What is a difference between joiner and lookup transformation?

Both joiner and lookup transformations are used in data integration and ETL (Extract, Transform, Load) processes in data warehousing. However, some of the major key differences are as follows:

Functionality: The joiner transformation is used to combine data from two or more sources based on a common key. It performs a join operation similar to what is done in SQL. The lookup transformation, on the other hand, is used to look up a value from a reference table or file based on a key value in the input data.

Performance: Joiner transformations can be more resource-intensive, especially when dealing with large datasets. This is because they require sorting and merging of data from multiple sources. Lookup transformations, on the other hand, can be more efficient as they typically involve only a single table or file lookup.

Usage: Joiner transformations are typically used when you need to combine data from multiple sources into a single dataset. This might be necessary, for example, when you need to combine sales data from a POS (Point of Sale) system with customer data from a CRM (Customer Relationship Management) system. Lookup transformations, on the other hand, are useful when you need to enrich or supplement data with additional information from a reference table or file. For example, you might use a lookup transformation to add product descriptions or prices to an existing sales dataset.

In summary, while both joiner and lookup transformations are used to integrate data in ETL processes, the main difference lies in their functionality and usage.