CDC Pipeline Interview Questions
CDC Pipeline Interview Questions
Change data capture pipeline design for data engineer interview prep.
Change data capture (CDC) pipeline interview questions for data engineer roles. Debezium watching Postgres WAL or MySQL binlog. Kafka Connect S3 Sink for raw immutable change events. Spark daily ETL with composite-key dedup. Snowflake MERGE INTO on (pk, run_id) for idempotent upsert. The canonical pattern for syncing transactional data to a warehouse.
Change data capture (CDC) pipeline design is the canonical pattern for syncing transactional source databases to an analytical warehouse without batching SELECT * every night. CDC reads the database's transaction log (Postgres write-ahead log, MySQL binlog, MongoDB oplog, SQL Server CDC) and emits change events (insert, update, delete) downstream. Modern CDC stacks in 2026 use Debezium as the connector framework, Kafka as the event bus, Kafka Connect S3 Sink for raw landing, Spark for transformation, and Snowflake MERGE INTO for warehouse upsert.
The canonical CDC architecture for a data engineer interview round. Step 1: Debezium connector watches the source database transaction log. For Postgres, this requires logical replication slots (configured with wal_level = logical, max_replication_slots). Debezium emits change events to Kafka with one topic per source table. Each event includes the before-state, after-state, and operation type. Step 2: Kafka topic per table with retention 7 days for replay. Step 3: Kafka Connect S3 Sink writes raw immutable change events to S3 partitioned by source table and date. Step 4: Spark daily ETL reads the S3 partitions, applies dedup on the composite natural key (table_pk, op_ts), and writes to a staging schema in Snowflake. Step 5: Snowflake MERGE INTO upserts from the staging schema to the production gold tables, with run_id baked into the output for backfill safety.
Five recurring failure modes in CDC pipeline design rounds. Debezium falls behind: the source database commits faster than Debezium can read the WAL; replication lag grows. Mitigation: monitor replication lag, scale Debezium connector instances, restart from snapshot if lag becomes unrecoverable. Schema change at source: a column is added or renamed in the source table; the Debezium event schema diverges from the destination. Mitigation: schema registry (Confluent Schema Registry or AWS Glue Schema Registry) enforces schema evolution rules, alerts on incompatible changes. Out-of-order events: rare with single-partition topics keyed by primary key, common with multi-partition topics. Mitigation: key the topic by primary key so all events for one row land on the same partition (guarantees in-order per row). Dedup needed: at-least-once delivery means the same change event can arrive twice; dedup on (pk, op_ts) with ROW_NUMBER, keep first. Snowflake MERGE deadlock: concurrent writer holds the table lock. Mitigation: serialize via Snowflake task or queue, or use Snowflake's auto-commit pattern with smaller transactions.
The MERGE INTO pattern for idempotent CDC upsert. MERGE INTO prod_table p USING staging_table s ON p.pk = s.pk WHEN MATCHED AND s.op_type = 'DELETE' THEN DELETE WHEN MATCHED AND s.op_type IN ('UPDATE', 'INSERT') THEN UPDATE SET col1 = s.col1, col2 = s.col2, updated_at = s.op_ts WHEN NOT MATCHED AND s.op_type IN ('UPDATE', 'INSERT') THEN INSERT (pk, col1, col2, updated_at) VALUES (s.pk, s.col1, s.col2, s.op_ts). The op_ts is the source database's commit timestamp; using it for updated_at preserves source ordering. Run_id can be baked into the staging table for backfill: re-running with the same run_id produces the same upsert result.
- What is the canonical CDC pipeline architecture for a data engineer interview?
- Debezium connector watches the source database transaction log (Postgres WAL, MySQL binlog) and emits change events to Kafka. Kafka Connect S3 Sink writes raw immutable events to S3 partitioned by source table and date. Spark daily ETL reads S3, dedups on (pk, op_ts), writes to Snowflake staging. Snowflake MERGE INTO on the staging schema upserts to production gold with run_id baked in.
- Why use Debezium instead of polling SELECT MAX(updated_at)?
- Three reasons. Debezium captures every change including soft deletes and intermediate updates between polls. Debezium does not load the source database with repeated SELECT queries. Debezium provides at-least-once delivery with snapshot recovery for new tables. Polling misses deletes (the row is gone), misses intermediate updates (only the latest state is visible), and adds load to the source production database.
- How does a data engineer handle schema changes in a CDC pipeline?
- Schema registry (Confluent Schema Registry, AWS Glue Schema Registry) enforces schema evolution rules at the producer side. Additive changes (new nullable column) propagate automatically. Breaking changes (renamed column, narrowed type) refuse to publish. Downstream bronze layer keeps raw payload as JSON for replay after writing a new mapping. Producer-side contract tests in CI are worth more than any amount of pipeline-side validation.
- How does a data engineer ensure CDC events are processed in order?
- Key the Kafka topic by primary key so all events for one row land on the same partition. Within a partition, Kafka preserves order. Multi-partition topics keyed by something other than primary key (or unkeyed) can produce out-of-order events for the same row, which breaks the upsert semantics. Use op_ts in the MERGE clause to handle the rare out-of-order case.
- What is the MERGE INTO pattern for idempotent CDC upsert?
- MERGE INTO prod_table p USING staging_table s ON p.pk = s.pk WHEN MATCHED AND s.op_type = 'DELETE' THEN DELETE WHEN MATCHED AND s.op_type IN ('UPDATE', 'INSERT') THEN UPDATE SET col = s.col, updated_at = s.op_ts WHEN NOT MATCHED AND s.op_type IN ('UPDATE', 'INSERT') THEN INSERT VALUES (s.pk, s.col, s.op_ts). The op_ts is the source commit timestamp; using it preserves source ordering. Re-running with the same staging data produces the same result.
- How does a data engineer handle Debezium falling behind on replication?
- Monitor replication lag (Debezium exposes metrics; CloudWatch or Prometheus alerts on lag thresholds). Scale connector instances if lag grows. If lag becomes unrecoverable (Debezium has fallen so far behind that the WAL has rotated past its position), restart from snapshot: Debezium re-snapshots the current state of every table, then resumes streaming from the current WAL position. The bronze layer's raw events allow replay over the snapshot gap.
- What is the difference between CDC and ELT?
- CDC is a specific mechanism for capturing changes from a source database. ELT is a pattern for loading raw data into a warehouse and transforming inside the warehouse. CDC is often the source of an ELT pipeline: Debezium captures changes, lands raw events in S3 bronze, dbt or Spark transforms inside the warehouse to silver and gold. ELT can also use non-CDC sources (event streams, API pulls, batch dumps); CDC is one input mechanism.
- Can a CDC pipeline achieve exactly-once semantics?
- Exactly-once effect, yes. Pure message-level exactly-once is impossible across producer-broker-consumer boundaries. The data engineer achieves exactly-once effect with at-least-once delivery (Debezium with snapshot recovery, Kafka with replication) plus idempotent processing (dedup on pk+op_ts, MERGE INTO with run_id, downstream tables FK to source pk). Re-running the pipeline produces the same warehouse state.
124 practice problems matching this filter. Difficulty: medium (57), hard (67).
Pipeline Architecture (124)
- 45 Minutes Turned Into 3.5 Hours - medium - Spark jobs are running. Just not fast enough.
- 600 Million Events a Day - hard - 600 million events a day. Two years of retention.
- A Clean Number for Every Merchant - hard - Raw payment logs in. Clean merchant summaries out.
- A Million Cars Phoning Home - hard - Every vehicle is a sensor. Deploy the pipeline to catch it all.
- Analysts Are Slowing the Store Down - medium - Orders placed. Data warehouse hungry.
- A New Column on a Billion Rows - hard - Add and backfill a new column to a billion-row production table with zero downtime.
- A Shared Drive Full of Contracts - medium - Buried in PDFs. The data is in there somewhere.
- A Stream All Day and a File at Midnight - hard - Real-time and batch. Same pipeline. No compromises.
- Badging Items That Already Sold Out - hard - Same-day delivery. The features have to be faster.
- Basel, CCAR, and Monday Morning - medium - The regulator does not accept 'eventually consistent.'
- Bikes Before Rush Hour - hard - Bikes in, bikes out. The city needs to predict demand.
- Credit for Every Touch - medium - They saw the ad, clicked the email, then bought. Who gets credit?
- Doubling Every Six Months - hard - Tuesdays are quiet. Black Friday is not.
- Eight-Hour-Old Positions - hard - Positions shift by the second. The math cannot lag.
- Eight Teams, Eight Latencies - medium - Millions of gamers. The architecture decision changes everything.
- End of Day Is Too Late - medium - Every swipe tells a story.
- Equities, ETFs, and the SEC - hard - Fractional shares, multi-currency, point-in-time. All of it.
- Event System for Multiple Consumers - hard - One event, many hungry consumers.
- Every Dataset Needs a Paper Trail - hard - The FDA has opinions about your data pipeline.
- Every Deal Is a Financial Transaction - hard - Real money on the table. Reconstruct every hand.
- Every Device, Every Impression - hard - Every ad seen. Every second watched. Real-time.
- Every Device Has Its Own Dialect - medium - Three sources. Three formats. Same workout.
- Every Firm Formats It Differently - medium - The regulator changed the format. Again. Handle it.
- Every Format Imaginable - hard - PDFs, HL7, JSON. All of it lands in the same lake.
- Everyone Wants the Same Data, Differently - hard - How you store it decides how fast you can read it.
- Every Region Exports Its Own Way - medium - Sales data, BigQuery, Dataflow. Make it all sing.
- Every Scan, Every Parcel, Every Pin Code - medium - Out for delivery. Delivered. Except the events arrived backwards.
- Fifty Thousand Retailers - medium - Retail data at CPG scale. Every SKU, every store.
- The Box That Won't Fit the Data - hard
- Five Times the Traffic, Five Times the Bill - hard - Scale up when needed. Do not bankrupt the team.
- Five Years of Cron Jobs - hard - Half the jobs run on cron. Half run on events. All of it has to move.
- Flying Blind Until Midnight - hard - Intraday risk, full lineage. The regulator is watching.
- Four Teams, One Topic, No Agreement - hard - Everybody is writing to it. Nobody documented it. Now production is fragile.
- Greenfield Build for Six Sources - hard - Infrastructure as code. Meaning as a service.
- Half a Million Rental Cars - medium - Every vehicle is reporting. Every rental matters.
- The Identity Problem - hard - Old systems. New demands. The same customer appears under three different names.
- Listens From Everywhere, Counted Once - hard - Phones, tablets, laptops. And some of them report late.
- Live Viewers, Live Billing - hard - The stream is live. The data cannot wait.
- Near-Real-Time Trending Dishes Dashboard - hard - The dish rankings update faster than the kitchen.
- Nested Docs, Flat Reports - medium - Two databases. One direction. No data left behind.
- Nightly Exports Are Too Slow - medium - Healthcare claims change constantly. The warehouse cannot fall behind.
- 4,500 Stores Before Sunrise - medium - The shelves open at 7. The data better be there.
- Not Every Team Can See Every Row - hard - Everyone can see the bucket. Not everyone should.
- One Bill Across Three Clouds - medium - AWS, Azure, GCP. Three bills. One truth.
- One Earthquake, Ten Thousand Tweets - hard - The firehose is on. Separate signal from noise.
- Out of the Data Center - medium - The on-prem servers are not getting any younger.
- The Speed Layer - medium - Dashboards can't wait for raw logs. Something has to happen upstream.
- Prove the Number Is Right - hard - Bad data in fintech is not just messy. It is expensive.
- Real Data, Fake Patients - hard - Dev needs production data. HIPAA says absolutely not.
- The Register Never Sleeps - medium - Every swipe lands in the warehouse. The table has to stay current without breaking.
- Recommendations Now, Royalties Later - medium - The catalog updated. Did anyone notice?
- Replicate It Without Breaking It - hard - The source changed. The lake needs to know immediately.
- Risk Models on Week-Old Data - medium - Loan approved. Loan denied. Every decision is an event.
- SaaS API Connector with Incremental Sync - medium - The API has rate limits. You have deadlines.
- Same-Day Sales, Every Store - medium - The cash register data needs to be queryable by morning.
- The Living Table - medium - Data lands continuously. History must survive every update.
- Score It Before It Clears - hard - The fraudsters move fast. Your pipeline has to move faster.
- Ship Before Fraud Finishes Checking - hard - The claim looks clean. The fraud model disagrees.
- Six Hours to Miss a Deadline - medium - The rebuild works. It just doesn't finish in time.
- Six Hours to Refresh Every Number - medium - Ratings change. The incremental model has to keep pace.
- Six Million Rows Before the Market Opens - medium - One massive CSV. Millions of timestamps.
- Six Sources, One Platform - medium - ADF orchestrates. Unity Catalog governs. Nothing leaks.
- Sixty Minutes, Every Hour - medium - Every hour, on the hour. No excuses.
- Stores and the Site, Together - hard - The registers never stop ringing.
- Store, Site, and Distributor - medium - Sales data is piling up. Someone has to make sense of it.
- The Acquisition Still Taking Bookings - hard - Two systems, two schemas. One truth.
- The Agency That Changes the Columns - medium - The schema changed overnight. Again.
- The Analysts Cannot Touch Production - medium - Production is the source. Analytics needs its own copy.
- The Analyst Who Saw the Salary Data - hard - Two incidents. One shared lake. The access model was never designed, just assumed.
- The API Drip Feed - medium - The API gives you 100 records at a time. You need millions.
- The Bad Row That Broke the Dashboard - medium - Bad records cannot reach the warehouse.
- The Binding and the Claim - medium - Policies are instant. Claims take their time.
- The Booking That Came Three Ways - hard - PMS, OTA, and website all think they took the reservation first.
- The Boutique That Sold in Six Currencies - hard - Every sale is real. The rate it was converted at depends on who is asking.
- The Bucket Full of Resumes - medium - A thousand resumes. Structured data inside each one.
- The Carrier Moving to Azure - medium - Claims arrive messy. The medallion cleans them up.
- The Claim That Picks Its Own Lane - medium - Three entry points. Different workflows. All must route correctly.
- The Clicks We Throw Away - hard - Every tap, swipe, and scroll. At scale.
- The Clock That Runs Two Ways - hard - Nightly batch and live events. One dashboard.
- The Consent Stitcher - medium - Consent was given. Or was it? Stitch the records together.
- The Dashboard and the Attribution Model - hard - Streaming and batch. One pipeline to rule them.
- The Decision Before the Door Closes - hard - The window to stop it is smaller than you think.
- The Distributor Filing Problem - medium - Hundreds of suppliers. One warehouse. One deadline.
- The Event Pile - hard - 600 million clicks a day. The budget is not infinite.
- The Fare Aggregator - medium - Airfares shift every minute. Catch the best ones.
- The Fleet That Never Stops - hard - Every truck is talking. Not everyone can hear them yet.
- The Leaderboard That Costs $25K a Month - hard - Product wants it live. Engineering has a price tag.
- The Meal Kit That Knows You - medium - What they ordered says a lot about what they want next.
- The Migration That Cannot Break Morning - hard - It all works today. Moving it without losing a single report is the hard part.
- The Models Going Stale - hard - The model is only as good as what you feed it.
- The Panel and the Set-Top Boxes - hard - Set-top boxes tell you who watched. Projection tells you how many.
- The Patients We Cannot Move - hard - Patient data stays local. Insights have to be global.
- The Points Arrive Two Days Late - medium - The bank data shows up late. The rewards were already sent.
- The Provider That Sometimes Sleeps - medium - The models run at dawn. The data has to be there first.
- The Query That Used to Be Fast - medium - Queries used to be fast. Something changed.
- The Queue That Wouldn't Stop Growing - medium - 500,000 messages behind and the number keeps climbing.
- The Revenue That Was Wrong for Two Weeks - medium - Nobody caught it until the CFO asked a question. Design the system that catches it first.
- The Sale That Needs to Land Now - medium - Three channels feeding one view. Not all of them speak the same language.
- The Signals That Power Recommendations - medium - Fresh signals, many teams, one pipeline.
- The User Who Asked to Be Forgotten - hard - Users want their data erased. Completely.
- The Vendor Who Never Warns You - medium - Every month, something is different. The dashboards have no idea.
- The What-If Machine - hard - A million slots. A thousand campaigns. Every combination matters.
- The Whiteboard Exercise - medium - Marker in hand. Draw the whole thing.
- Thirty Cities, One Forecast - hard - Five cities. Five data formats. One prediction.
- Thirty Countries, One Solvency Number - hard - Premiums collected globally. Losses happen locally.
- Thirty Million Unique Jobs a Year - hard - One press run, many orders. Group them right.
- Thousands of Practices, One Dataset - hard - Patient records in, operational insights out.
- Three Providers, One Workout - hard - The same ride, reported three times.
- Three Regions, One Finance Team - hard - Payments from everywhere. One consistent report.
- Three Regions, One Report - hard - Three regions, billions of payments, one merchant summary by 6 AM.
- Towers and Phones, Same Story - hard - Tower signals meet app events. Somewhere in between is the truth.
- Traders, Risk, and the Regulators - medium - Markets move in milliseconds. The pipeline has to keep up.
- Two Hundred Million Redirects - medium - Billions of clicks. One tiny code. Two very different clocks.
- Two Million Boxes by Monday Morning - hard - Shipped, maybe. Delivered, debatable.
- Two Systems, One Room Count - hard - Two booking systems. Rooms do not duplicate themselves.
- Two Ways to Catch a Change - medium - Two ways to watch the database. Each has a cost.
- Two Years of Every Click - hard - Every click, every aisle, every day for two years.
- Two Years of Clicks, Cheap - hard - Two years of clicks. Every query has to be affordable.
- What Everyone Is Watching - hard - Someone is watching. Capture everything.
- What Should We Recommend Tonight - hard - They ordered pad thai twice. That means something.
- Where Is Every Truck, Right Now - medium - Trucks are moving. Every ping counts.
- Which Promotion Is Actually Working - hard - Was the promotion worth it? The data knows.
- Who Is Churning and Why - medium - Subscribers churn. The pipeline cannot.
- Who Saw the Ad Twice - hard - TV and digital. Same viewer, two measurement worlds.