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Building ETL pipelines to capture changes when new records are inserted into source tables?

SQLeasy0.3 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

**Patterns:** (1) Incremental: watermark (max id/updated_at), query WHERE id > watermark. (2) CDC: Debezium, AWS DMS—log-based, low latency. (3) Hash/checksum: compare batches. (4) MERGE: upsert by key. **Snowflake:** Stage + MERGE. Store last watermark in state table. Use...

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Patterns: (1) Incremental: watermark (max id/updated_at), query WHERE id > watermark. (2) CDC: Debezium, AWS DMS—log-based, low latency. (3) Hash/checksum: compare batches. (4) MERGE: upsert by key.

Snowflake: Stage + MERGE. Store last watermark in state table. Use event time, not process time, for late arrivals. Idempotent design: rerun same date = same result.

SELECT * FROM source WHERE updated_at > :last_run
-- Store last_run after successful load

⚡
Pro Tip

Red Flag: Full refresh for incremental use case. Pro-Move: 'We use watermark table + MERGE; CDC for real-time; batch for history.'

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