Jupyter Notebook ETL Saves Day, Becomes Unmaintainable Monolith By Lunchtime

A recent data migration project involved transferring tens of thousands of records, highlighting the complexities of migrating data between services. The process, known as ETL, or extract, transform, and load, requires careful planning and analysis to ensure a smooth transition. The decision to migrate data can come from either the product or engineering teams, with the engineering team ultimately responsible for assessing the technical viability of the migration. Factors such as data volume, structure, and complexity must be considered, as well as the potential impact on the product and customers. In some cases, a simple solution such as a Jupyter Notebook may suffice, while more complex migrations may require a dedicated application. The migration process must also account for potential errors and inconsistencies in the data, with a clear strategy for rollback and testing. Effective data migration requires a thorough understanding of the data and the systems involved, as well as careful planning and execution to minimize disruptions and ensure a successful outcome.