Data Engineering / Intermediate: change, time and coordination
Data Engineering โ Intermediate: change, time and coordination
Coordinate increments, CDC, late data, layout, tasks and quality evidence.
What you will learn
- Design complete cursors, versioned state and bounded retries.
- Connect accepted data to lineage and logical task intervals.
Incremental cursors and checkpoints
Extract bounded changes without skipping equal timestamps or advancing progress before accepted effects. Define the cursorโs source semantics and recovery boundary.
Start learningChange data capture and versioned state
Apply updates and deletes according to source order rather than arrival order. Preserve enough version history to prevent stale events from resurrecting deleted records.
Start learningEvent time, windows and late data
Separate event occurrence from processing progress and choose explicit correction/finalization rules. Treat watermarks as a source-informed completeness mechanism, not a universal wall clock.
Start learningPartitions, file layout and compaction
Align dataset layout with legitimate access and correction patterns. Distinguish logical partitions, file statistics and compaction from guaranteed performance or atomic publication.
Start learningOrchestration, dependencies and retries
Model task ordering and retry state without treating a scheduler as an atomic database. Bind each attempt to an identified data interval and accepted artifacts.
Start learningData quality, rejects and lineage
Turn quality rules into scoped evidence and retain enough lineage to explain a published result. Distinguish absence of detected failures from proof of complete correctness.
Start learning