A few years ago, setting up real-time database replication meant buying an expensive enterprise software license or writing brittle polling scripts that dragged your production servers to a crawl. Today, if you ask a data engineer how they handle real-time syncs, they’ll likely point to Debezium.
As databases grow distributed and the demand for real-time analytics spikes, keeping downstream systems updated without breaking the source database has become a massive engineering problem.
Debezium solves the capture part of that problem. It’s the open-source standard for turning database transactions into event streams. But you still need to handle the downstream processing of those events and manage the infrastructure that keeps Debezium running — and that’s where the real complexity lies.
This article explores what Debezium is used for, how its architecture works, the three ways to deploy it, and why managing that infrastructure yourself might be an unnecessary tax on your engineering team.
What is Debezium, and why does it matter for CDC pipelines?
To understand Debezium, you must first understand the problem it solves: change data capture (CDC). CDC is the process of identifying and tracking row-level changes, such as inserts, updates, and deletes, in a database so that downstream systems can react to them immediately.
Debezium is an open-source, distributed platform built specifically for this purpose. Instead of repeatedly querying a database to see what changed since you last checked it, Debezium sits in the background and monitors the database’s transaction logs. When a row changes, Debezium captures that event and streams it forward.
That makes it possible to build event-driven workflows, trigger microservices, or feed real-time analytics dashboards without ever altering the source data model or adding heavy query loads to your production database. A Debezium CDC pipeline solution turns an intermittently updated system of record into a continuous, real-time stream of updates for your data infrastructure.
How Debezium works: Architecture, connectors, and supported databases
Debezium is a collection of distributed services that work together to pull data changes out of your database and push them into a message broker.
Essential architecture components
The standard Debezium architecture relies on four core pieces:
- Connectors: These are database-specific modules. You deploy a specific Debezium connector for MySQL, a different one for PostgreSQL, and so on. Each connector knows exactly how to read the proprietary transaction logs of its respective database.
- Kafka Connect: This is the distributed framework that runs the connectors. It handles deployment, scaling, and fault tolerance. If a worker node dies, Kafka Connect automatically spins up the connector on another node.
- Schema registry: Databases change as columns get added or dropped. The schema registry tracks these structural evolutions so downstream systems know how to interpret the incoming event data.
- Kafka topics: The message queues where Debezium writes the captured changes. By default, each database table maps to one specific Kafka topic.
Core capabilities that define Debezium
What makes Debezium the default choice for open-source CDC is how it handles data:
- Relies entirely on log-based change capture: Because Debezium reads directly from the transaction logs (like the MySQL binlog or PostgreSQL WAL), it adds virtually zero CPU load to the source database.
- Guarantees ordered event streaming: If a user updates their email address and then immediately deletes their account, Debezium ensures those events arrive in that exact sequence.
- Delivers near-real-time capture: From the moment a transaction commits in the database to the moment it hits the message broker, the latency is typically in milliseconds.
The Debezium CDC pipeline
When you put the above components and capabilities together, a standard Debezium architecture pipeline flows like this:
- A transaction commits in the source database, creating a change event in the transaction log.
- The Debezium connector reads that change directly from the log.
- Debezium serializes the event (usually as JSON or Avro) and streams it to an Apache Kafka topic in real time.
- The schema registry notes any structural changes to the payload.
- Downstream consumers subscribe to that Kafka topic and process the changes.
Notice how heavily this architecture depends on Kafka. And Kafka isn’t a database — it’s an event streaming platform, and managing it is essentially a full-time job.
Databases supported by Debezium
Debezium’s popularity is largely due to its broad coverage. It provides stable, production-ready connectors for the most common relational and NoSQL databases. The most commonly used connectors are as follows:
- Debezium MySQL provides full support by reading the binlog. The MariaDB connector uses the same approach.
- Debezium PostgreSQL uses logical decoding plugins, like pgoutput or wal2json, to stream changes.
- Debezium SQL Server reads directly from the SQL Server transaction log.
- Debezium MongoDB captures changes by reading the replica set oplog.
- Debezium Oracle supports CDC via LogMiner for enterprise deployments.
Debezium deployment modes
Most guides assume you’re running Debezium on Kafka Connect. But Debezium actually offers three distinct deployment modes, and choosing the wrong one guarantees a failed implementation.
1. Kafka Connect
This is the classic, recommended deployment. Your Debezium connectors run as native source connectors within a Kafka Connect cluster.
You need a Java runtime, a highly available Kafka broker cluster, and Kafka Connect worker nodes. This mode supports massive scale. And because it’s distributed, you get automatic task rebalancing and fault tolerance. If you’re a large enterprise that already runs Kafka and needs to sync dozens of databases simultaneously, this is a logical choice.
2. Debezium Server
If you want the power of Debezium without managing Kafka, Debezium Server is an alternative.
It’s a stand-alone Java application built on the Quarkus framework that you run as a standard container or JAR file. Instead of requiring Kafka, Debezium Server includes sink adapters that let you stream changes directly to other messaging infrastructures like Amazon Kinesis, Google Cloud Pub/Sub, or Redis Streams. It’s much easier to deploy, but you sacrifice the automatic clustering and failover that Kafka Connect provides.
3. Embedded Engine
The Debezium Engine isn’t a service at all. It’s a Java library API that you embed directly into your applications.
You write the code that creates the Engine instance, configures the connector, and receives the change events via callbacks. Plus, you’re entirely responsible for all operational concerns: offset management, error handling, and state storage. You only use this mode when building a highly specialized replication tool or a microservice that needs to react to database changes without network hops to an external broker.
Automate your CDC pipelines with Fivetran
Debezium is an impressive piece of open-source engineering. But for data teams focused on analytics, it can quickly become more overhead than advantage.
Building a real-time CDC pipeline with Debezium means operating ZooKeeper, Kafka brokers, and schema registries — plus configuring connectors for each database, recovering from offset errors, and fixing downstream breakages when schemas change. It’s a massive infrastructure tax for teams that want to simply replicate a production database into Snowflake, BigQuery, or Databricks for analytics.
Fivetran eliminates that burden entirely. Its native, log-based CDC connectors for PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB read directly from transaction logs with near-zero impact on source database performance. You get Debezium-level latency without provisioning a single Kafka broker or Kafka Connect worker node. The entire pipeline is fully managed.
Schema evolution — where most self-managed CDC pipelines break — is automatic. When columns are added, dropped, or altered in the source, Fivetran propagates those changes to the destination without any manual reconfiguration or pipeline downtime.
For databases outside the standard connector library, Fivetran offers a serverless framework for building and deploying custom connectors. And for teams that need programmatic control, the Fivetran REST API and Terraform provider offer full automation over pipeline creation, scheduling, and monitoring.
When evaluating Debezium alternatives and CDC tools, start with a simple question: Do you want to build and maintain data infrastructure, or do you want to analyze data?
If the answer is the latter, explore Fivetran’s automated connectors and start moving data in minutes.
FAQ
How does Debezium work with SQL Server?
Debezium captures changes from SQL Server by reading the transaction log. It monitors the database for inserts, updates, and deletes, and then streams those row-level changes to a message broker with millisecond latency. This near-real-time replication ensures downstream systems stay perfectly synced with the source.
What is Debezium for PostgreSQL?
Debezium for PostgreSQL is a specific connector that uses PostgreSQL’s native logical decoding feature. By using plugins like pgoutput, it reads the write-ahead log and translates those binary changes into a structured event stream that can be routed to Kafka or other messaging systems.
What is a Debezium connector?
A Debezium connector is a database-specific module that reads directly from a source database’s transaction log and streams row-level changes to a message broker in real time.
