropriate join type based on your desired output.
• Performance: For large datasets, consider using optimized join techniques like broadcast joins or shuffle hash joins.