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CDC During Migration - explain approaches for real-time Change Data Capture

System Design/Architectureeasy0.5 min read

CDC captures inserts, updates, and deletes from a source and applies them to a target in near real-time, enabling minimal-downtime migrations. **Approaches**: Log-based CDC (Debezium, AWS DMS)—reads WAL/redo logs; lowest latency, no schema change. Trigger-based—triggers on...

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Frequency
Low
Asked at 2 companies
Category
179
questions in System Design/Architecture
Difficulty Split
15E|6M|158H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
MoonfareSnowflake
Interview Pro Tip

Red Flag: Claiming trigger-based CDC is 'real-time' without acknowledging write amplification. Pro-Move: Mention handling schema evolution (e.g., Debezium SMT) and idempotent writes to avoid duplicates during retries.

Why This Question Matters

This easy-level System Design/Architecture question appears frequently in data engineering interviews at companies like Moonfare, Snowflake. While less common, it tests deeper understanding that distinguishes strong candidates.

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
109 words

CDC captures inserts, updates, and deletes from a source and applies them to a target in near real-time, enabling minimal-downtime migrations. Approaches: Log-based CDC (Debezium, AWS DMS)—reads WAL/redo logs; lowest latency, no schema change. Trigger-based—triggers on source; adds load and schema coupling. Timestamp/version columns—incremental only; misses deletes and out-of-order updates. Dual-write with reconciliation—applications write to both; eventual consistency and complexity. Why log-based: Non-invasive, captures all changes, low source overhead. Scalability: Kafka as CDC backbone allows multiple consumers and backpressure handling. Cost: DMS/MongoDB Atlas CDC have per-hour costs; Debezium is OSS but requires Kafka infra. Trade-off: Initial full snapshot + CDC is required; plan for schema evolution and idempotent upserts.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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According to DataEngPrep.tech, this is one of the most frequently asked System Design/Architecture interview questions, reported at 2 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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