Core

Change Data Capture

Change Data Capture (CDC) streams the inserts, updates, and deletes from a source database so downstream systems stay in sync without costly full reloads. It drives incremental pipelines, real-time analytics, and keeping a search index or feature store current. The main concerns are handling updates and deletes (not just inserts), ordering, and applying the change stream idempotently. AI, ML, and GenAI interviews probe it because keeping a RAG index, feature store, or warehouse fresh is a constant need, and full reloads do not scale.

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RELATED CONCEPTS

Idempotent Data Pipelines Data Quality and Contracts The RAG Pipeline Feature Stores and Training-Serving Skew

PRACTICE THIS IN REAL QUESTIONS

SQL & Data EngineeringHow do you keep an analytics warehouse in sync with a source database using change data capture?→SQL & Data EngineeringCompare CDC variants: log-based, query-based, and trigger-based. What are the failure modes of each?→SQL & Data EngineeringImplement Slowly Changing Dimension Type 2 history tracking in a Delta lakehouse.→SQL & Data EngineeringGroup a stream of user events into sessions in SQL (30-minute inactivity gap) using window functions.→SQL & Data EngineeringFind the top-N records per group and a running total per group in SQL.→SQL & Data EngineeringDeduplicate events exactly-once over a sliding 7-day window in a high-throughput stream without running out of memory.→

COMPANIES THAT ASSUME THIS

Scale AI Palantir Databricks Snowflake

NEXT IN DATA & SQL ENGINEERINGDeduplication (Exact and Fuzzy)→