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 expensive full reloads. It powers incremental pipelines, real-time analytics, and keeping a search index or feature store fresh. The key concerns are handling updates and deletes (not just inserts), ordering, and idempotent application of the change stream. Applied-AI interviews probe it because keeping a RAG index, feature store, or warehouse current 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 Zendesk

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