Change Data Capture Tools: 10 Best Options Compared

These are the best change data capture tools for real-time data pipelines:

1. Tinybird
2. Debezium
3. AWS Database Migration Service (DMS)
4. Google Cloud Datastream
5. Fivetran
6. Airbyte
7. Confluent CDC Connectors
8. Oracle GoldenGate
9. Qlik Replicate
10. Maxwell's Daemon

When you need to capture database changes and propagate them to analytical systems, Change Data Capture (CDC) has become the industry standard. Instead of re-extracting entire tables in expensive batch jobs, CDC captures only the deltas—inserts, updates, and deletes—and streams them with minimal latency.

CDC's core appeal is efficiency: lower impact on source databases, fresher data in analytical systems, and the foundation for event-driven architectures that respond to changes in real-time.

But choosing the right CDC tool involves more than just capturing changes. You need to consider delivery guarantees, snapshot and backfill strategies, schema evolution handling, delete representation, and operational complexity. The difference between a successful CDC pipeline and a production nightmare often comes down to these details.

Teams evaluating CDC tools typically fall into three categories: those building real-time analytics pipelines, those implementing data replication for disaster recovery, and those creating event-driven microservices architectures.

We evaluate each tool based on capture mechanisms, delivery semantics, operational complexity, and integration capabilities to help you choose the right solution for your specific needs.

Need to turn CDC streams into real-time analytics APIs?

If you're implementing CDC to power real-time dashboards, user-facing analytics, or operational intelligence, consider Tinybird. It's a real-time data platform built on ClickHouse® that can ingest CDC streams from Kafka, webhooks, or direct connections and transform them into instant HTTP APIs. No complex ETL pipelines, just SQL queries that become production-ready endpoints in seconds.

1. Tinybird: Real-Time Analytics Platform for CDC Destinations

Before diving into CDC capture tools, let's address where those captured changes should go—and how to turn them into actionable analytics.

Tinybird isn't a CDC capture tool—it's the ideal destination for CDC streams. As a real-time data platform built on ClickHouse®, Tinybird handles the ingestion, transformation, and API publication of change events in one integrated service. If your goal is real-time analytics from database changes, Tinybird completes the CDC pipeline that capture tools start.

The Missing Piece in Most CDC Architectures

Most CDC discussions focus on capturing changes—but capturing is only half the problem. Once you have a stream of inserts, updates, and deletes, you need to:

• Materialize current state from the change stream
• Handle deduplication for at-least-once delivery
• Process deletes appropriately for analytical queries
• Serve queries with sub-second latency at scale

Traditional data warehouses weren't designed for these patterns. They expect batch loads, not continuous streams. They struggle with high-frequency updates. And they can't serve user-facing applications with the latency requirements modern products demand.

This limitation highlights the architectural differences between batch-oriented data warehouses and streaming-first analytical systems.

Purpose-Built for Streaming Ingestion

Tinybird connects directly to Kafka topics where CDC tools like Debezium publish changes. Data flows continuously into ClickHouse®-powered storage and becomes immediately queryable.
This ingestion approach aligns closely with modern real-time data processing practices, ensuring minimal latency and consistent freshness across analytical endpoints.

The platform handles CDC event semantics naturally:

• Upserts via ReplacingMergeTree engines
• Delete handling through soft deletes or filtered views
• Schema evolution with managed migrations
• Exactly-once semantics through idempotent writes

You don't build custom consumers or manage Kafka Connect sinks. Tinybird is the sink—optimized specifically for analytical queries on streaming data.

Instant APIs from CDC Data

One of Tinybird's most powerful features is the instant API layer. Write a SQL query over your CDC-derived data, publish it as a secure HTTP endpoint with one click. No backend service to build, no API framework to maintain, no infrastructure to scale.

For teams building operational dashboards, customer-facing analytics, or real-time monitoring from database changes, this capability saves months of development time.

Fully Managed Infrastructure

With most CDC architectures, you manage capture tools, message brokers, stream processors, and analytical databases—each requiring separate expertise and monitoring. This operational burden often increases with hybrid or cloud computing environments, where scalability and cost-efficiency must be carefully balanced.

Tinybird collapses this complexity. Connect your CDC stream, write SQL transformations, publish APIs. Automatic scaling, built-in high availability, SOC 2 Type II compliance, and expert support come standard.

When Tinybird Makes Sense

Tinybird is ideal when:

• Your CDC goal is real-time analytics, not just replication
• You need sub-100ms query latency on fresh data
• You want instant APIs from database changes
• Operational simplicity matters more than maximum flexibility
• You're building user-facing features powered by CDC

2. Debezium: The Open-Source CDC Standard

Debezium has become the de facto standard for open-source CDC, providing log-based capture from major databases through Kafka Connect.

Log-Based Capture Architecture

Debezium reads transaction logs directly—PostgreSQL's WAL, MySQL's binlog, SQL Server's transaction log, Oracle's redo logs. This log-based approach has minimal impact on source databases compared to trigger-based or polling alternatives.

Each change becomes a structured event with rich metadata: operation type, before and after states, source information, and transaction context. This event structure enables sophisticated downstream processing across both upstream and downstream systems.

Kafka Connect Integration

Debezium runs as Kafka Connect source connectors, publishing changes to Kafka topics. This integration provides:

• Distributed, fault-tolerant execution
• Offset management for exactly-once source semantics
• Schema Registry integration for typed events
• Extensive sink ecosystem for destinations

For teams already running Kafka, Debezium fits naturally into existing infrastructure.

Critical Production Considerations

Debezium's event format requires understanding for proper consumption. Events include before and after states, with tombstones (null values) for deletes that enable log compaction in Kafka.

Snapshot handling is crucial: Debezium performs an initial consistent snapshot before streaming, and supports incremental snapshots for adding tables or backfilling without restarting connectors.

Schema evolution flows through the schema history topic, tracking DDL changes so events can be properly deserialized. If history becomes inconsistent, connectors can fail on new tables.

When Debezium Fits

Consider Debezium when:

• You want open-source CDC with community support
• Kafka is your event backbone
• Your team has Kafka Connect operational expertise
• You need maximum flexibility in event processing
• Multi-database capture is required

3. AWS Database Migration Service: Managed CDC for AWS

AWS DMS provides managed CDC capabilities for database migration and ongoing replication, deeply integrated with the AWS ecosystem.

Full Load Plus Ongoing Replication

DMS supports full load (initial migration) combined with ongoing replication (CDC) to keep targets synchronized. This pattern enables zero-downtime migrations and continuous data pipelines to analytical systems.

The service handles heterogeneous migrations—different database engines between source and target—making it valuable for modernization projects.

AWS Ecosystem Integration

DMS integrates natively with RDS, Aurora, Redshift, S3, and Kinesis. For AWS-centric architectures, this simplifies connectivity and security configuration compared to self-managed solutions.

IAM-based access control and VPC networking follow standard AWS patterns, reducing operational learning curve for teams already on AWS.

Operational Simplicity vs. Flexibility

DMS abstracts replication instance management, task configuration, and monitoring. You don't operate Kafka clusters or manage connector deployments.

The trade-off: less control over event format, delivery semantics, and transformation logic. DMS is opinionated about how CDC works, which simplifies operations but limits customization.

When AWS DMS Fits

Consider AWS DMS when:

• AWS is your primary cloud platform
• You're doing database migrations with CDC
• Managed operations are preferred over flexibility
• Targets are AWS services like Redshift or S3
• You don't need fine-grained event control

While AWS DMS simplifies CDC management, it still depends on the structure and performance of the underlying database. Understanding database behavior remains essential to achieving stable, low-latency replication pipelines.

4. Google Cloud Datastream: Serverless CDC

Google Cloud Datastream provides serverless CDC with automatic scaling and tight GCP integration.

Serverless Architecture

Datastream scales automatically based on change volume—no capacity planning or instance sizing required. You pay for data processed, not provisioned infrastructure.

For teams wanting to avoid CDC operations entirely, this removes significant burden.

GCP Ecosystem Focus

Datastream targets GCP services: BigQuery, Cloud SQL, Cloud Storage, and Pub/Sub. For GCP-native architectures, the integration is seamless.

The service handles backfills, schema changes, and ongoing synchronization with minimal configuration.

Limitations to Consider

Datastream's source support is narrower than Debezium—primarily MySQL, PostgreSQL, Oracle, and AlloyDB. If you have diverse database estates, you may need multiple CDC solutions.

Event transformation capabilities are limited compared to Kafka Connect's SMT ecosystem. Complex routing or enrichment requires additional processing layers.

When Datastream Fits

Consider Google Cloud Datastream when:

• GCP is your primary cloud platform
• Serverless operations are a priority
• Targets are GCP services like BigQuery
• Source databases are MySQL, PostgreSQL, or Oracle
• You want minimal CDC infrastructure

5. Fivetran: SaaS CDC for Data Teams

Fivetran provides managed CDC as part of its broader ELT platform, targeting teams who want connect-and-go simplicity.

Log-Based Replication

Fivetran's CDC uses log-based capture where supported—reading transaction logs asynchronously with minimal source impact. Changes flow to data warehouses with configurable sync frequencies.

The service abstracts connector configuration, schema mapping, and incremental loading—data teams define sources and destinations, Fivetran handles the mechanics.

Warehouse-Centric Model

Fivetran targets analytical warehouses: Snowflake, BigQuery, Redshift, Databricks. The model assumes you're building analytics, not event-driven systems.

History mode options let you choose between current state (upsert) and append-only (full history) tables—important for analytics that need point-in-time queries.

Operational Simplicity vs. Control

Fivetran eliminates CDC operations almost entirely. No Kafka clusters, no connector management, no schema registry maintenance.

The cost: less flexibility in event routing, transformation, and delivery semantics. Pricing scales with data volume, which can become expensive at scale.

When Fivetran Fits

Consider Fivetran when:

• Data warehouse is your analytical target
• Operational simplicity is the top priority
• You're building analytics, not event systems
• Budget allows for SaaS pricing at your scale
• Speed to value matters more than customization

6. Airbyte: Open-Source ELT with CDC

Airbyte provides open-source ELT with CDC capabilities, often using Debezium under the hood for log-based capture.

Open-Source Foundation

Airbyte's open-source core gives you full visibility into connector implementation and the option to self-host for cost control or compliance requirements.

The project has rapid connector development, with community contributions expanding source and destination coverage.

CDC Implementation

Many Airbyte CDC connectors use Debezium internally, providing log-based capture with familiar semantics. The platform handles connector orchestration, state management, and destination loading.

Delete handling is explicit: Airbyte typically marks deleted rows with metadata columns rather than removing them, preserving audit history in destinations.

Cloud and Self-Hosted Options

Airbyte offers both Airbyte Cloud (managed) and self-hosted deployment. Self-hosting reduces costs but requires operational investment in Kubernetes, databases, and monitoring.

The connector ecosystem is large but quality varies—production-critical connectors may need testing and validation.

When Airbyte Fits

Consider Airbyte when:

• Open-source is important for your organization
• You want self-hosting options for cost or compliance
• Connector coverage meets your source needs
• You're building ELT pipelines, not event systems
• Budget constraints favor open-source over SaaS

7. Confluent CDC Connectors: Managed Kafka CDC

Confluent provides managed Debezium connectors as part of Confluent Cloud, combining Debezium's capabilities with managed Kafka infrastructure.

Debezium on Managed Infrastructure

Confluent's CDC connectors are Debezium-based, providing the same log-based capture, event format, and source support. The difference: Confluent manages everything—Kafka brokers, Connect workers, Schema Registry.

This eliminates Kafka operations while preserving Debezium's flexibility and ecosystem.

Enterprise Features

Confluent adds enterprise capabilities: enhanced security, audit logging, RBAC, and support SLAs. For organizations with compliance requirements, these features matter.

Schema Registry is fully managed, with compatibility enforcement and schema evolution handling built in.

Cost Considerations

Confluent Cloud pricing combines compute, storage, and networking costs. At scale, costs can exceed self-managed Kafka significantly—evaluate carefully for high-volume CDC.

The operational savings may justify premium pricing for teams without Kafka expertise.

When Confluent Fits

Consider Confluent CDC when:

• You want Debezium capabilities without operations
• Managed Kafka aligns with your strategy
• Enterprise features (security, support) matter
• Budget accommodates premium managed pricing
• Kafka ecosystem is your event backbone

8. Oracle GoldenGate: Enterprise CDC Standard

Oracle GoldenGate is the enterprise standard for CDC in Oracle environments, with decades of production proven deployment.

Log-Based Real-Time Capture

GoldenGate provides log-based CDC with real-time delivery and minimal source impact. The architecture supports high-availability configurations with conflict detection and resolution.

For Oracle-to-Oracle replication, GoldenGate is unmatched in capabilities and vendor support.

Heterogeneous Support

Beyond Oracle, GoldenGate supports heterogeneous replication: Oracle to Kafka, PostgreSQL, MySQL, SQL Server, and various targets. This makes it viable for mixed-database estates.

Transformation capabilities during replication enable data filtering, column mapping, and format conversion.

Enterprise Complexity

GoldenGate requires significant expertise to deploy and operate. The licensing model is complex and expensive. Configuration involves multiple components with intricate dependencies.

For Oracle shops with dedicated DBA teams, this investment makes sense. For others, simpler alternatives may be more practical.

When GoldenGate Fits

Consider Oracle GoldenGate when:

• Oracle databases are primary sources
• Enterprise support and SLAs are required
• High-availability replication is critical
• Your organization has Oracle licensing agreements
• Dedicated database teams can manage complexity

9. Qlik Replicate: Enterprise CDC Platform

Qlik Replicate (formerly Attunity) provides enterprise CDC with broad source support and high-performance capture.

High-Performance Architecture

Qlik Replicate emphasizes low-latency, high-throughput CDC with minimal source impact. The architecture handles high change volumes that stress simpler tools.

Parallel processing and optimized capture make it suitable for enterprise-scale workloads.

Broad Source Coverage

Qlik Replicate supports extensive source databases: mainframes, Oracle, SQL Server, MySQL, PostgreSQL, SAP, and more. For heterogeneous enterprise environments, this breadth is valuable.

The platform handles both log-based and trigger-based CDC, choosing the optimal method per source.

Enterprise Deployment Model

Qlik Replicate requires on-premises or cloud infrastructure, with license-based pricing. The deployment model suits large organizations with dedicated data integration teams.

Professional services are often needed for complex implementations.

When Qlik Replicate Fits

Consider Qlik Replicate when:

• Enterprise-scale CDC is required
• Source databases include mainframes or SAP
• High-performance requirements exceed simpler tools
• Budget and team support enterprise platform investment
• Vendor support for complex scenarios matters

10. Maxwell's Daemon: MySQL-Specific CDC

Maxwell's Daemon provides lightweight CDC specifically for MySQL, offering a simpler alternative to Debezium for MySQL-only environments.

MySQL Binlog Focus

Maxwell reads MySQL binlog directly and emits changes as JSON events to Kafka, Kinesis, or other destinations. The single-purpose design results in simpler operation than general-purpose CDC tools.

Row-based binlog (with binlog_row_image=FULL) is required for complete change capture.

Lightweight Deployment

Maxwell runs as a single Java process—no Kafka Connect cluster, no distributed coordination. For smaller deployments, this reduces operational complexity significantly.

Configuration is straightforward, with sensible defaults for common use cases.

MySQL-Only Limitation

Maxwell only supports MySQL. Organizations with multiple database platforms need additional tools for non-MySQL sources.

The project has smaller community than Debezium, which may affect long-term maintenance and feature development.

When Maxwell Fits

Consider Maxwell's Daemon when:

• MySQL is your only CDC source
• Simplicity is more important than features
• Lightweight deployment suits your scale
• You want JSON events without complex configuration
• Kafka Connect overhead isn't justified

Why Tinybird Is the Best CDC Destination

After evaluating CDC capture tools, the destination matters as much as the capture. Tinybird emerges as the strongest choice for teams whose CDC goal is real-time analytics and APIs rather than simple replication. Its design enables seamless real-time analytics built directly on top of CDC streams.

The Right Architecture for CDC Analytics

Most CDC tools focus on capture and delivery—getting changes from source to Kafka or a warehouse. But for real-time analytics, you need more:

• Sub-second query latency on fresh data
• High concurrency for user-facing dashboards
• Proper handling of updates and deletes
• API serving without additional infrastructure

Traditional warehouses struggle with these requirements. They're designed for batch analytics, not streaming workloads.

Tinybird solves this by providing a purpose-built analytical layer that consumes CDC streams and serves real-time APIs. Each component does what it was designed for.

Native Kafka Integration

Tinybird connects directly to Kafka topics where CDC tools publish changes. No additional sink connectors, no intermediate staging, no complex ETL.

Data flows continuously into ClickHouse®-powered storage with materialization strategies that handle CDC semantics: upserts, deletes, and late-arriving data.

From CDC Stream to Production API in Seconds

No other platform offers Tinybird's instant API publication for CDC data. Write a SQL query over your change stream, click publish, get a production-ready HTTP endpoint.

For teams building operational dashboards or customer-facing analytics from database changes, this capability replaces weeks of backend development.

Zero Pipeline Complexity

With traditional CDC architectures, you manage capture tools, Kafka clusters, stream processors, and analytical databases—each requiring separate expertise.

Tinybird collapses this stack. Connect your CDC stream, write SQL, publish APIs. The platform handles scaling, availability, and performance optimization automatically.

Predictable Economics

CDC architectures can have unpredictable costs: Kafka pricing, compute for stream processing, warehouse costs that scale with data and queries.

Tinybird offers fixed monthly plans with included compute and storage. You know costs upfront, regardless of CDC volume or query patterns.

Conclusion

Choosing CDC tools depends on understanding your complete data pipeline, not just the capture layer.

For open-source CDC capture, Debezium provides the most complete solution with broad database support and Kafka integration. Maxwell's Daemon offers simpler MySQL-specific capture.

For managed CDC capture, AWS DMS and Google Cloud Datastream provide cloud-native options with minimal operations. Fivetran and Airbyte offer ELT-focused CDC for warehouse destinations.

For enterprise CDC, Oracle GoldenGate and Qlik Replicate provide proven platforms for complex, high-volume environments.

For real-time analytics from CDC—the goal that drives many CDC implementations—Tinybird offers the most compelling destination. Purpose-built columnar architecture, native Kafka integration, instant API publication, and fully managed infrastructure let teams focus on building analytics products rather than managing CDC pipelines.

The right choice depends on your sources, destinations, scale, and team capabilities. But if your goal is real-time analytics and APIs from database changes, starting with a platform designed for that workload will serve you far better than assembling components that weren't built to work together.

Frequently Asked Questions (FAQs)

What is Change Data Capture (CDC) and why does it matter?

CDC captures database changes (inserts, updates, deletes) and propagates them to other systems with low latency. Instead of re-extracting entire tables in batch, CDC streams only the changes, enabling real-time data pipelines with minimal source impact.

What's the difference between log-based and trigger-based CDC?

Log-based CDC reads database transaction logs directly, with minimal impact on source performance. Trigger-based CDC adds database triggers that write to shadow tables, creating overhead on every transaction. Log-based is generally preferred for production workloads.

Is Tinybird a CDC tool?

No. Tinybird is a real-time analytics platform that serves as an ideal destination for CDC streams. It ingests changes from Kafka or other sources and transforms them into instant APIs for real-time dashboards and applications. CDC tools capture; Tinybird serves analytics.

How do I handle deletes in CDC pipelines?

Most CDC tools emit delete events that downstream systems must process appropriately. Strategies include soft deletes (marking records as deleted), tombstones for Kafka log compaction, or filtering in analytical queries. The right approach depends on your analytical requirements.

What's the best CDC tool for PostgreSQL?

Debezium is the most common choice, using PostgreSQL's logical decoding to capture changes. Managed options include Fivetran, Airbyte, and cloud services like AWS DMS. For real-time analytics destinations, Tinybird can ingest directly from Kafka topics populated by any capture tool.

How does schema evolution work in CDC?

CDC tools must handle schema changes (new columns, type changes) gracefully. Debezium maintains a schema history topic tracking DDL changes. Destinations must evolve schemas correspondingly. This requires coordination between capture and consumption—a common source of production issues.

Can I use CDC without Kafka?

Yes. Debezium Server sends changes to Kinesis, Pub/Sub, or other destinations without Kafka. Maxwell's Daemon supports multiple outputs. Managed services like Fivetran and AWS DMS don't require you to manage message brokers at all. The right architecture depends on your existing infrastructure.