Best Practices to Design a Data Ingestion Pipeline

Data ingestion is a crucial step in the ETL/ELT process, as it connects tools and databases to data warehouses. Following best practices from the start ensures high-quality data for transformations and analyses. These best practices include choosing an ingestion tool, documenting sources, orchestration, testing, and monitoring. Documenting best practices forces a set structure, preventing sloppy work and ensuring consistency across the team. Comparing data ingestion tools using a scorecard with must-have's, nice-to-have's, and dealbreakers helps in deciding on the right tool for the team. Keeping a record of data sources and their connectors is essential to avoid confusion about raw data origins. Maintaining a separate database for raw data ensures its protection and serves as a backup for accidental deletions or modifications. Running syncs and models synchronously ensures accurate validation of data and allows for more precise testing. Creating alerting at the data source level helps in identifying issues early on, making them easier to fix. Following these best practices from the beginning stages of a data stack sets the team up for success and prevents future problems.