Advanced~2h
Change Data Capture in Data Engineering CoreInterview Readiness Check
Log-based CDC internals: snapshot vs incremental phases, Debezium envelope, WAL/binlog mechanics, ordering and delivery semantics, schema evolution across CDC boundaries, downstream patterns (warehouse MERGE, lakehouse UPSERT, cache invalidation), the outbox pattern, and operational failure modes..
change data captureCDCDebeziumWALbinloglogical replicationoutbox patternschema registryupsertAWS DMSFivetran
Learning path
Try a sample question
Your team needs to replicate every row-level change from a PostgreSQL OLTP database into a downstream analytics store in near real-time. What approach gives you the lowest latency and the least load on the source database?
A.Polling: run a SELECT query every 30 seconds on the source table filtering by updated_at to detect changes
B.Log-based CDC: read the database's write-ahead log (WAL) using a tool like Debezium — captures every INSERT, UPDATE, DELETE without querying tables, adding near-zero read load to the source
C.Trigger-based CDC: add database triggers that write changed rows to a shadow table, then poll the shadow table
D.Full table dump: export the entire table to S3 every hour and diff the exports to find changes
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of change data capture fundamentals
- Trade-offs and design decisions in real system scenarios
- change data capture and CDC — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the Change Data Capture check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the Change Data Capture check test?
- The check covers log-based cdc internals: snapshot vs incremental phases, debezium envelope, wal/binlog mechanics, ordering and delivery semantics, schema evolution across cdc boundaries, downstream patterns (warehouse merge, lakehouse upsert, cache invalidation), the outbox pattern, and operational failure modes.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for Change Data Capture?
- Key concepts include: change data capture, CDC, Debezium, WAL, binlog, logical replication, outbox pattern, schema registry, upsert, AWS DMS, Fivetran. These frequently appear in data engineering core interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study Change Data Capture?
- Most engineers spend around 2 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before Change Data Capture?
- Change Data Capture builds on Batch ETL & Pipeline Design and Kafka & Streaming Systems. Complete those first for the best results.
- Is change data capture, CDC, Debezium covered?
- Yes. The Change Data Capture module covers change data capture, CDC, Debezium among other key concepts. The check includes questions that reflect real data engineering core interview patterns.