pipeline-design

ETL pipeline design requires building idempotent operations, checkpoints at each stage (extract, stage, transform, load), and recovery mechanisms. Implement dead-letter queues to capture failed records, retry logic with exponential backoff, and state tracking so pipelines resume without reprocessing or data loss.

Batch ETL processes large data volumes on a schedule—source to extract, stage, transform, then load to warehouses like Snowflake or BigQuery. Streaming ETL ingests continuous data via Kafka or Kinesis into a process layer with state stores, then sinks results. Batch suits historical loads; streaming handles real-time requirements.

Reliable pipelines require idempotent transformations, incremental processing to avoid recomputing, comprehensive error handling, and recoverability through checkpoints. Observability comes from metrics and logs at each stage, enabling you to detect failures, trace data lineage, and diagnose issues quickly.

Yes. Pipeline architecture applies across major cloud data warehouses. Design extraction from your source, staging and deduplication in intermediate layers, then load to your target warehouse. SQL patterns for staging deduplication and transform-to-target are warehouse-agnostic with minor syntax adjustments.

ETL pipelines must be testable at each stage—validate extraction volume and schema, verify staging deduplication removes only true duplicates, confirm transformation logic, and check load completeness. Include integration tests that simulate failures to verify recovery and idempotence work end-to-end.

Deduplication happens in the staging layer using SQL patterns that identify and remove duplicates before transformation. Combine this with idempotent operations so re-running the pipeline with overlapping data produces the same warehouse state without accumulating duplicates.