What is Change data capture (CDC)? Meaning, Examples, Use Cases, and How to Measure It?

Posted on February 19, 2026 | by Rajesh Kumar

Quick Definition

Change data capture (CDC) is a pattern and set of techniques to identify and record row-level changes in a source system and deliver those changes reliably to downstream systems with low latency.

Analogy: CDC is like a bank’s ledger feed that publishes every account transaction as soon as it commits so other systems can reconcile balances, detect fraud, or update reports in near real time.

Formal technical line: CDC captures create/update/delete events from a transactional data source, often by reading a persistent change stream such as a write-ahead log or transaction log, and produces an ordered, idempotent event stream for downstream consumption.

What is Change data capture (CDC)?

What it is:

A data integration approach that captures row-level changes from databases or other stateful sources and streams them as events.
It preserves order, supports at-least-once or exactly-once semantics depending on implementation, and commonly represents changes as before/after images or delta records.

What it is NOT:

Not simply periodic batch extraction or full-table snapshots.
Not guaranteed transactional semantics across multiple systems unless additional protocols are used.
Not a substitute for application-level concurrency control or isolation.

Key properties and constraints:

Source fidelity: depends on source transaction log fidelity.
Ordering guarantees: per-entity or per-partition ordering is typical; global ordering is hard.
Delivery semantics: at-least-once is common; exactly-once requires idempotence or deduplication.
Latency: ranges from sub-second to minutes.
Schema evolution handling: must support DDL changes.
Security and compliance: needs encryption, access controls, and auditing.
Backpressure and retention: consumers must handle backlog; retention of logs affects caught-up behavior.

Where it fits in modern cloud/SRE workflows:

Real-time analytics, materialized views, event-driven architectures, and microservice data sharing.
Infrastructure as code teams operate connectors and pipelines on Kubernetes or managed services.
SREs monitor SLIs like replication lag, event throughput, and consumer offsets; they own on-call for pipeline incidents.
Security teams review access to source logs and enforce least privilege.

Text-only diagram description:

Source database writes to transaction log. CDC agent tails the log and emits ordered change events. Events flow into a message bus or streaming platform. Consumers like analytics, caches, search indexes, and downstream databases subscribe and apply changes. Monitoring and schema registry are parallel services.

Change data capture (CDC) in one sentence

CDC converts transactional changes into an ordered event stream so downstream systems can react to data modifications in near real time.

Change data capture (CDC) vs related terms (TABLE REQUIRED)

ID	Term	How it differs from Change data capture (CDC)	Common confusion
T1	ETL	Batch-oriented extract transform load vs streaming row-level changes	Mistaken for real-time
T2	ELT	Load-first then transform vs CDC streams changes	Assumed same as CDC
T3	Event sourcing	Domain events are canonical vs CDC mirrors data store changes	Events vs source-of-truth confusion
T4	Stream replication	Often low-level log replication vs CDC may enrich events	Thought identical
T5	Log shipping	Binary log copies vs CDC emits semantic changes	Confused with CDC pipelines
T6	Materialized view	Read-optimized copy vs CDC supplies updates to build views	Viewed as CDC product
T7	Snapshot	Full image at time T vs CDC incremental changes	People use snapshots for CDC bootstrapping
T8	Audit logging	Often append-only messages vs CDC focuses on change replay	Assumed identical
T9	CDC connector	Implementation vs concept	Connector is a piece not whole
T10	Debezium	Implementation project vs CDC concept	People call CDC “Debezium”
T11	Replication lag	Metric vs replication correctness	Lag != data correctness
T12	CDC stream	Logical change events vs event-driven domain events	Semantic difference

Row Details (only if any cell says “See details below”)

None.

Why does Change data capture (CDC) matter?

Business impact:

Revenue continuity: Faster analytics and reduced data latency enable timely offers, fraud detection, and pricing adjustments.
Trust and compliance: Accurate audit trails and consistent replayable change history support audits and regulatory traceability.
Risk reduction: Minimize data drift between systems, reducing revenue leakage due to stale inventory or pricing.

Engineering impact:

Incident reduction: Automated propagation reduces manual sync errors and out-of-sync incidents.
Velocity: Teams decouple reads and writes, enabling independent scaling and faster feature development.
Data democratization: Downstream teams can subscribe to changes without direct source DB access.

SRE framing:

SLIs/SLOs: e.g., replication lag, event delivery ratio, consumer application apply latency.
Error budgets: Define acceptable window of lag or percent of dropped events.
Toil reduction: Automate schema evolution and connector restarts to reduce manual interventions.
On-call: SREs respond to pipeline stalls, schema incompatibilities, and retention issues.

What breaks in production (realistic examples):

Schema change causes connector crash and pipeline stalls; downstream caches are stale.
Slow consumer causes backlog; retention expires and data is lost.
Network partition between CDC service and message bus yields duplicated replays during recovery.
Misconfigured access gives connector read-only view, missing write intents and producing incomplete events.
Hidden data type mismatch causes silent data truncation in analytics.

Where is Change data capture (CDC) used? (TABLE REQUIRED)

ID	Layer/Area	How Change data capture (CDC) appears	Typical telemetry	Common tools
L1	Edge	Rare directly; used to sync edge stores	Sync latency, error rate	See details below: L1
L2	Network	Replication traffic metrics	Bandwidth, retries	Kafka Connect
L3	Service	Service-level view updates from CDC	Apply latency, failures	Debezium, Confluent
L4	Application	Populate caches and materialized views	Miss rate, backlog	Redis, Materialize
L5	Data	Feeding analytics and ML features	Throughput, lag, schema errors	Snowflake ingesters
L6	IaaS/PaaS	Managed connectors or self-hosted agents	CPU, memory, restart rate	AWS DMS, GCP Dataflow
L7	Kubernetes	CDC connectors as sidecars or operators	Pod restarts, offset lag	Strimzi, Operators
L8	Serverless	Managed CDC pipelines with functions	Invocation errors, duration	Functions, managed connectors
L9	CI/CD	Connector config deployments and migrations	Deployment success, drift	GitOps pipelines
L10	Observability	Monitoring CDC health	SLIs, logs, traces	Prometheus, Grafana
L11	Security	Access and audit trails	ACL violations, audit logs	IAM, Vault

Row Details (only if needed)

L1: Edge systems use CDC to synchronize local caches; latency and conflict resolution matter.
L5: Snowflake and data warehouses receive CDC to maintain near-real-time analytics.
L6: Managed services may hide infrastructure but differ in tuning and retention.

When should you use Change data capture (CDC)?

When it’s necessary:

You need low-latency replication from OLTP to analytics or caches.
Multiple systems must stay synchronized with transactional source.
Auditability and replayability of changes are required.
You must build feature stores or streaming ML pipelines.

When it’s optional:

Periodic reporting where hourly batch is sufficient.
Systems with low change volume and tolerant of delays.
When copying whole tables periodically is cheaper than maintaining CDC.

When NOT to use / overuse it:

For small datasets where periodic snapshot and copy is simpler.
For infrequently changing configuration data where complexity outweighs benefits.
If you cannot secure access to transaction logs or lack retention.

Decision checklist:

If sub-minute freshness is required AND cross-system consistency matters -> use CDC.
If analytic freshness of hours is acceptable AND cost must be minimal -> use batch ETL.
If transactional semantics across multiple sources required -> consider distributed transactions or compensating workflows.

Maturity ladder:

Beginner: Single-source simple CDC into a message bus with one consumer. Basic monitoring and retries.
Intermediate: Multiple connectors, schema registry, consumer groups, consumer-side idempotence, CI for connector configs.
Advanced: Multi-region replication, transactional outbox patterns, CDC-powered event mesh, automated schema evolution, SLO-driven autoscaling.

How does Change data capture (CDC) work?

Components and workflow:

Source capture: Agent or service reads the source change stream (e.g., WAL, binlog, redo logs, or native DB CDC API).
Event extraction: Changes are converted into structured change events (insert/update/delete) with metadata (timestamp, transaction id).
Optional transformation/enrichment: Normalize, add context, or apply masking/PII rules.
Publish to transport: Write to a durable streaming platform or queue (Kafka, Kinesis, Pub/Sub).
Consumer processing: Downstream systems subscribe, transform, and apply changes ensuring idempotence and ordering where necessary.
Offset and checkpoint: Consumers commit offsets or positions to track progress and support resuming.
Monitoring and governance: Metrics, audit logs, schema registry and access controls.

Data flow and lifecycle:

Initial snapshot: required for bootstrapping target with current state.
Ongoing stream: applies deltas after snapshot.
Retention: source log retention defines how far back missing consumers can catch up.
Compaction/cleanup: downstream may compact events to reduce storage.

Edge cases and failure modes:

Schema changes mid-stream; must handle added/dropped columns and type changes.
Transactional boundaries: multi-row transactions must be kept atomic where necessary.
Network partitions cause split-brain or duplicated events upon reconnect.
Backpressure: slow consumers create buildup; retention might expire leaving gaps.
Consumer crashes; need to resume from last committed offset safely.

Typical architecture patterns for Change data capture (CDC)

Direct DB log-to-bus: – Use-case: Low-latency replication with minimal intermediaries. – When: High throughput transactional systems.
Connector + Kafka + Consumers: – Use-case: Generic streaming hub powering many consumers. – When: Multiple downstream teams consume same data.
Outbox pattern: – Use-case: Ensure cross-system transactional atomicity by writing domain events to DB table within the transaction and CDC reads the outbox. – When: Need transactional guarantees without distributed transactions.
Materialized view builder: – Use-case: Build read-optimized views in a store like Redis or Elasticsearch from CDC. – When: Low-latency read access required.
Managed CDC service: – Use-case: Use cloud-managed connectors to reduce ops. – When: Prefer operational simplicity and can accept provider constraints.
Schema-registry-enabled pipeline: – Use-case: Enforce schemas and compatibility for evolution and type safety. – When: Many consumers and frequent schema changes.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Connector crash	No new events published	Connector error or OOM	Restart, increase mem, fix bug	Crash count, restarts
F2	Lag growth	Consumer lag rising	Slow consumer or throughput spike	Scale consumers, tune batching	Consumer offset lag
F3	Schema error	Event rejected downstream	DDL not handled	Add transformation, update schema registry	Schema error logs
F4	Duplicate events	Duplicate records applied	At-least-once semantics	Make consumers idempotent	Duplicate apply metric
F5	Data loss	Missing updates after retention	Log retention expired	Increase retention, faster consumers	Gap in offsets
F6	Partial transaction	Out-of-order records	Transaction boundary lost	Use transaction-aware connector	Transaction error logs
F7	Backpressure	Producer blocked	Downstream slow or broker full	Increase broker capacity, throttle	Producer throughput drop
F8	Security breach	Unauthorized access	Loose IAM or creds leaked	Rotate creds, tighten ACLs	ACL violation logs
F9	Latency spikes	High end-to-end latency	Network or GC pauses	Tune JVM, scale network	99th percentile latency
F10	Inconsistent state	Divergence between source & target	Replays or missed events	Resync snapshot	State diff reports

Row Details (only if needed)

None.

Key Concepts, Keywords & Terminology for Change data capture (CDC)

Term — Definition — Why it matters — Common pitfall

Transaction log — Persistent DB write-ahead or binlog stream — Source of truth for changes — Assuming all DBs expose it
Binlog — MySQL binary log format — Primary source for MySQL CDC — Connector config errors
WAL — Write-ahead log (Postgres) — Used for WAL-based CDC — Log retention constraints
Connector — Agent that reads logs and emits events — Implements CDC logic — Misconfiguring offsets
Debezium — Open-source CDC project — Widely used connector framework — Treat as protocol instead of tool
Kafka — Distributed log used for CDC transport — Durable streaming backbone — Topic partitioning mistakes
Topic partition — Shard for ordering and scale — Maintains order per key — Poor key choice breaks ordering
Offset — Consumer position in stream — Enables resume — Not committed leads to replay
Exactly-once — Delivery guarantee with de-duplication — Reduces duplicates — Hard to implement end-to-end
At-least-once — Common delivery guarantee — Simpler and reliable — Requires idempotence
Idempotence — Ability to apply event multiple times safely — Simplifies recovery — Often unimplemented
Snapshot — Full data export at specific time — Bootstraps downstream state — Heavy on source
Outbox pattern — Persist domain events to DB table to guarantee atomic write — Solves transactional consistency — Adds table management
CDC stream — The event stream produced by CDC — Canonical source for downstream systems — Confused with domain events
Schema registry — Stores event schemas and compatibility rules — Manages evolution — Missing registry causes failures
DDL handling — How schema changes are captured — Required for long-lived pipelines — Can break consumers
Change event — Representation of insert/update/delete — Fundamental CDC unit — Poorly modeled events create ambiguity
Before/After image — Snapshot of row before and after change — Enables diffs — Privacy concerns if PII is included
Logical decoding — Postgres feature to stream logical changes — Enables non-invasive CDC — Plugin dependencies
Debezium snapshotting — Debezium snapshot mode — Bootstrapping approach — Long snapshots can block
Exactly-once semantics (EOS) — Guarantee of single effective delivery — Important for financial systems — Complex to coordinate
Compaction — Reducing events by keeping latest state — Saves storage — Loses history
Tombstone — Marker for deletion in compacted topics — Needed for GC-aware systems — Consumer must respect deletion
Watermark — Progress marker in streams — Helps windowing for analytics — Hard to maintain cross-partition
Backpressure — When consumers slow producers — Requires flow control — Often unhandled
Retention — How long logs or topics retained — Affects catch-up ability — Short retention causes data loss
Consumer group — Set of consumers that share load — Enables scale — Misbalanced partitions lead to hotspots
Partition key — Determines event-to-partition mapping — Preserves per-key ordering — Poor keys cause skew
Exactly-once connectors — Source/sink connectors with transactional writes — Improves correctness — Limited support
Change data capture lag — Time difference between commit and downstream apply — Core SLI — Often ignored until incidents
Mutation — A single row change — Unit of CDC — High mutation rate can overload pipeline
CDC pipeline — Full end-to-end CDC flow — Operational unit — Many moving parts to observe
Event enrichment — Adding context such as tenant id — Useful for multi-tenant systems — Adds complexity
Schema evolution — Changes to event schema over time — Expected in real systems — Breaking changes if not managed
Data lineage — Traceability of data’s origin — Important for compliance — Often incomplete
Masking — Hiding PII before publishing — Security requirement — Over-masking breaks analytics
Replayability — Ability to replay events to rebuild state — Enables debugging — Requires durable retention
Snapshot isolation — DB isolation level impacting CDC consistency — Affects correctness — Not always supported
CDC operator — Kubernetes controller for connectors — Simplifies lifecycle — Operator bugs cause outage
Throttling — Rate-limiting of events — Protects downstream systems — Misconfigured throttle causes excessive lag
Schema compatibility — Backwards/forwards compatibility rules — Ensures safe evolution — Ignored for rapid changes
Event watermarking — Markers for progress and time windows — Important for streaming analytics — Coordination costs
CDC governance — Policies for access, retention, and schemas — Reduces risk — Often absent in orgs
Transaction boundary — Grouping of changes from single transaction — Needed for correctness — Losing it corrupts state

How to Measure Change data capture (CDC) (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Replication lag	How far behind consumers are	Source commit time vs apply time	95p < 5s	Time sync needed
M2	Event delivery ratio	Percent events delivered	Events published vs acknowledged	99.9%	Duplicate handling affects ratio
M3	Consumer offset lag	Messages unprocessed	Broker lag per partition	< 1000 messages	Varied sizes skew latency
M4	Connector uptime	Availability of connector	Healthy seconds/total	99.9%	Short flaps still disruptive
M5	Snapshot completion time	Time to bootstrap	Snapshot end-start	See details below: M5	Snapshots impact source
M6	Schema error rate	Events failing due to schema	Schema rejects/total	< 0.01%	Registry configs matter
M7	Apply error rate	Downstream failures applying events	Failed applies/total	< 0.1%	Retry storms inflate errors
M8	Duplicate apply rate	Percent duplicate applies	Duplicate detects/total	< 0.01%	Requires idempotency logic
M9	Backlog size	Retained unprocessed events	Topic depth or storage	See details below: M9	Backlog can mask issues
M10	End-to-end latency P99	Worst-case latency	Percentile of commit to final apply	P99 < 30s	Spikes indicate issues

Row Details (only if needed)

M5: Snapshot completion time measures how long initial state copy takes; affects source load and availability.
M9: Backlog size measured in bytes or events; rising backlog suggests scaling or retention problems.

Best tools to measure Change data capture (CDC)

Tool — Prometheus + Grafana

What it measures for Change data capture (CDC): Connector metrics, lag, throughput, JVM stats.
Best-fit environment: Kubernetes and cloud VMs.
Setup outline:
Export connector metrics via Prometheus endpoints.
Scrape broker and consumer metrics.
Define dashboards and alerts.
Use recording rules for derived metrics.
Strengths:
Flexible and open.
Good ecosystem for alerting and dashboards.
Limitations:
Needs ops work to scale.
Short-term retention unless managed.

Tool — Kafka Connect metrics

What it measures for Change data capture (CDC): Connector-specific throughput, offsets, errors.
Best-fit environment: Kafka-based pipelines.
Setup outline:
Enable JMX metrics for Connect.
Map metrics to Prometheus or monitoring.
Monitor task-level metrics.
Strengths:
Detailed connector telemetry.
Task-level debugging.
Limitations:
Requires Kafka expertise.
Varies by connector implementation.

Tool — Observability platforms (Datadog/New Relic)

What it measures for Change data capture (CDC): End-to-end traces, logs, metrics combined.
Best-fit environment: Cloud teams needing integrated view.
Setup outline:
Ingest metrics, logs, and traces.
Create CDC-specific dashboards.
Configure alerts across signals.
Strengths:
Unified view and anomaly detection.
Limitations:
Cost at high scale.
Vendor lock-in considerations.

Tool — Confluent Control Center

What it measures: Kafka topical health, consumer lag, connector health.
Best-fit environment: Confluent-managed Kafka.
Setup outline:
Deploy C3 with Kafka cluster.
Configure connector monitoring.
Use schema registry integration.
Strengths:
Purpose-built for Kafka ecosystems.
Limitations:
Tied to Confluent stack.

Tool — Data validation frameworks (e.g., Monte Carlo-like)

What it measures: Data quality, drift, schema changes, row counts.
Best-fit environment: Analytics teams with many consumers.
Setup outline:
Define expectations for tables and streams.
Schedule checks that validate CDC outputs.
Alert on anomalies.
Strengths:
Detects silent data issues.
Limitations:
Config overhead and cost.

Recommended dashboards & alerts for Change data capture (CDC)

Executive dashboard:

Panels:
Overall pipeline health (aggregate connector uptime)
Business-critical lag (top 5 pipelines by data importance)
Trend of delivery ratio over 30 days
Incidents open related to CDC
Why: Gives leadership readiness and business risk summary.

On-call dashboard:

Panels:
Connector status list and restarts
Per-connector lag heatmap
Top failing partitions or topics
Recent schema errors and failing endpoints
Recent alerts and incident links
Why: Fast triage and routing to owners.

Debug dashboard:

Panels:
Per-task JVM metrics (GC, heap)
Broker metrics (IO wait, disk usage)
Consumer offsets and throughput
Example event payloads and schema
Snapshot progress and duration
Why: Deep troubleshooting for RCA.

Alerting guidance:

Page vs ticket:
Page (pager) for connector down, sustained lag above SLO, or data loss signs.
Ticket for transient errors, schema warnings, or non-business-critical issues.
Burn-rate guidance:
Use error budget burn rates to escalate; if 3x expected burn in 1 hour, page on-call.
Noise reduction tactics:
Deduplicate alerts for same connector across nodes.
Group alerts by logical pipeline.
Suppress noisy alerts during planned maintenance or snapshot windows.

Implementation Guide (Step-by-step)

1) Prerequisites – Identify critical source tables and transactional properties. – Ensure time synchronization (NTP) across systems. – Define security model and grant least-privilege access. – Choose transport and storage (Kafka, cloud pub/sub). – Prepare schema registry and transformation plans.

2) Instrumentation plan – Export connector and broker metrics. – Generate SLIs and dashboards before production runs. – Add tracing IDs to events where possible. – Log everything relevant for debugging.

3) Data collection – Decide snapshot strategy and snapshot window. – Configure connector for logical decoding or binlog read. – Enable encryption in transit and at rest.

4) SLO design – Define replication lag SLOs per pipeline. – Define acceptable delivery ratios and error budgets. – Align SLOs with business objectives.

5) Dashboards – Build executive, on-call, and debug dashboards. – Create severity tagging for panels to help triage.

6) Alerts & routing – Map alerts to owners and escalation policies. – Configure grouping and suppression rules.

7) Runbooks & automation – Create runbooks for connector restarts, resyncs, and schema changes. – Automate safe rollbacks for connector configurations.

8) Validation (load/chaos/game days) – Run load tests with realistic mutation patterns. – Inject failures: kill connectors, reduce retention, simulate DDLs. – Run game days to test ops readiness.

9) Continuous improvement – Review incidents and update runbooks weekly. – Automate repetitive fixes and enable CI for connector config.

Checklists:

Pre-production checklist

Confirm access to source logs.
Test snapshot and incremental flow.
Validate SLI dashboards and alerts.
Run small-scale load test.
Document rollback and resync steps.

Production readiness checklist

Define owner and on-call rota.
Ensure retention meets catch-up needs.
Verify encryption and IAM.
Load capacity planning completed.
Legal/compliance reviewed for PII.

Incident checklist specific to Change data capture (CDC)

Verify connector health and logs.
Check retention horizon and backlog.
Review schema registry for recent changes.
If data loss suspected, halt downstream consumers and plan resync.
Capture offsets and take snapshots before remediation.

Use Cases of Change data capture (CDC)

Real-time analytics – Context: BI needs up-to-date dashboards. – Problem: Batch ETL causes stale insight. – Why CDC helps: Streams changes into data warehouse quickly. – What to measure: Replication lag, apply errors. – Typical tools: Kafka Connect to Snowflake.
Cache population – Context: Read-heavy service needs fast access. – Problem: Cache rebuilds expensive and inconsistent. – Why CDC helps: Maintain cache incrementally. – What to measure: Cache miss rate, lag. – Typical tools: Redis or Memcached with CDC job.
Search index updates – Context: Product search requires current inventory. – Problem: Bulk reindex is slow and costly. – Why CDC helps: Apply updates to Elasticsearch incrementally. – What to measure: Index lag, document mismatch rate. – Typical tools: Logstash or bespoke consumers.
Microservice data sync – Context: Multiple services need customer data. – Problem: Cross-service queries cause tight coupling. – Why CDC helps: Event-driven data sharing and local materialized views. – What to measure: Consistency checks, lag. – Typical tools: Outbox pattern + CDC.
Feature stores for ML – Context: ML requires fresh features. – Problem: Feature staleness reduces model accuracy. – Why CDC helps: Stream feature updates into store. – What to measure: Update latency, completeness. – Typical tools: Feature store with streaming ingestion.
Audit and compliance – Context: Regulatory audits need immutable history. – Problem: Not all systems record row-level changes. – Why CDC helps: Provide a replayable history of changes. – What to measure: Completeness, retention policy adherence. – Typical tools: Immutable event storage, schema registry.
Multi-region replication – Context: Low-latency regional reads. – Problem: Keeping regions consistent is complex. – Why CDC helps: Stream changes to regional replicas. – What to measure: Cross-region lag, conflict rate. – Typical tools: Message bus with geo-replication.
Hybrid-cloud data sync – Context: On-prem database must sync with cloud analytics. – Problem: Network and security barriers. – Why CDC helps: Incremental, bandwidth-efficient sync. – What to measure: Throughput, encryption verification. – Typical tools: Secure connectors and VPNs.
Data migration – Context: Moving data to new platform. – Problem: Downtime unacceptable. – Why CDC helps: Bootstrapping snapshot + CDC for cutover. – What to measure: Cutover lag, divergence checks. – Typical tools: CDC-enabled migration tools.
Operational alerts – Context: Real-time alerts for business anomalies. – Problem: Batch detection delays responses. – Why CDC helps: Trigger alerts on important mutations. – What to measure: Alert accuracy, latency. – Typical tools: Streaming analytics engines.
Materialized view maintenance – Context: Precomputed joins for fast queries. – Problem: View staleness. – Why CDC helps: Incrementally update views. – What to measure: View freshness, correctness. – Typical tools: Stateful stream processors.
Data warehouse harmonization – Context: Consolidate sources into unified DW. – Problem: Data drift and schema mismatches. – Why CDC helps: Stream standardized changes and handle DDL. – What to measure: Schema compatibility, ingestion rate. – Typical tools: CDC pipelines into ETL tools.

Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes-based CDC for multi-tenant SaaS

Context: SaaS product runs on Kubernetes with PostgreSQL per customer and needs aggregated analytics. Goal: Stream tenant-level changes into central analytics cluster with low latency. Why Change data capture (CDC) matters here: Avoids expensive queries and enables near real-time aggregate metrics. Architecture / workflow: Postgres WAL -> Debezium sidecar deployed in Kubernetes -> Kafka topic per tenant -> Consumer jobs aggregate to analytics DB. Step-by-step implementation:

Deploy Debezium as StatefulSet with proper resource limits.
Configure logical decoding plugin and replication slot per DB.
Create topics partitioned by tenant id.
Build consumer that aggregates and writes to analytics. What to measure: Replication lag per tenant, connector restarts, per-topic backlog. Tools to use and why: Debezium for Postgres, Kafka for transport, Prometheus for metrics. Common pitfalls: Replication slots accumulating causing WAL bloat; fix by monitoring slot usage and retention. Validation: Load test with simulated tenant activity and run chaos tests by killing pods. Outcome: Near-real-time tenant analytics without querying production DB.

Scenario #2 — Serverless CDC into analytics (managed PaaS)

Context: Cloud DB on managed service; want serverless ingestion without managing connectors. Goal: Ingest changes to analytics warehouse with minimal ops. Why Change data capture (CDC) matters here: Keeps analytics current with low ops overhead. Architecture / workflow: Managed CDC service -> Cloud pub/sub -> Serverless functions transform and load into data warehouse. Step-by-step implementation:

Enable managed DB change streams.
Configure topic delivery to pub/sub.
Deploy serverless functions to apply transformations and write to warehouse. What to measure: Invocation errors, function duration, end-to-end latency. Tools to use and why: Managed connector, cloud pub/sub, serverless functions. Common pitfalls: Cold starts causing latencies; mitigate with warming or provisioned concurrency. Validation: Perform controlled schema change and observe compatibility. Outcome: Managed, low-ops pipeline for analytics.

Scenario #3 — Incident response and postmortem for missed events

Context: Production alerts show analytics dashboards missing orders for an hour. Goal: Identify root cause, repair state, and prevent recurrence. Why Change data capture (CDC) matters here: Provides replayable history to rebuild missing state. Architecture / workflow: Investigate connector logs -> check retention and offsets -> snapshot and replay missing window. Step-by-step implementation:

Triage: check connector health and broker offsets.
Determine retention horizon; if data still available, replay from offset.
If lost, take a snapshot and reapply.
Run data diff checks to verify. What to measure: Time to detect and time to recover. Tools to use and why: Broker monitoring, data validation tools, snapshot utilities. Common pitfalls: Insufficient retention causing irreversible loss. Validation: Postmortem with RCA and action items. Outcome: Restored consistency and improved alerting and retention.

Scenario #4 — Cost/performance trade-off scenario for high-volume updates

Context: Retail platform with flash sale causing tens of thousands of updates per second. Goal: Maintain low-latency analytics while controlling cost. Why Change data capture (CDC) matters here: Streams updates without expensive full-table scans. Architecture / workflow: Connector -> partitioned topics by product id -> consumers with batching and compaction for downstream. Step-by-step implementation:

Partition topics effectively to spread load.
Enable compaction to retain only latest per key.
Batch applies to analytics to reduce cost.
Autoscale consumers during sale windows. What to measure: Cost per GB, apply throughput, latency percentiles. Tools to use and why: Partitioned Kafka, compaction, autoscaling policies. Common pitfalls: Wrong partition key causing hotspots and throttling. Validation: Load tests simulating flash sale spikes. Outcome: Controlled costs with acceptable latency and availability.

Scenario #5 — Serverless outbox for transactional email events

Context: Need reliable delivery of transactional emails triggered by DB writes. Goal: Guarantee emails are sent exactly once per triggering transaction. Why Change data capture (CDC) matters here: Use outbox table written in same transaction and CDC to publish events. Architecture / workflow: App writes data + outbox row -> CDC emits outbox event -> serverless function consumes and sends email -> marks event processed. Step-by-step implementation:

Add outbox table schema and write within app transaction.
Configure CDC to stream outbox table.
Implement consumer with idempotence and DLQ. What to measure: Delivery ratio, duplicate send attempts, latency. Tools to use and why: Debezium + serverless functions + idempotent email sender. Common pitfalls: Missing unique idempotence key causing double sends. Validation: Simulate retries and ensure idempotence holds. Outcome: Reliable transactional messaging without distributed transactions.

Scenario #6 — Multi-region read replicas with conflict resolution

Context: Global application with low-latency reads per region. Goal: Replicate writes to regional read stores and handle rare conflicts. Why Change data capture (CDC) matters here: Stream writes to regional replicas quickly. Architecture / workflow: CDC to central bus -> geo-replicated topics -> region-specific consumers apply changes with conflict resolution rules. Step-by-step implementation:

Establish global topics with replication.
Implement deterministic conflict resolution policies.
Use compaction to maintain latest state. What to measure: Cross-region lag, conflict rate, divergence. Tools to use and why: Kafka with MirrorMaker or managed equivalents. Common pitfalls: Non-deterministic resolution causing divergence. Validation: Simulate concurrent writes and validate results. Outcome: Low-latency regional reads with consistent conflict handling.

Common Mistakes, Anti-patterns, and Troubleshooting

List of mistakes with Symptom -> Root cause -> Fix

Symptom: Connector keeps restarting -> Root cause: OOM or memory leak -> Fix: Increase memory, tune GC, update connector.
Symptom: Rising replication lag -> Root cause: Slow consumer processing -> Fix: Scale consumers, improve batching.
Symptom: Schema error spikes -> Root cause: Uncoordinated DDL -> Fix: Use schema registry and staged deployments.
Symptom: Duplicate records downstream -> Root cause: At-least-once delivery without idempotence -> Fix: Add idempotency keys.
Symptom: Missing historical data -> Root cause: Short log retention -> Fix: Increase retention or snapshot soon.
Symptom: Platform cost spikes -> Root cause: Inefficient batching or per-event transactions -> Fix: Batch and optimize writes.
Symptom: Silent data corruption -> Root cause: Type casting or transformation bug -> Fix: Add validation tests and checksums.
Symptom: High disk I/O on broker -> Root cause: Excessive retention or unbounded topics -> Fix: Tune retention and compaction.
Symptom: Long snapshot windows -> Root cause: Large tables and blocking snapshot mode -> Fix: Use non-blocking snapshot and offline copy.
Symptom: Security audit failures -> Root cause: Wide DB credentials for connectors -> Fix: Apply least-privilege and rotate creds.
Symptom: Alerts ignored due to noise -> Root cause: Poor alerting thresholds -> Fix: Reduce noise with grouping and SLO-driven alerts.
Symptom: Consumer offset drift -> Root cause: Uncommitted offsets during crashes -> Fix: Ensure commit-on-success or durable checkpoints.
Symptom: Hot partitions -> Root cause: Poor partition key selection -> Fix: Repartition or redesign keys.
Symptom: Long GC pauses -> Root cause: Large JVM heaps for connector -> Fix: Tune JVM, use container memory limits.
Symptom: Data privacy issues -> Root cause: Publishing PII without masking -> Fix: Implement masking in pipeline.
Symptom: Broken downstream joins -> Root cause: Inconsistent event schemas -> Fix: Enforce compatibility and migrations.
Symptom: Missed SLAs during peak -> Root cause: No capacity planning -> Fix: Autoscale and stress test.
Symptom: Manual resyncs frequent -> Root cause: No automated resync tools or runbooks -> Fix: Automate resync and create runbooks.
Symptom: High latency spikes -> Root cause: Network flaps or broker GC -> Fix: Multi-zone network configuration, broker tuning.
Symptom: Incorrect delete handling -> Root cause: Missing tombstones -> Fix: Ensure deletions emit tombstone events.
Symptom: Observability gaps -> Root cause: Missing metrics or logs -> Fix: Instrument all components and ship telemetry.
Symptom: Unauthorized access -> Root cause: Overly permissive IAM -> Fix: Tighten ACLs and audit keys.
Symptom: Event ordering lost -> Root cause: Multi-partition writes without keying -> Fix: Choose partition key to preserve ordering.
Symptom: Downstream idempotence errors -> Root cause: No idempotency strategy -> Fix: Use unique keys and de-dup caches.
Symptom: Slow consumer restarts -> Root cause: Large backlog and cold cache -> Fix: Warm caches and incremental catch-up.

Observability pitfalls (at least 5 included above):

Missing per-connector metrics.
No offset tracking visibility.
No correlation IDs in logs.
Absent historical telemetry for lag trends.
Lack of schema evolution metrics.

Best Practices & Operating Model

Ownership and on-call:

Assign clear owner for each CDC pipeline end-to-end.
Runbook owner separate from source DB owner.
On-call rotations with playbooks for common failures.

Runbooks vs playbooks:

Runbook: Step-by-step operational checks and commands.
Playbook: Higher-level decision trees and escalation procedures.
Maintain both; test them in game days.

Safe deployments:

Canary connectors and staged rollout of DDL handling.
Use feature flags for transformations.
Implement automated rollback if lag or error rates spike.

Toil reduction and automation:

Automate connector restarts with restart limits and health checks.
Autoscale consumers based on lag metrics.
Automate schema compatibility checks in CI.

Security basics:

Least-privilege for connectors and service accounts.
Encrypt data in transit and at rest.
Mask PII at source or in transit.
Audit access and use key rotation.

Weekly/monthly routines:

Weekly: Review connector restarts and lag trends.
Monthly: Capacity planning and retention review.
Quarterly: Security audits and schema cleanup.

What to review in postmortems related to Change data capture (CDC):

Timeline of events with offsets and commits.
Retention and backlog states during incident.
Schema changes and deployment events.
Actions taken, time to detect, time to remediate, and follow-ups.

Tooling & Integration Map for Change data capture (CDC) (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Connectors	Read DB logs and produce events	Kafka, Pub/Sub, DWs	Debezium widely used
I2	Brokers	Durable transport and storage	Connectors, consumers	Kafka, Kinesis, Pub/Sub
I3	Schema registry	Store and enforce schemas	Producers and consumers	Compatibility rules
I4	Stream processors	Transform and enrich events	Kafka Streams, Flink	Real-time transformations
I5	Managed CDC	Cloud-managed connectors	Cloud DB and pubsub	Less ops heavy
I6	Data warehouses	Sink for analytics	CDC pipelines	Requires ingestion best practices
I7	Cache/index stores	Materialized views and search	CDC consumers	Redis, Elasticsearch
I8	Monitoring	Metrics and alerting	Prometheus, Datadog	Per-connector visibility
I9	Validation tools	Data quality checks	Sinks and sources	Detect silent drift
I10	Operators	Manage connectors on K8s	Kubernetes	Operators simplify lifecycle

Row Details (only if needed)

None.

Frequently Asked Questions (FAQs)

What is the main difference between CDC and event sourcing?

Event sourcing treats domain events as the primary source of truth; CDC exposes database changes from an existing transactional store.

Can CDC guarantee exactly-once delivery?

End-to-end exactly-once is hard; some systems provide transactional sinks, but idempotence and careful design are the practical approach.

Is CDC only for relational databases?

No. Any stateful system with changelog semantics can be a source, but relational DBs are common.

How do you handle schema changes in CDC?

Use schema registry, compatibility rules, and staged migrations; treat DDL as first-class events.

Does CDC increase security risks?

It can if access and encryption aren’t enforced; apply least-privilege and audit logs.

Is CDC real-time?

It depends on implementation; latency ranges from sub-second to minutes.

How does CDC work with multi-row transactions?

Transaction-aware connectors preserve ordering and emit transaction boundaries when supported.

What happens if the CDC connector falls behind?

Backlog grows; if log retention expires, you may need a snapshot to resync.

Do I need Kafka to use CDC?

No; alternatives exist like cloud pub/sub or managed brokers.

How do you make downstream systems idempotent?

Use unique keys, dedupe caches, and design operations to be commutative where possible.

Can CDC be used for GDPR right-to-erasure requirements?

Yes, but you must handle deletion events and retention policies carefully, and possibly mask or delete PII downstream.

Should I use managed CDC services?

If you prefer less ops and your requirements fit the provider, yes; managed services reduce operational overhead.

How do you test CDC pipelines?

Use synthetic load, schema change tests, replay scenarios, and data diff checks.

What are typical SLOs for CDC?

Common SLOs include replication lag percentiles and delivery ratio thresholds, tuned per pipeline criticality.

How do you prevent duplication during recovery?

Design idempotent consumers and use unique event keys or transactional sinks.

Is CDC suitable for multi-master setups?

It can be complex; conflict resolution and deterministic merging are necessary.

How much does CDC cost?

Varies / depends on throughput, retention, and tooling; plan for storage, compute, and network.

How do I handle PII in CDC streams?

Mask or redact at source or in pipeline; apply access controls and encryption.

Conclusion

Change data capture is a foundational pattern for modern data platforms enabling low-latency synchronization, event-driven architecture, and robust auditing. It requires careful design around schema evolution, delivery semantics, observability, and security. Proper SLOs, runbooks, and automation are essential to operate CDC at scale.

Next 7 days plan:

Day 1: Inventory source tables and define critical pipelines.
Day 2: Choose transport and connector approach; secure access.
Day 3: Prototype connector with snapshot + incremental flow.
Day 4: Build basic dashboards and SLI collection for lag and errors.
Day 5: Run small load test and validate resync/runbook steps.
Day 6: Implement schema registry and baseline compatibility rules.
Day 7: Conduct a game day simulating connector failure and recovery.

Appendix — Change data capture (CDC) Keyword Cluster (SEO)

Primary keywords
change data capture
CDC
database change data capture
CDC pipeline
CDC architecture
real-time data replication
database binlog capture
WAL change data capture
Secondary keywords
Debezium CDC
Kafka CDC
logical decoding Postgres
outbox pattern
CDC monitoring
replication lag metric
CDC connectors
schema registry CDC
Long-tail questions
what is change data capture and how does it work
how to implement CDC in Kubernetes
CDC vs event sourcing differences
how to measure CDC replication lag
best practices for CDC and schema evolution
how to make CDC idempotent
how to handle DDL in CDC pipelines
how to secure CDC connectors
how to scale CDC during traffic spikes
how to test CDC pipelines in staging
how to replay CDC events to rebuild state
how to avoid data loss in CDC
best tools for CDC in cloud
CDC monitoring dashboard examples
CDC for analytics vs materialized views
when not to use change data capture
CDC retention and log retention planning
CDC snapshot strategies and best practices
how to build an outbox table for CDC
cost considerations for change data capture
Related terminology
write-ahead log
binlog
logical decoding
snapshotting
tombstone record
compaction
partition key
consumer offset
message bus
stream processing
materialized view
idempotency key
schema evolution
delivery semantics
at-least-once
exactly-once
transaction boundary
replication slot
snapshot isolation
watermarking
backpressure
retention policy
transformation and enrichment
PII masking
audit trail
reconciliation
data lineage
observability signal
SLI SLO CDC
connector health
broker throughput
consumer group lag
compaction policy
CDC operator
managed CDC service
outbox consuption
transactional outbox
schema compatibility
streaming analytics
feature store ingestion
geo replication
cross-region lag