Change Data Capture (CDC) on AWS for Data Engineers

Why CDC Matters in Modern Data Pipelines

Every second, your production databases generate thousands of inserts, updates, and deletes. Traditional batch ETL processes that run nightly or hourly miss the real-time nature of these changes. By the time your data warehouse refreshes, the information is already stale. Change Data Capture solves this problem by capturing and delivering data changes as they happen.

For data engineers on AWS, mastering CDC is essential. It powers real-time analytics, enables event-driven architectures, and keeps downstream systems synchronized with source databases without the overhead of full data reloads.

What is Change Data Capture?

Change Data Capture (CDC) is a design pattern that identifies and tracks changes in data so that action can be taken using the changed data. Rather than reloading entire tables, CDC captures only the delta � inserts, updates, and deletes � and propagates those changes to downstream systems.

Core Benefits

CDC vs Traditional ETL

Traditional ETL extracts entire datasets on a schedule. This approach becomes problematic when tables grow to billions of rows. CDC flips the model � instead of asking "give me everything," it asks "what changed since the last check?"

Traditional ETL:
  Full Extract (2 hours) ? Transform ? Load ? Ready (3+ hours)

CDC Pipeline:
  Capture Changes (seconds) ? Stream ? Ready (near real-time)

CDC Methods Comparison

There are three primary methods for implementing CDC, each with distinct trade-offs:

Log-Based CDC (Recommended)

Log-based CDC reads the database transaction log (WAL in PostgreSQL, binlog in MySQL, redo log in Oracle) to capture changes. Since the database already writes every change to its log for recovery purposes, reading that log adds virtually no overhead to the source system.

How it works:

1. The CDC tool connects to the database as a replication client
2. It reads the transaction log sequentially
3. Each committed transaction is deserialized into row-level operations
4. Changes are streamed to the target in near real-time

Timestamp-Based CDC

This method relies on a last_updated column in each table. Queries filter for rows where the timestamp exceeds the last known value. While simple, it cannot capture deletes unless you implement soft deletes with a deleted_at column.

Trigger-Based CDC

Database triggers fire on INSERT, UPDATE, or DELETE operations and write change records to an audit table. This approach captures all change types but adds significant overhead to every write operation on the source database.

AWS Database Migration Service (DMS)

AWS DMS is the primary managed CDC service on AWS. It supports continuous data replication with built-in CDC capabilities for both homogeneous and heterogeneous database migrations.

Key DMS Features

• Full Load + CDC: Initial bulk copy followed by ongoing change capture
• Change Streams: Real-time capture from transaction logs
• Homogeneous & Heterogeneous: Same DB engine or cross-engine migration
• Continuous Replication: Keep source and target synchronized indefinitely
• Serverless: DMS Serverless automatically provisions capacity

DMS Task Configuration for CDC

When setting up a CDC task in DMS, you configure several critical settings:

Task Settings (CDC-specific):
+-- TargetMetadata
�   +-- SupportLobs: true
�   +-- LimitedSizeLobMode: true
�   +-- LobMaxSize: 32
+-- FullLoadSettings
�   +-- TargetTablePrepMode: DROP_AND_CREATE
�   +-- CreatePkAfterFullLoad: true
+-- ChangeProcessingTuning
�   +-- BatchApplyEnabled: true
�   +-- BatchApplyPreserveTransaction: true
�   +-- BatchSplitSize: 0
+-- ChangeProcessingDdlHandlingPolicy
    +-- HandleSourceTableDropped: true
    +-- HandleSourceTableTruncated: true
    +-- HandleSourceTableAltered: true

DMS CDC Modes

Full Load + CDC is the most common pattern. DMS first performs a bulk copy of all existing data (full load), then automatically transitions to capturing changes from the transaction log.

CDC Only mode skips the initial load and begins capturing changes immediately. Use this when the target already contains a synchronized copy of the data.

Debezium on Amazon MSK

Debezium is an open-source distributed platform for change data capture. Running Debezium on Amazon MSK (Managed Streaming for Apache Kafka) gives you a fully managed, scalable CDC pipeline.

Why Debezium?

While DMS excels at point-to-point database migration, Debezium shines when you need to broadcast changes to multiple consumers. It produces Kafka topics that any number of downstream services can subscribe to independently.

Debezium Connector Configuration

A typical Debezium PostgreSQL connector configuration:

{
  "name": "postgres-cdc-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "mydb.cluster-xxx.us-east-1.rds.amazonaws.com",
    "database.port": "5432",
    "database.user": "debezium",
    "database.password": "${secrets:db-password}",
    "database.dbname": "production",
    "database.server.name": "prod-server",
    "plugin.name": "pgoutput",
    "slot.name": "debezium_slot",
    "publication.name": "debezium_publication",
    "table.include.list": "public.users,public.orders,public.products",
    "topic.prefix": "dbserver1",
    "snapshot.mode": "initial",
    "heartbeat.interval.ms": "10000",
    "tombstones.on.delete": "true"
  }
}

Key Debezium Concepts

• Connector: A Kafka Connect component that reads changes from a specific source
• Task: The actual worker that reads the database log; multiple tasks scale horizontally
• Offset: Tracks the position in the transaction log, enabling restart without data loss
• Schema Registry: Ensures downstream consumers can evolve schemas independently

CDC Pipeline Patterns

Different scenarios call for different CDC patterns. Here are the most common architectures used on AWS:

Pattern 1: Full Load + CDC

The most common starting point. DMS performs an initial full load of all existing data, then transitions to CDC mode to capture ongoing changes. This pattern works well for migrating databases or setting up initial data warehouse populations.

Best for: Database migrations, initial data warehouse loads, one-time syncs.

Pattern 2: Streaming CDC

Debezium captures changes and publishes them to Kafka topics. Multiple consumers subscribe independently, each processing only the changes relevant to them. This fan-out pattern eliminates point-to-point integrations.

Best for: Microservices architectures, event-driven systems, multiple downstream consumers.

Pattern 3: Hybrid

Combines DMS for bulk data warehouse loading with Debezium on MSK for real-time event streaming. This pattern serves both analytical and operational workloads from a single CDC capture point.

Best for: Enterprise architectures needing both batch analytics and real-time event processing.

DMS vs Debezium: When to Use Which?

CDC Performance Tuning

DMS Performance Tips

1. Enable parallel load for large tables with high-volume changes
2. Use batch apply to group multiple change operations into single transactions
3. Configure LOB handling � use limited LOB mode for better performance
4. Select appropriate instance size based on change volume and number of tables
5. Monitor CDCLatencySource and CDCLatencyTarget metrics

Debezium Performance Tips

1. Increase max.batch.size for higher throughput at the cost of memory
2. Tune poll.interval.ms � lower values reduce latency but increase CPU usage
3. Use table-level topic routing to spread load across Kafka partitions
4. Configure heartbeats to prevent slot expansion during low-activity periods
5. Enable SMTs (Single Message Transforms) for lightweight transformations before Kafka

Common CDC Challenges and Solutions

Challenge: Schema Changes

When the source database schema changes (new columns, renamed columns, type changes), CDC must handle these gracefully.

Solution: DMS provides HandleSourceTableAltered setting. Debezium uses Schema Registry with compatibility modes (BACKWARD, FORWARD, FULL) to manage schema evolution.

Challenge: Large Objects (LOBs)

LOBs (BLOB, CLOB, TEXT) can significantly impact CDC throughput.

Solution: In DMS, use LimitedSizeLobMode with a reasonable LobMaxSize. In Debezium, configure column.include.list to exclude LOB columns if not needed downstream.

Challenge: Transaction Ordering

Changes must be applied in the same order they occurred in the source database.

Solution: Both DMS and Debezium maintain transaction ordering by reading the log sequentially. Ensure your target apply mechanism respects transaction boundaries.

Challenge: Handling Deletes

Source databases may hard-delete rows, making it impossible to detect changes via timestamps.

Solution: Use log-based CDC (DMS or Debezium) which captures DELETE operations from the transaction log. Alternatively, implement soft deletes with a deleted_at timestamp column.

Cost Optimization for CDC on AWS

DMS Cost Factors

• Replication Instance Hours: Choose right-sized instances; Serverless mode auto-scales
• Data Transfer: Intra-region transfer is free; cross-region incurs charges
• Storage: Minimal; DMS uses local storage for replication logs

MSK + Debezium Cost Factors

• MSK Broker Hours: Scale brokers based on throughput needs
• MSK Storage: Kafka retains data based on retention policy; tune retention.ms
• Connector Workers: Run on MSK Connect (managed) or self-managed EC2/ECS

Cost Comparison Scenario

For a database with 100 GB initial load and 1 GB/hour of ongoing changes:

Architecture Flow

Interview Q&A

Q1: What is Change Data Capture and why is it important?

Answer: Change Data Capture (CDC) is a pattern that identifies and tracks changes in data at the source, then propagates those changes to downstream systems. It is important because it enables near real-time data synchronization without the overhead of full data reloads, reduces latency in analytics pipelines, minimizes source database load, and decouples producers from consumers in data architectures.

Q2: Explain the difference between log-based and timestamp-based CDC.

Answer: Log-based CDC reads the database transaction log (WAL, binlog, redo log) to capture every committed change including inserts, updates, and deletes. It has minimal overhead since the database already writes to its log. Timestamp-based CDC queries for rows where updated_at exceeds the last known value. It is simpler to implement but cannot capture deletes without soft-delete patterns, requires a timestamp column, and adds query load to the source database.

Q3: When would you choose AWS DMS over Debezium, and vice versa?

Answer: Choose DMS when you need a fully managed solution for database migration or syncing a single source to a single target (like loading a data warehouse). DMS is simpler to operate and integrates natively with RDS, Aurora, and Redshift. Choose Debezium on MSK when you need to broadcast changes to multiple consumers independently, require schema evolution management, or are building event-driven microservice architectures where the fan-out pattern is essential.

Q4: How does DMS handle the initial full load before starting CDC?

Answer: DMS performs the full load by reading source tables in bulk using SELECT queries (typically ordered by primary key). During this phase, it caches any changes that occur. Once the full load completes, DMS applies the cached changes and then transitions to reading the transaction log for ongoing CDC. The TargetTablePrepMode setting controls whether target tables are created, dropped, or truncated before the full load.

Q5: What are the key challenges when implementing CDC at scale?

Answer: Key challenges include: (1) Schema evolution � source schema changes must be handled without breaking downstream consumers; (2) LOB handling � large objects impact throughput; (3) Transaction ordering � changes must be applied in source order; (4) Delete detection � timestamp-based methods miss deletes; (5) Monitoring � CDC lag and error detection require proper metrics; (6) Idempotency � downstream applies must handle duplicate delivery gracefully; (7) Hot tables � tables with very high write volumes may cause CDC backlog.

Q6: How does Debezium ensure exactly-once delivery semantics?

Answer: Debezium itself provides at-least-once delivery. It tracks offsets (position in the transaction log) and commits them to Kafka after successful processing. For exactly-once semantics, the downstream consumer must implement idempotent processing � for example, using the change event's primary key and transaction ID to detect and skip duplicates. Kafka's transactional producer API can also be used to achieve exactly-once within the Kafka ecosystem.

Q7: Describe a production CDC monitoring setup on AWS.

Answer: A comprehensive monitoring setup includes: CloudWatch alarms on DMS CDCLatencySource and CDCLatencyTarget metrics to detect replication lag; MSK metrics for UnderReplicatedPartitions and ActiveProducerCount; custom metrics via Lambda or CloudWatch agent for end-to-end latency; SNS notifications for task failures; CloudWatch Logs Insights for error pattern analysis; and AWS X-Ray for tracing change events through the pipeline.

Q8: How would you handle a scenario where CDC falls behind due to a traffic spike?

Answer: First, identify the bottleneck: is it source database log generation, CDC processing capacity, or target apply rate? For DMS, scale up the replication instance or switch to DMS Serverless. For Debezium, add Kafka Connect workers or MSK brokers. Implement backpressure mechanisms to throttle non-critical consumers. Consider table-level parallelization for hot tables. Long-term, implement adaptive scaling policies based on CDC lag metrics.

Q9: What is the role of a Schema Registry in a CDC pipeline?

Answer: A Schema Registry stores the schema (Avro, JSON Schema, Protobuf) for each CDC event topic. It provides: schema versioning for evolution tracking; compatibility checking (BACKWARD, FORWARD, FULL) to prevent breaking changes; centralized schema management for producers and consumers; and the ability for consumers to deserialize messages from any schema version within the compatibility window.

Q10: Compare DMS Serverless with provisioned DMS for CDC workloads.

Answer: DMS Serverless automatically provisions and scales replication capacity based on workload. It is ideal for variable or unpredictable CDC workloads, reducing operational overhead and cost during low-activity periods. Provisioned DMS gives you explicit control over instance type and capacity, which can be more cost-effective for steady-state high-throughput workloads. Serverless charges by DCU (Data Processing Units) consumed, while provisioned charges by instance hours. For production CDC with consistent high volume, provisioned may be more predictable; for bursty workloads, Serverless offers better elasticity.

Summary

This topic covered the key concepts of AWS data engineering. Review the architecture diagrams, practice the interview questions, and understand the trade-offs between different service options.

Next Steps

Continue to the next topic to build on your AWS data engineering knowledge.

Knowledge Check

See Also

• AWS DMS
• AWS Glue
• Streaming Pipeline