In the modern data landscape, batch processing is no longer sufficient for organizations that need real-time insights and immediate data synchronization. Change Data Capture (CDC) has become the gold standard for capturing and streaming database changes, and Debezium has established itself as the most robust open-source platform for implementing CDC at scale.

At DataBolt Technologies, we've built dozens of CDC pipelines using Debezium across various databases and use cases. In this post, I'll share the practical lessons we've learned about building reliable, production-grade CDC systems that won't keep you up at night.

What Makes CDC with Debezium Special?

Before diving into implementation details, let's understand why Debezium has become the go-to choice for CDC pipelines. Unlike query-based CDC approaches that poll databases for changes, Debezium reads directly from database transaction logs—the same logs databases use for replication and recovery.

This log-based approach offers several critical advantages:

Debezium connectors are built on Kafka Connect, which means you inherit the reliability, scalability, and ecosystem that Kafka provides. This architectural decision is one of Debezium's greatest strengths.

Architecture Fundamentals

A typical Debezium pipeline consists of several components that work together:

The source database generates transaction log entries as part of normal operations. For PostgreSQL, this is the Write-Ahead Log (WAL); for MySQL, it's the binlog; for MongoDB, the oplog. Each database has its own logging mechanism, and Debezium provides specialized connectors that understand these formats.

The Debezium connector runs within Kafka Connect and continuously reads from the transaction log, parsing entries and transforming them into structured change events. These connectors maintain their position in the log and handle reconnections gracefully.

Apache Kafka serves as the durable message queue where change events are published. Each database table typically maps to one or more Kafka topics, providing natural partitioning and parallelism.

Finally, your downstream consumers—whether they're microservices, analytics systems, or other databases—subscribe to these topics and process changes in real-time.

Setting Up for Success: Database Configuration

One of the most common mistakes we see is jumping straight into Debezium configuration without properly preparing the source database. Database setup is critical for reliability.

PostgreSQL Configuration

For PostgreSQL, you need to enable logical replication. In your postgresql.conf, set:

wal_level = logical
max_wal_senders = 10
max_replication_slots = 10

You'll also need to create a replication slot and publication for the tables you want to capture:

CREATE PUBLICATION debezium_pub FOR TABLE users, orders, products;
SELECT pg_create_logical_replication_slot('debezium_slot', 'pgoutput');

The replication slot is crucial—it ensures PostgreSQL retains WAL data even if Debezium goes offline temporarily. However, this also means you need monitoring in place to prevent unbounded WAL growth if your connector stays down too long.

MySQL Configuration

MySQL requires binlog to be enabled with the correct format:

server-id = 123456
log_bin = mysql-bin
binlog_format = ROW
binlog_row_image = FULL

The ROW format is essential—statement-based replication won't give you the detailed change information Debezium needs. Create a dedicated user with replication permissions:

CREATE USER 'debezium'@'%' IDENTIFIED BY 'secure_password';
GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'debezium'@'%';

Connector Configuration Best Practices

Here's where many CDC implementations go wrong. The default Debezium configuration works for demos, but production systems need careful tuning. Let's look at a real-world PostgreSQL connector configuration:

{
  "name": "inventory-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "postgres.example.com",
    "database.port": "5432",
    "database.user": "debezium",
    "database.password": "${file:/secrets/db-password.txt:password}",
    "database.dbname": "inventory",
    "database.server.name": "inventory_prod",
    "plugin.name": "pgoutput",
    "publication.name": "debezium_pub",
    "slot.name": "debezium_slot",
    "table.include.list": "public.users,public.orders,public.products",
    "heartbeat.interval.ms": "60000",
    "snapshot.mode": "initial",
    "decimal.handling.mode": "precise",
    "time.precision.mode": "adaptive",
    "tombstones.on.delete": "true",
    "max.batch.size": "2048",
    "max.queue.size": "8192"
  }
}

Let me highlight the critical settings:

Heartbeat intervals are essential for tables with infrequent updates. Without heartbeats, your replication slot position won't advance, and you might accumulate unnecessary WAL data. Set heartbeat.interval.ms to something reasonable like 60 seconds.

Snapshot mode determines what happens on first startup. The initial mode takes a consistent snapshot of existing data before streaming changes. For large tables, consider initial_only or schema_only depending on your requirements.

Table filtering is critical. Use table.include.list rather than capturing everything. Being selective reduces resource usage and makes your pipeline easier to reason about.

Handling the Initial Snapshot

The initial snapshot is often the most challenging phase of a CDC pipeline. For large databases, snapshots can take hours or days, and you need strategies to handle this gracefully.

First, consider whether you actually need a snapshot. If you're starting a new project, you might be fine with schema_only mode and only capturing changes going forward. But for most migrations, you need that historical data.

For large tables, use snapshot.select.statement.overrides to add custom WHERE clauses that limit the snapshot scope. For example:

"snapshot.select.statement.overrides": "public.orders",
"snapshot.select.statement.overrides.public.orders": "SELECT * FROM public.orders WHERE created_at >= '2024-01-01'"

Monitor snapshot progress through Kafka Connect's REST API. The connector emits metrics that tell you how far along the snapshot is, which is invaluable for large tables.

Monitoring and Alerting

A CDC pipeline without monitoring is a disaster waiting to happen. You need visibility into several key metrics:

We expose these metrics to Prometheus and alert when lag exceeds thresholds or connectors fail. For PostgreSQL specifically, monitor the replication slot size:

SELECT slot_name, pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)) AS retained_wal
FROM pg_replication_slots;

If this grows unbounded, you'll eventually run out of disk space—a common production issue we've encountered.

Schema Evolution and Breaking Changes

Databases evolve. Tables get new columns, data types change, and sometimes you need to restructure entirely. Debezium handles many schema changes gracefully, but you need to understand its behavior.

Adding nullable columns? No problem—Debezium captures the change and downstream consumers see the new field. Dropping columns? Also handled, though downstream systems need to tolerate missing fields.

But data type changes and column renames can be problematic. When in doubt, test schema changes in a staging environment first. Use Debezium's schema history topic to understand how schema evolution is being tracked.

For breaking changes, consider a blue-green approach: create new tables, dual-write temporarily, switch over consumers, then deprecate the old tables. It's more work upfront but prevents production incidents.

Lessons from Production

After running Debezium in production for years, here are the hard-won lessons we always share:

Start small and expand gradually. Don't try to capture your entire database on day one. Begin with a few critical tables, build confidence, then expand.

Plan for connector restarts. Kafka Connect can restart connectors for various reasons. Your downstream consumers must handle duplicate messages—idempotency is not optional.

Disk space kills pipelines. Whether it's WAL accumulation on PostgreSQL or Kafka topic retention, running out of disk is the most common failure mode. Monitor aggressively.

Test failure scenarios. What happens if your database goes down? If Kafka is unavailable? If the connector pod crashes? Test these scenarios before they happen in production.

Document your setup. Future you (or your colleagues) will thank you for documenting database permissions, replication slot names, and connector configurations.

Conclusion

Building reliable CDC pipelines with Debezium is entirely achievable, but it requires understanding both Debezium's capabilities and your source database's behavior. The log-based approach gives you reliability and completeness that query-based solutions simply cannot match.

Focus on proper database configuration, thoughtful connector settings, comprehensive monitoring, and planning for failure scenarios. With these foundations in place, Debezium provides a robust platform for real-time data integration that scales with your organization's needs.

The investment in setting up CDC properly pays dividends in reduced latency, improved data quality, and the ability to build truly real-time data products. Start with the practices outlined here, iterate based on your specific requirements, and you'll build CDC pipelines that are both reliable and maintainable.