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

SQLeasy0.3 min read

**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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Frequency
Low
Asked at 1 company
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Snowflake
Key Concepts Tested
etlsnowflake

Why This Question Matters

This easy-level SQL question appears frequently in data engineering interviews at companies like Snowflake. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl, snowflake) will help you answer variations of this question confidently.

How to Approach This

Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.

Expert Answer
68 words

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

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