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Amazon Redshift supports five types of joins, namely Inner join, Left outer join, Right outer join, Full outer join, and Cross join. Each join type serves a unique purpose and is used based on the specific requirements of the data analysis task at hand.
In Redshift, the Inner join is used to combine rows from two or more tables based on a related column between them. The syntax for using Inner join is: SELECT column_name(s) FROM table1 INNER JOIN table2 ON table1.column_name = table2.column_name.
SELECT name_of_table1.column_name1, name_of_table2.column_name2 FROM table1 INNER JOIN table2 ON table1.column_name1 = table2.column_name2;
The Left outer join in Redshift is used to return all the records from the left table and the matched records from the right table. If there is no match, the result is NULL on the right side.
SELECT c.state_residence, o.item_name, w.location FROM customers c LEFT OUTER JOIN orders o ON o.customer_id = c.id LEFT OUTER JOIN warehouses w ON w.id = o.warehouse_id;
The Right outer join in Redshift is used to return all the records from the right table and the matched records from the left table. If there is no match, the result is NULL on the left side.
SELECT name_of_table1.column_name1, name_of_table2.column_name2 FROM table1 RIGHT OUTER JOIN table2 ON table1.column_name1 = table2.column_name2;
The Full outer join in Redshift returns all records when there is a match in either the left table or the right table. If there is no match, the result is NULL on both sides.
SELECT name_of_table1.column_name1, name_of_table2.column_name2 FROM table1 FULL OUTER JOIN table2 ON table1.column_name1 = table2.column_name2;
The Cross join in Redshift is used to combine each row from two tables where each row from the first table is combined with each row from the second table. It is useful for data analysis as it allows you to quickly and easily combine data from multiple sources.
SELECT * FROM table1 CROSS JOIN table2 ON table1.column1 = table2.column2;
Secoda's no-code integration with Redshift simplifies the process of setting up a data dictionary by eliminating the need to manually write SQL code. It can read the metadata of Redshift tables and columns, and set up data governance on Redshift. This allows users to get started in minutes, with no code or engineering required.