Many corporations these days face the difficult task of migrating data from old systems to new systems. This typically entails using a proliferation of third-party tools and armies of developers to manually migrate data and code from legacy sources such as Teradata and Netezza to target cloud providers such as Amazon Redshift, Google BigQuery, and Snowflake. Of course, corporations may have decades of code and data stored on their legacy platforms, so migrating this code quickly and securely becomes an extremely large undertaking.
One thing that is often overlooked in this process are the ETLs (Extract, Transform, Load). We’ll explain the ETL process in more detail below, but they are basically the “pipelines” that connect each application to a central data warehouse or data lake. These pipelines allow for data migration, sharing the source database from each tool across the entire system.
In this article, we’re going to give a basic outline of ETLs, explain why they are so often overlooked when planning a data migration project, and also explain how to ensure your legacy ETL pipelines are properly migrated to the new cloud platform: