1. Introduction & Overview
What is CDC (Change Data Capture)?
Change Data Capture (CDC) is a design pattern and technology that identifies and tracks changes (inserts, updates, deletes) to data in a source system (usually a database) and ensures those changes are captured and made available for downstream systems. It is primarily used for real-time data synchronization, event-driven architecture, and streaming analytics.
History or Background
- Origin: Originally developed to support ETL (Extract, Transform, Load) workflows in data warehousing.
- Evolution: Grew popular with the rise of stream-processing tools (Kafka, Debezium) and microservices.
- Current Use: Widely used in cloud-native applications, CI/CD pipelines, real-time monitoring, and security auditing.
Why is it Relevant in DevSecOps?
CDC becomes highly relevant in DevSecOps because:
- It enables real-time monitoring of sensitive data changes, enhancing audit and compliance.
- It supports data integrity and replication across environments (dev, staging, production).
- It empowers event-driven security triggers that can flag unauthorized changes.
- It ensures visibility and traceability of data lifecycle events across the SDLC.
2. Core Concepts & Terminology
Key Terms and Definitions
| Term | Definition |
|---|---|
| Change Data Capture (CDC) | A pattern that detects and captures data changes in source systems. |
| Debezium | An open-source CDC platform built on Apache Kafka. |
| Log-based CDC | Captures changes by reading database transaction logs. |
| Trigger-based CDC | Uses database triggers to record changes. |
| Snapshot | The initial full copy of a dataset before capturing incremental changes. |
| Sink | A target system where CDC data is propagated (e.g., Elasticsearch, S3). |
How It Fits Into the DevSecOps Lifecycle
| DevSecOps Stage | Role of CDC |
|---|---|
| Plan | Define compliance policies for data change capture. |
| Develop | Enable CDC for development DBs to simulate production events. |
| Build | Validate that schema changes are safe and tracked. |
| Test | Automate tests to verify data flows from CDC sources. |
| Release | Trigger secure deployments based on critical data events. |
| Operate | Monitor data change events for security or incident response. |
| Monitor | Integrate with SIEM or dashboards for real-time change visibility. |
3. Architecture & How It Works
Components of a CDC System
- Source Connector
Detects changes in the source system (e.g., PostgreSQL, MySQL, MongoDB). - Change Log Processor
Reads database logs or listens to triggers to extract changes. - Transformation Layer
Optional step to enrich, filter, or validate changes. - Sink Connector
Forwards changes to a destination (Kafka, Elasticsearch, data lake, etc.). - Monitoring & Auditing Layer
Logs metadata, ensures compliance, and alerts security tools.
Internal Workflow
- Initial Snapshot: Capture a consistent view of existing data.
- Continuous Capture: Detect and stream all new changes.
- Transformation (optional): Filter PII, normalize schema, or enrich events.
- Delivery to Sink: Changes are pushed to downstream systems.
- Security Hooks: Integrate alerts for anomalies or policy violations.
Architecture Diagram (Descriptive)
+----------------+
| Source DB |
| (MySQL/Postgres)|
+--------+-------+
|
[Change Logs or Triggers]
|
+--------v--------+
| CDC Connector | <--- Debezium / AWS DMS / LogStash
+--------+--------+
|
+--------v--------+
| Kafka/Event Bus | <--- Message broker for stream processing
+--------+--------+
|
+----------------+----------------+
| |
+-------v--------+ +--------v-------+
| Security Engine| | Data Warehouse |
| (SIEM, Splunk) | | (Redshift, BigQuery) |
+----------------+ +----------------+
Integration Points with CI/CD or Cloud Tools
| Tool | Integration |
|---|---|
| Jenkins / GitLab CI | Automate tests to verify correct CDC config before deploy. |
| HashiCorp Vault | Encrypt CDC stream with secrets at runtime. |
| AWS DMS | Managed CDC solution; integrate with AWS pipelines. |
| SIEM Tools (Splunk/ELK) | Push CDC streams to detect anomalies or unauthorized changes. |
| Kubernetes | Deploy CDC connectors as sidecars or services. |
4. Installation & Getting Started
Prerequisites
- Java (for Debezium)
- Apache Kafka
- Docker (for containerized setup)
- Database (e.g., PostgreSQL)
- Access permissions to replication logs or triggers
Step-by-Step: Debezium with PostgreSQL & Kafka
1. Clone Debezium Docker Environment
git clone https://github.com/debezium/docker-images.git
cd docker-images/examples/postgres
2. Start Services
docker-compose up -d
3. Verify Services
docker ps
4. Register a PostgreSQL Source Connector
curl -X POST http://localhost:8083/connectors \
-H "Content-Type: application/json" \
-d '{
"name": "cdc-postgres-connector",
"config": {
"connector.class": "io.debezium.connector.postgresql.PostgresConnector",
"database.hostname": "postgres",
"database.port": "5432",
"database.user": "postgres",
"database.password": "postgres",
"database.dbname": "inventory",
"database.server.name": "dbserver1",
"plugin.name": "pgoutput"
}
}'
5. Listen to Kafka Events
docker exec -it kafka bash
kafka-console-consumer --bootstrap-server localhost:9092 --topic dbserver1.inventory.customers --from-beginning
5. Real-World Use Cases
1. Audit Logging in Financial Systems
- CDC tracks sensitive data changes (e.g., account balances).
- Alerts are sent to SIEM tools for compliance and fraud detection.
2. Data Synchronization Across Environments
- Real-time sync from production to staging (excluding PII).
- Helps in simulating production-like test scenarios securely.
3. Event-Driven Security Triggers
- Unauthorized schema changes trigger rollback or incident response.
- Example: Data deletions in healthcare EHRs flag alerts.
4. DevSecOps Pipeline Verification
- Changes in configuration tables automatically trigger test pipelines.
- Used in container orchestration systems (e.g., Istio policy updates).
6. Benefits & Limitations
Key Advantages
- Real-time visibility into data changes.
- Improved traceability and audit readiness.
- Enhanced automation in CI/CD & monitoring pipelines.
- Scalable and decoupled from core application logic.
Common Limitations
- Overhead on DB systems if not tuned properly.
- Complexity in managing schema evolution.
- Security risks if change logs are not encrypted.
- Tooling lock-in (e.g., vendor-specific CDC in cloud platforms).
7. Best Practices & Recommendations
Security Tips
- Always encrypt data in transit and at rest.
- Mask or exclude PII and sensitive fields before publishing to sinks.
- Set access controls on CDC streams (IAM, ACLs).
Performance
- Use log-based CDC for minimal impact.
- Filter irrelevant tables/columns to reduce noise.
- Batch or throttle high-frequency changes.
Maintenance & Compliance
- Regularly rotate credentials for CDC connectors.
- Align with GDPR, HIPAA by maintaining immutable change logs.
- Audit connector configs during every pipeline build.
8. Comparison with Alternatives
| Feature | CDC (e.g., Debezium) | Polling | Triggers | ETL Tools |
|---|---|---|---|---|
| Real-time | ✅ | ❌ | ✅ | ❌ |
| Overhead | Low (log-based) | High | Medium | High |
| Scalability | High | Low | Medium | Medium |
| DevSecOps Friendly | ✅ | ❌ | ❌ | ❌ |
When to Choose CDC?
- When real-time change tracking is crucial.
- When integrating event-driven automation or security workflows.
- When building auditable systems with regulatory compliance.
9. Conclusion
CDC is a powerful enabler of real-time data flow, visibility, and automation within DevSecOps. It ensures that sensitive changes are tracked, verified, and responded to—automatically and securely.
Future Trends
- AI-based anomaly detection on change streams.
- Policy-as-code for data mutations.
- Cloud-native CDC platforms like Azure Data Factory, Google Datastream.
Official Resources & Community
- Debezium: https://debezium.io
- AWS DMS: https://aws.amazon.com/dms
- Kafka Connect CDC Plugins: https://www.confluent.io
- Reddit Community: r/devops, r/dataengineering