Debezium Blog
The modern data landscape bears little resemblance to the centralized databases and simple ETL processes of the past. Today’s organizations operate in environments characterized by diverse data sources, real-time streaming, microservices architectures, and multi-cloud deployments. What began as straightforward data flows from operational systems to reporting databases has evolved into complex networks of interconnected pipelines, transformations, and dependencies. The shift from ETL to ELT patterns, the adoption of data lakes, and the proliferation of streaming platforms like Apache Kafka have created unprecedented flexibility in data processing. However, this flexibility comes at a cost: understanding how data moves, transforms, and evolves through these systems has become increasingly challenging.
Understanding data lineage
Data lineage is the process of tracking the flow and transformations of data from its origin to its final destination. It essentially maps the "life cycle" of data, showing where it comes from, how it’s changed, and where it ends up within a data pipeline. This includes documenting all transformations, joins, splits, and other manipulations the data undergoes during its journey.
At its core, data lineage answers critical questions: Where did this data originate? What transformations has it undergone? Which downstream systems depend on it? When issues arise, where should teams focus their investigation?
