Data engineering moves records from sources to trustworthy, queryable datasets while preserving identity, time, and recovery behavior. Every pipeline has a contract for late data, retries, and schema change.
Choose a starting point
Begin with ingestion and storage formats. Then build transformations, batch and streaming jobs, quality checks, orchestration, and observability around explicit ownership of each dataset.
- Data Engineering Tutorial
- Data source contracts: preserve raw records before transformation
- Event time and late arrivals: close windows with an explicit correction policy
- Schema compatibility and consumer rollout
- DAG intervals and idempotent task outputs
- Shuffle boundaries and local combiners
- Fact grain and measure additivity
- Keyed state, checkpoints and recovery
- Data classification and access boundaries
Common Mistakes
A successful job can still double count events, drop late records, or publish a schema consumers cannot read. The linked sections focus on those boundaries and the cost of repairing them.