Migrating all our reports in Looker from Redshift to Snowflake was a multi-quarter project for us at GumGum. We had strategically planned (see previous blog post) this migration in phases to ensure business as usual and cost optimization. Our first phase of migration was the longest where we gave enough time to ourselves to experiment, and learn from the gotcha moments. Right after the first phase of the migration, we realized that we need to build a process for the future migrations.
Data warehouse migration is moving data from one location to another or from one application to another. There could be various reasons for data migration like cost optimization, upgrading technology for performance, and scalability or consolidating data at one location. It is similar in concept to human migration, which is driven by climate, food availability, and other environmental factors.
At GumGum, business is expanding constantly, along with that, our data storage and compute requirements are growing exponentially. To strike a balance between scalability and cost, we have decided to move our reporting data from Redshift to Snowflake. This translated…