Records plus transaction/change metadata.
SQL database All projects
PROJECT WALKTHROUGH / CDC
CDC Data Pipeline
Process inserts, updates, and deletes while keeping downstream records consistent and recoverable.
ARCHITECTURE / LEARNING VIEW01Operational source 02Change capture 03Change landing 04Apply changes 05Current state 06Audit and consumers
Illustrative change feed and ordered offsets.
CDC conceptAppend-only change records with operation type.
Python · filesDeduplicate, order, and apply upsert/delete rules.
SQLMaintain a queryable latest record per key.
SnowflakeExpose current views and change lineage.
SQL views DESIGN WALKTHROUGH6 LAYERS
PROJECT OVERVIEW
Understand the system before building it.
A change data capture walkthrough covering ordered changes, replay safety, delete handling, checkpointing, and downstream current-state tables. It uses generic operational data and does not connect to a real CDC stream.
WHAT YOU WILL EXPLOREDistinguish snapshot loads from change streamsDesign offset/checkpoint and replay boundariesPreserve update and delete semantics downstream
01
START WITH THE WHY
Business problem
Make the data need understandable before choosing tools or designing pipelines.
A generic subscription service updates customer and service records throughout the day. Downstream analysis needs a current state plus enough history to explain when changes occurred.
Data engineering provides a repeatable way to move, validate, transform, and make data available with clear ownership and operational expectations.
The design gives consumers a reconciled current-state view and an auditable change path while making replay and delete semantics explicit.
02
DEFINE THE BOUNDARIES
Requirements
Separate what the workflow must do from the reliability and operational qualities it needs.
- Capture inserts, updates, and deletes
- Apply changes in a deterministic order
- Support replay from a known checkpoint
- Use configuration rather than hard-coded environment-specific values.
- Add validation, structured run logging, and clear failure boundaries.
- Protect credentials and grant only the access each workload needs.
- Measure freshness, duration, volume, and quality outcomes.
03
FOLLOW THE DATA
Architecture
A layered view of how data moves from source systems to a useful consumer-facing output.
Records plus transaction/change metadata.
SQL databaseIllustrative change feed and ordered offsets.
CDC conceptAppend-only change records with operation type.
Python · filesDeduplicate, order, and apply upsert/delete rules.
SQLMaintain a queryable latest record per key.
SnowflakeExpose current views and change lineage.
SQL viewsConceptual learning architectureSpecific services depend on requirements, constraints, and deployment choices.
04
KNOW WHAT ENTERS THE SYSTEM
Source systems
Map each source to its data shape, arrival pattern, ingestion choice, and likely failure modes.
SOURCE / 01Relational tables with change metadata
Subscription service database
Example data
Subscriber, plan, and service-status changes
Frequency
Continuous or micro-batch changes
Ingestion
Consume ordered change records from a supported source mechanism
Potential issues
Out-of-order eventsDuplicate deliveryDelete/tombstone interpretationRetention gaps
05–07
MOVE, SHAPE, STORE
Implementation flow
Build the pipeline in observable stages so each boundary can be tested and recovered independently.
Data ingestion
- Define the source change mechanism and its ordering key.
- Persist the source offset with each landed batch.
- Treat at-least-once delivery as possible and design for replay.
- Advance checkpoints only after target changes are committed.
SourcePipelineLandingRaw
Transformation
- Normalize operation type and source timestamps.
- Deduplicate repeated event IDs or source offsets.
- Apply updates by business key and deterministic sequence.
- Represent deletes explicitly rather than treating them as missing updates.
# Illustrative transformation outline
valid = [row for row in records if is_valid(row)]
rejected = [row for row in records if not is_valid(row)]
write_curated(valid)
write_quarantine(rejected, run_id=run_id)Storage / warehouse
- Retain append-only changes for audit within a defined retention plan.
- Maintain a current-state table keyed by business identity.
- Consider history tables where consumers need point-in-time state.
- Keep checkpoint and run metadata separate from business entities.
08
TRUST THE OUTPUT
Data quality
Make quality expectations explicit and decide what happens when a record or batch does not pass.
Incoming records
ValidationSchema · rules · keys
Valid recordsCurated target
!Invalid recordsReason · run ID · quarantine
- Check event ordering and uniqueness by source offset.
- Validate required business keys and operation values.
- Reconcile change counts against applied inserts, updates, and deletes.
- Monitor checkpoint lag and unexpected gaps.
09–12
RUN IT RESPONSIBLY
Operations, performance, security & monitoring
A realistic project also explains how the system behaves when inputs change, work slows down, or something fails.
09 / Error handling
- Retry transient transport errors without committing a checkpoint.
- Quarantine malformed events with their source offset.
- Make replay idempotent by using event identity and merge keys.
- Define a recovery plan if source retention no longer covers the checkpoint.
10 / Performance
- Process bounded micro-batches and avoid repeated full scans.
- Cluster or partition based on measured access patterns.
- Reduce unnecessary state rewrites and small-file generation.
- Measure lag, throughput, and merge cost together.
11 / Security
- Protect customer identifiers and sensitive attributes.
- Separate capture, transform, and consumer roles.
- Use managed secret storage in an actual deployment.
- Audit who can read raw changes and history.
12
Monitoring and observability
Monitor system health and data health together: pipeline status alone does not tell you whether the delivered data is fresh and complete.
- Track source offset lag, event volume, and apply duration.
- Alert on gaps, dead-letter growth, and stale current-state tables.
- Compare change operations received with operations applied.
- This is an observability plan, not a running CDC service.
OBSERVABILITY DESIGNCONCEPT
Pipeline statusRun state
Job durationElapsed time
Record countsRead · written · rejected
FreshnessLast successful data time
Data qualityRule outcomes
Access auditWho · what · when
CDC Data Pipeline monitoring signals shown as a design concept. No live pipeline or alert integration is connected.
13
PROMOTE WITH CONTROL
Deployment flow
Treat infrastructure, SQL, configuration, and validation as reviewed changes that move through separate environments.
01DevelopmentBuild and iterate
02TestingRun checks
03StagingValidate release
04ProductionOperate and observe
Git branches and pull requestsCI checks and data testsEnvironment-specific configurationDeployment validation and rollback plan
Deployment workflow is a learning design. No CI/CD pipeline is implemented by this walkthrough.
14
THINK THROUGH TRADE-OFFS
Challenges and solutions
Strong project explanations show the problem-solving process, not only the happy path.
The same change arrives twice after a retry.
Possible approach
Use a stable event ID or source offset and idempotent merge semantics.
An update arrives after a later event for the same key.
Possible approach
Use source ordering metadata and define how late events affect current state.
A delete is represented by a tombstone rather than a full row.
Possible approach
Model delete operations explicitly and preserve the key and event metadata needed downstream.
15
MAKE THE WORK CLEAR
How to explain this project in an interview
Use this outline to structure a truthful explanation. Adapt it to work you personally completed; this sample is not a claim about your experience.
EXPLANATION STRUCTURE
01Business problem
02Your role and scope
03Architecture and data flow
04Technology choices
05Challenges and solutions
06Quality, security, and performance
07Deployment and outcome
EXAMPLE INTERVIEW EXPLANATIONExample interview explanation: “This CDC design consumes ordered source changes, lands them append-only with operation type and offset, and applies them idempotently to a current-state table. The checkpoint advances only after changes are committed, so retries can safely replay a bounded interval. I would explicitly handle deletes, duplicates, and late events, and reconcile received versus applied operation counts. Monitoring would focus on lag, gaps, failures, and downstream freshness. The actual capture mechanism depends on the source database and its retention guarantees.”
PROJECT-SPECIFIC QUESTIONS
Practice the follow-up.
01
How do you distinguish event time from processing time?
02
How do you handle duplicate or out-of-order changes?
03
When should a checkpoint advance?
04
How would you represent deletes and preserve history?
05
What signals show CDC lag or data loss?
PROJECT CHECKLIST
Work through the build.
Tick items as you explore. This checklist is session-only and is not saved.
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