Data Engineering Best Practices
4 min read Quiz at the end
Idempotency, incremental loads, immutable raw layer, data quality gates, lineage — production data engineering.
Data Engineering Best Practices
- Idempotency -- pipelines can run multiple times without duplicating data; use MERGE or DELETE+INSERT
- Backfill support -- pipelines accept date parameters so historical data can be reprocessed
- Incremental loading -- process only new/changed data; full reload daily is not scalable
- Immutable raw data -- never delete or modify Bronze/raw layer; reprocess from raw on errors
- Data lineage -- track source->transform->consumption; essential for impact analysis
- Schema evolution -- handle new columns gracefully without breaking downstream consumers
- Data quality gates -- validate data before publishing to next layer; fail fast on quality issues
- Partition by date -- always partition time-series tables for efficient time-range queries
- Monitor freshness and volume -- alert when data is late or row counts deviate from baseline
- Version control everything -- DAGs, SQL models, schemas in Git; treat data code like software code
Topic Quiz · 1 questions
Test your understanding before moving on
1. What does idempotency mean for a data pipeline?
💡 Idempotent pipelines can safely retry without creating duplicate records — use MERGE or truncate+insert.