Why You Should Reconsider Debezium: Challenges and Alternatives

Quick answer: What are the best Debezium alternatives in 2026?

The best Debezium alternative depends on what you want to change about your current architecture:

• Estuary: Best for managed real-time CDC, historical backfills, transformations, and multi-destination delivery without operating Kafka infrastructure.
• Confluent Cloud: Best for teams that want to keep a Kafka-centric architecture while using fully managed Debezium-based CDC connectors.
• Striim: Best for enterprise CDC with real-time stream processing and hybrid deployment requirements.
• Apache Flink CDC: Best for engineering teams already operating Flink that want open-source CDC integrated with stream processing.
• Airbyte: Best for teams that want broad open-source data integration and can accept scheduled CDC delivery.
• Qlik Replicate: Best for enterprise and heterogeneous database replication, including legacy systems.
• AWS DMS or Google Cloud Datastream: Best when your CDC workloads are primarily tied to one cloud ecosystem.

Debezium itself remains a strong option when Kafka is already part of your architecture, your team is comfortable operating Kafka Connect and CDC infrastructure, and you want open-source control over change events.

Teams generally look for Debezium alternatives when they want the same database change capture capability with less infrastructure to operate, simpler historical backfills and downstream delivery, or a managed platform that owns more of the pipeline lifecycle.

What is Change Data Capture?

Change Data Capture (CDC) is the process by which you capture all the new events from a source system. Any update, any insert, or other modification to the source database that needs to be ultimately reflected in a downstream application. 

A CDC pipeline will be constructed by data engineers to avoid this potential problem, especially those working in eCommerce, fraud, logistics, and financial services. 

Implementation options range from simply querying the database on a periodic basis (query-based CDC), to complex implementations that stream database changes in real-time. 

What is Debezium? 

Debezium is an open-source change data capture framework that captures row-level database changes and converts them into structured change events.

It is most commonly deployed through Kafka Connect, but Kafka is not a strict requirement. Debezium Server can send change events to systems such as Amazon Kinesis, Google Cloud Pub/Sub, Apache Pulsar, Redis Streams, and NATS JetStream.

How Debezium captures changes depends on the source database. PostgreSQL uses logical replication from the WAL, MySQL reads the binary log, MongoDB uses native change streams, and the SQL Server connector relies on SQL Server's native CDC feature and reads changes from its CDC change tables.

Debezium gives engineering teams significant control over CDC events, routing, retention, and downstream processing. That flexibility also means teams should evaluate the infrastructure and operational work around Debezium, not just the CDC connector itself.

The Challenges with Debezium 

Debezium is a popular open-source choice for streaming change data from databases like PostgreSQL, MySQL, and MongoDB, commonly through Kafka and on to downstream consumers. It was one of the first open-source CDC frameworks to gain popularity, and thousands of data teams large and small still choose Debezium and contribute to the project.

But Debezium also comes with operational challenges that can require significant engineering time and expertise when running it at scale, namely:
 

1. Debezium and its surrounding infrastructure require specific expertise. Teams using the common Kafka Connect deployment model need to understand Kafka, Kafka Connect, connector configuration, offsets, schema history, monitoring, and recovery. Modern Kafka no longer requires ZooKeeper, which reduces some of the infrastructure burden, but operating a self-managed Kafka-based CDC stack still requires specialized knowledge.

3. Large tables and high change volumes require careful capacity planning.

There are a few challenges that can arise when scaling Debezium:

• Large DML operations can generate significant backlogs and take time to process. There have been cases where operations affecting tens of millions of rows took hours to finish processing.
• Debezium now supports incremental snapshots, which divide tables into chunks, can run alongside ongoing change capture, and can resume after interruption. However, large-table snapshots still require planning around chunk size, database load, signaling, and log retention.
• Throughput depends on transaction size, event size, database workload, Kafka capacity, connector configuration, and downstream consumers. Teams should benchmark their own workload rather than rely on a single events-per-second number.
• Connector failover and recovery still need to be planned. Kafka Connect distributed mode can move connector tasks between workers, but database-specific state such as replication slots and transaction-log positions also needs to remain available.
• PostgreSQL logical decoding and replication slots require monitoring so that a stalled or disconnected connector does not cause unnecessary WAL retention.

4. Debezium provides at-least-once delivery by default. This means duplicate events can occur during some failures or restarts. Supported Debezium source connectors can also participate in Kafka Connect's exactly-once delivery mode when configured appropriately. Teams should verify delivery guarantees across the complete source-to-destination pipeline, not only at the capture layer.

5. Complex schema changes still require careful handling. Debezium captures row-level changes and maintains schema information, but schema evolution behavior varies by source connector and downstream system.

For example:

• Primary-key changes affect event keys. In PostgreSQL, Debezium represents a primary-key change as a delete for the old key followed by a create event for the new key, so downstream consumers must handle that behavior correctly.
• Schema changes can alter the structure of events consumed downstream, so teams need to make sure serializers, schemas, and consumers remain compatible. Some database-specific changes can also require additional connector or source configuration.

6. Backfills require planning. Debezium supports initial and incremental snapshots, including on-demand incremental snapshots for previously captured tables. However, teams still need to configure and operate snapshot signaling, chunk sizes, source permissions, log retention, and downstream handling. If historical events must be retained for additional consumers, Kafka retention or log compaction also needs to be considered.

7. Transformations are limited at the connector layer. Debezium and Kafka Connect support Single Message Transformations, which are useful for operations such as modifying fields, flattening records, or routing events. For stateful transformations such as streaming joins or aggregations across multiple records, an additional processing system such as Flink or Kafka Streams is generally required.

If your team already operates Kafka and has the engineering resources to manage this infrastructure, these trade-offs may be completely reasonable. Debezium remains a strong choice for teams that value open-source control over their CDC and event-streaming architecture.

For teams that primarily want to move database changes to warehouses, lakes, search systems, or operational databases without managing the surrounding streaming infrastructure, a managed CDC platform may be a better fit.

Alternatives to Debezium

There are several alternatives to Debezium for capturing and delivering database changes in real time. The right choice depends on whether you want to keep Kafka, reduce operational work, use an open-source streaming framework, or move to a fully managed CDC platform.

The table below focuses on representative alternatives rather than every CDC product available:

There are other options for more specialized requirements. Qlik Replicate and Oracle GoldenGate are commonly used for enterprise database replication, while AWS DMS and Google Cloud Datastream fit cloud-specific replication workloads. Broader data integration platforms such as Airbyte may also be worth considering when scheduled CDC delivery is sufficient.

Estuary as a Debezium Alternative

Estuary is a real-time data integration platform that combines CDC, streaming, and batch data movement.

For supported databases, Estuary uses transaction logs or database-native change mechanisms to capture inserts, updates, and deletes without repeatedly querying production tables. Captured data is stored in durable collections that can feed transformations and multiple destinations without rereading the source independently for every target.

Estuary collections are backed by journals from the open-source Gazette project, while Estuary's runtime manages transactional processing, scaling, and recovery. Collections provide durable cloud-backed storage for captured data.

Estuary vs. Debezium

Debezium provides at-least-once delivery by default, although supported source connectors can participate in Kafka Connect's exactly-once delivery mode when configured appropriately.

With Estuary, delivery semantics depend on the destination. Supported transactional materializations can commit destination updates and processing checkpoints together, providing exactly-once semantics within that materialization boundary.

For either platform, evaluate delivery guarantees across the complete path to your actual destination rather than comparing capture-layer terminology alone.

• Scaling and recovery

Both Debezium and Estuary can support high-volume CDC, but the operational models differ.

With Debezium, teams are responsible for sizing and monitoring connector workers, Kafka infrastructure, transaction-log retention, offsets, and downstream consumers. Estuary manages the streaming runtime, connector infrastructure, recovery, and task scaling as part of the service, reducing the amount of capacity planning and infrastructure management required from users.

The important comparison is therefore not a single events-per-second number, but how much capacity planning, backlog management, and recovery your team needs to operate itself.

• Schema changes

Debezium maintains schema history and can emit schema-change events, but the exact behavior and operational process depend on the database connector and downstream consumers.

Every Estuary collection has a JSON Schema that defines its structure and constraints. Estuary validates documents against these schemas and provides schema-evolution workflows for changes to fields, types, keys, and partitions.

Compatible schema changes can often be incorporated automatically through schema discovery and AutoDiscover. When configured, AutoDiscover can also re-version incompatible collections and trigger required downstream updates or backfills. Changes to keys, partitions, or incompatible field types may still require a collection evolution or backfill.

• Backfills

Debezium supports initial and incremental snapshots. Incremental snapshots can run alongside ongoing CDC and can be triggered for selected tables, but teams still manage the snapshot configuration, signaling, chunk sizes, source permissions, log retention, and downstream handling.

Estuary treats backfills as part of the managed dataflow. Source data can be incrementally recaptured into collections, while destination tables can also be rebuilt directly from data already stored in collections without rereading the production source.

This is especially useful when you need to refresh one destination or add another destination without repeating the original source capture.

• Transforms

Debezium and Kafka Connect support Single Message Transformations for lightweight operations on individual events. Stateful operations such as joins and aggregations generally require an additional processing layer such as Kafka Streams or Flink.

Estuary supports stateful streaming transformations within the platform using SQL and TypeScript, with Python available on Private and BYOC deployments. These derivations can filter, join, aggregate, enrich, and restructure captured data before it is delivered to one or more destinations.

Your Next Step in Streaming Change Data Capture

Debezium remains a strong open-source CDC option, especially for teams already operating Kafka and Kafka Connect. But if your goal is to reduce infrastructure management, simplify backfills and recovery, or deliver database changes directly to downstream systems, a managed CDC platform may be a better fit.

Before choosing an alternative, compare the platforms using your actual source and destination, including latency, source-database impact, recovery behavior, schema evolution, backfills, deployment requirements, and total operational cost.

Estuary offers a permanent free plan with up to two connectors and 10 GB of data movement per month, so you can test a real-time data pipeline with your own workload before committing to a paid plan.

For a broader comparison, see our guide to the best CDC tools. If you have already narrowed your shortlist, use our CDC solution evaluation guide to test the options under production-like conditions.

Getting Started!

Use these resources:

• Get started for free.
  
• Estuary Docs.
  
• Tutorial: streaming Postgres CDC to Elasticsearch through Estuary (many more tutorials are on the blog!)