Data Engineering

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ETL extracts data, transforms it before loading, and writes the prepared result to a destination. ELT extracts and loads raw data first, then transforms it inside the destination platform. ETL can fit systems that need curated data before storage; ELT often uses scalable warehouses to retain raw data and transform it later. The choice depends on privacy, compute, latency, and governance needs.

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A data pipeline moves data from sources through ingestion and transformation to storage or downstream consumers. A reliable design defines data contracts, validates inputs, makes retries safe through idempotent writes, handles duplicates and late records, and tracks each run. Orchestration manages dependencies; monitoring and alerting expose failures and quality issues. Recovery and backfills should be designed rather than improvised after an outage.