Delete and Vacuum vs. Deep Copy in Snowplow: Optimizing Redshift Storage

Snowplow users dealing with large datasets in Amazon Redshift often weigh the pros and cons of the Delete and Vacuum approach against the more efficient Deep Copy method for data retention and storage cost management. The Delete and Vacuum method involves unloading older data to S3, deleting it from Redshift, and reclaiming disk space through vacuuming, which can be time-consuming and disruptive due to high disk usage and potential impacts on query performance. In contrast, the Deep Copy approach creates a new version of the table with only the desired data, bypassing the need for vacuuming and minimizing runtime, which makes it faster and more scalable for large datasets. Automating Deep Copy involves using a SQL Runner playbook and requires considerations such as dropping primary key constraints and ensuring sufficient disk space. While the Delete and Vacuum method is suitable for smaller datasets, Deep Copy significantly improves data management efficiency for large Snowplow data tables by optimizing runtime, minimizing disk usage, and maintaining data integrity.