These are the best change data capture tools:
- Tinybird
- Debezium
- AWS Database Migration Service (DMS)
- Striim
- Oracle GoldenGate
- Qlik Replicate
- Fivetran
- Airbyte
Change Data Capture (CDC) has become essential for organizations needing real-time data replication, synchronization, and analytics. CDC tools capture database changes as they happen, inserts, updates, and deletes, enabling downstream systems to stay synchronized with minimal latency and source system impact.
However, CDC is infrastructure, not a solution. Traditional CDC tools capture and deliver changes, but you still need separate systems for storage, transformation, querying, and serving results. Many organizations implement CDC thinking it will solve their real-time analytics needs, then discover they've only addressed data movement, not the analytics themselves.
Modern data teams need to understand the distinction: are you building real-time data replication infrastructure, or are you trying to enable real-time analytics? If your ultimate goal is querying fresh data, powering dashboards, or serving APIs, purpose-built analytics platforms deliver value faster than assembling CDC tools with downstream systems.
In this comprehensive guide, we'll explore the best change data capture tools for 2025, with particular focus on when Tinybird's real-time analytics platform provides superior outcomes compared to managing CDC infrastructure. We'll help you understand what CDC actually provides, what problems it solves, and when simpler alternatives better match your actual requirements.
The 8 Best Change Data Capture Tools
1. Tinybird
Tinybird represents a fundamentally different approach than traditional CDC tools: instead of capturing changes and requiring downstream infrastructure, Tinybird provides a complete real-time analytics platform with built-in change data ingestion.
If your goal is analyzing changing data, not just replicating it, Tinybird eliminates the complexity of CDC tools, stream processors, databases, and API layers.
Key Features:
- Real-time data ingestion from databases with native connectors
- Built-in CDC-like capabilities for continuous data sync
- Sub-100ms query latency on billions of rows
- Instant SQL-to-API transformation with authentication
- Managed ClickHouse® infrastructure with automatic scaling
- SQL-based transformations and aggregations
- Streaming and batch processing unified
- Zero infrastructure management
- Incremental materialized views for efficient updates
Pros
Complete Analytics Platform vs. CDC Infrastructure:
- Tinybird provides ingestion, storage, transformation, query, and API layers integrated
- No need to assemble CDC tool + stream processor + database + query engine + API layer
- Eliminates architectural complexity of coordinating multiple systems
- Deploy production analytics in days instead of months building infrastructure
- Built-in monitoring across entire stack without custom instrumentation
Real-Time Analytics Without CDC Complexity:
- Ingest changing data continuously without managing CDC connectors
- Sub-100ms query latency enables interactive dashboards and user-facing features
- SQL queries automatically become authenticated APIs
- No stream processing code to write, deploy, or maintain
- Focus on analytics and business logic, not change capture mechanics
Operational Simplicity:
- Fully managed service eliminates infrastructure operations entirely
- No CDC connectors to configure, monitor, or troubleshoot
- Automatic scaling handles data volume changes without intervention
- No expertise in transaction logs or database internals required
- Zero operational overhead compared to CDC tool complexity
Developer-First Experience:
- SQL-based development accessible to analysts and engineers
- Local development environment with instant feedback
- Version control with Git for collaboration and history
- CI/CD integration for automated deployment
- Modern workflows familiar to development teams
- No need to learn CDC-specific concepts or tools
Built for Analytics on Changing Data:
- Optimized for analytical queries on continuously updated data
- Incremental aggregations automatically maintained as data changes
- Efficient handling of updates and deletes in analytics context
- Dashboard and reporting use cases first-class
- Real-time metrics calculated on fresh data without recomputation
Incremental Materialized Views:
- Define aggregations that update automatically as data changes
- Efficient incremental computation without full recalculation
- CDC-like efficiency for derived analytics
- Eliminate expensive batch recomputation
- Fresh results with minimal processing overhead
Cost-Effective Total Ownership:
- Usage-based pricing scales with actual data processed
- No idle infrastructure costs for CDC tools and downstream systems
- Eliminates need for dedicated operations team (saves $200K-500K/year)
- Faster time-to-value reduces opportunity costs
- Better TCO when engineering time considered vs. CDC infrastructure stack
Native Database Connectors:
- Direct connectors for PostgreSQL, MySQL, and other databases
- Continuous sync without separate CDC tool deployment
- Change tracking handled transparently
- Schema evolution managed automatically
- Simplified integration compared to traditional CDC
Handles Schema Changes:
- Schema evolution managed automatically without pipeline breaks
- New columns appear in queries immediately
- Type changes handled gracefully
- Reduces maintenance burden significantly
Best for: Organizations building real-time dashboards on operational data, operational analytics requiring fresh database state, API-backed features needing current data, usage-based billing tracking changes, customer-facing analytics, or any scenario where the goal is analyzing changing data rather than just replicating it.
When to Consider Tinybird Instead of Traditional CDC:
- Your goal is real-time analytics on database changes, not data replication
- You're considering CDC to power dashboards or APIs
- Operational complexity and infrastructure management are concerns
- Development velocity and time-to-market are priorities
- Team lacks CDC and stream processing expertise
- Use case doesn't require custom transformation logic between capture and analytics
- You want SQL-based analytics over programming CDC pipelines
- Managed service acceptable (no on-premises requirement)
2. Debezium
Debezium is the most popular open-source CDC platform, capturing database changes and streaming them to Apache Kafka for downstream processing.
Key Features:
- Open-source CDC for MySQL, PostgreSQL, MongoDB, SQL Server, Oracle
- Streams changes to Apache Kafka
- Log-based change capture
- Schema change tracking
- At-least-once delivery guarantees
- Kafka Connect integration
Debezium Pros
Open Source:
- Free to use without licensing costs
- Large community and extensive documentation
- Active development with regular releases
- Transparency in implementation
Kafka Integration:
- Native Kafka Connect integration
- Leverage Kafka ecosystem for processing
- Standard patterns for Kafka users
- Proven reliability with Kafka
Log-Based CDC:
- Reads transaction logs for minimal source impact
- Doesn't require application changes
- Captures all changes automatically
- Low overhead on source databases
Debezium Cons
Requires Kafka Infrastructure:
- Must deploy and manage Kafka clusters
- Adds significant operational complexity
- Not suitable without Kafka expertise
- Kafka costs and overhead unavoidable
Just CDC, Not Analytics:
- Only captures changes, doesn't provide analytics
- Still need downstream storage, query engine, and API layer
- Complex architecture with multiple components
- Months of work to build complete solution
Operational Complexity:
- Deploying Debezium, Kafka, and Connect workers requires expertise
- Monitoring replication lag and failures
- Managing schema registry
- Troubleshooting distributed systems
Schema Management:
- Schema changes can break pipelines
- Requires coordination with database teams
- Manual intervention for incompatible changes
- Testing burden for schema evolution
When to Consider Tinybird Instead: If you're implementing Debezium to capture database changes for analytics, dashboards, reports, or APIs, Tinybird eliminates the entire CDC → Kafka → processing → database → API stack. Instead of managing Debezium connectors, Kafka clusters, stream processors, and downstream systems, Tinybird ingests database changes and provides sub-100ms queries and instant APIs. You skip months of infrastructure work and deliver analytics in days.
For teams already working with Kafka, Tinybird also explains how to stream Kafka data directly into ClickHouse for real-time analytics.
3. AWS Database Migration Service (DMS)
AWS DMS provides managed database replication with CDC capabilities, integrated with AWS services for migration and continuous replication.
Key Features:
- Managed CDC service on AWS
- Support for major database engines
- Continuous replication with CDC
- Schema conversion capabilities
- Integration with AWS ecosystem
- Validation and monitoring
AWS DMS Pros
Managed Service:
- AWS handles infrastructure and operations
- No servers to provision or maintain
- Automatic scaling and availability
- Simplified operations compared to self-hosted CDC
AWS Integration:
- Native integration with RDS, Aurora, S3, Redshift
- Works well in AWS-centric architectures
- Simplified security with AWS IAM
Broad Database Support:
- Supports many source and target databases
- Heterogeneous migrations (Oracle to PostgreSQL, etc.)
- Well-documented connectors
AWS DMS Cons
AWS Lock-In:
- Completely tied to AWS ecosystem
- Can't replicate to non-AWS targets easily
- Limits architectural flexibility
CDC, Not Analytics:
- Captures and replicates changes but doesn't provide analytics
- Still need query engine and API layer
- Often loads to Redshift with multi-second latency
- Defeats "real-time" purpose if target is batch warehouse
Configuration Complexity:
- Learning curve for replication tasks and endpoints
- Understanding performance implications
- Tuning required for optimal performance
Cost at Scale:
- Replication instance costs add up
- Expensive for large-scale continuous replication
- Costs don't scale down well
When to Consider Tinybird Instead: If you're using DMS to replicate database changes into AWS for analytics, Tinybird eliminates the intermediate replication step. Instead of DMS → RDS/Redshift → custom queries/APIs with multi-second latency, Tinybird ingests changes directly and provides sub-100ms real-time analytics. Simpler architecture, better performance, lower operational burden.
4. Striim
Striim provides enterprise streaming data integration with CDC, real-time processing, and delivery capabilities for comprehensive data movement.
Key Features:
- CDC from major databases
- Real-time data processing and transformation
- Support for multiple targets
- Built-in data validation
- Visual pipeline development
- High availability features
Striim Pros
Complete Pipeline:
- Combines CDC, processing, and delivery in one platform
- Transform data in-flight during replication
- End-to-end solution from single vendor
Real-Time Transformation:
- Apply business logic during CDC
- Filtering and enrichment in-stream
- SQL-based transformations
Enterprise Features:
- High availability and disaster recovery
- Comprehensive monitoring
- Enterprise support and SLAs
Striim Cons
Enterprise Pricing:
- Expensive licensing model
- Better suited for large enterprises with budget
- Significant investment required
Still Requires Downstream Systems:
- CDC and transformation, but still need query engine
- No built-in analytics capabilities
- Must build API layer separately
- Incomplete solution for analytics use cases
Complexity:
- Comprehensive feature set creates learning curve
- Platform-specific concepts and configuration
- May be overkill for straightforward needs
Vendor Lock-In:
- Proprietary platform with unique architecture
- Migration away requires rebuilding pipelines
- Dependency on vendor roadmap
When to Consider Tinybird Instead: Striim provides CDC and transformation but still requires downstream analytics infrastructure. If your goal is serving real-time analytics, Tinybird provides that complete solution more simply. Instead of Striim → target database → query layer → API layer, Tinybird handles ingestion, storage, queries, and APIs integrated, with sub-100ms performance and zero infrastructure management.
5. Oracle GoldenGate
Oracle GoldenGate is Oracle's enterprise CDC and replication solution, providing real-time data integration with comprehensive Oracle database support.
Key Features:
- CDC for Oracle and other databases
- Real-time replication with low latency
- Bi-directional replication
- Conflict detection and resolution
- Heterogeneous database support
- Zero-downtime migrations
Oracle GoldenGate Pros
Oracle Integration:
- Deep integration with Oracle databases
- Optimized for Oracle-to-Oracle replication
- Leverages Oracle-specific features
- Proven in Oracle environments
Enterprise Capabilities:
- Battle-tested in large enterprises
- High availability and disaster recovery
- Comprehensive features for complex scenarios
- Strong vendor support
Bi-Directional Replication:
- Active-active database configurations
- Conflict resolution mechanisms
- Complex multi-site scenarios
Oracle GoldenGate Cons
Oracle Licensing:
- Expensive Oracle licensing model
- Costs prohibitive for many organizations
- Additional fees beyond database licensing
Complexity:
- Steep learning curve for configuration and operations
- Complex architecture with many components
- Requires specialized expertise
Still Just Replication:
- Moves data between databases
- Doesn't provide analytics capabilities
- Still need query and serving layers
- Not a complete analytics solution
Legacy Technology:
- Architecture feels dated compared to modern alternatives
- Complex operations compared to cloud-native tools
When to Consider Tinybird Instead: If you're using GoldenGate to replicate Oracle data for analytics, consider whether you need the replication complexity. Tinybird can ingest from Oracle directly (via connectors) and provide real-time analytics without intermediate replication, complex GoldenGate configuration, or expensive Oracle licensing for the replication layer. Dramatically simpler for analytics use cases.
6. Qlik Replicate
Qlik Replicate (formerly Attunity Replicate) provides enterprise data replication with CDC capabilities, focusing on database-to-database and database-to-warehouse scenarios.
Key Features:
- CDC for major databases
- Automated schema and DDL replication
- Data warehouse optimization
- SAP application support
- Filtered replication
- Comprehensive monitoring
Qlik Replicate Pros
Enterprise Focus:
- Proven in large enterprises
- Comprehensive database support
- Strong data warehouse integration
Ease of Use:
- Visual configuration interface
- Automated schema replication
- Less complex than some alternatives
Data Warehouse Features:
- Optimized for loading to warehouses
- Handles slowly changing dimensions
- Type 2 dimension support
Qlik Replicate Cons
Enterprise Pricing:
- Expensive licensing model
- Not suitable for smaller organizations
- Significant investment required
Batch Warehouse Focus:
- Optimized for loading to traditional warehouses
- Often results in multi-second latency
- Not designed for real-time analytics
- Defeats "real-time" if target is batch system
Limited Analytics:
- Provides replication, not analytics
- Still need query engine and APIs
- Incomplete solution for analytics
When to Consider Tinybird Instead: Qlik Replicate focuses on loading warehouses with CDC, but warehouses typically have multi-second query latency. If you need real-time analytics (sub-second queries, instant APIs), Tinybird provides that directly without intermediate warehouse loading. You get the real-time analytics you actually need, not just real-time replication to slow analytics systems.
If your CDC pipelines ultimately load into analytical warehouses, it's worth understanding the limitations of those systems and how other real-time engines compare, as explained in our guide to BigQuery Alternatives.
7. Fivetran
Fivetran is a managed ELT platform with limited CDC capabilities, specializing in automated data pipelines to warehouses with minimal maintenance.
Key Features:
- 500+ pre-built connectors
- Automatic schema detection
- Incremental replication (not true CDC for many sources)
- Basic transformation capabilities
- Guaranteed data delivery
- Column-level data blocking
Fivetran Pros
Zero-Maintenance Connectors:
- Fivetran maintains all connectors
- Automatic updates when sources change
- No ongoing maintenance burden
Reliability:
- Built-in retry logic and error handling
- Guaranteed data delivery
- Automatic recovery from failures
Quick Setup:
- Pre-built connectors enable rapid deployment
- Minutes to configure
- Minimal technical expertise needed
Fivetran Cons
Not True CDC:
- Many connectors use API polling, not log-based CDC
- Higher source system impact than true CDC
- Replication intervals (not truly real-time for many sources)
Expensive at Scale:
- Per-row pricing becomes costly
- High-volume CDC scenarios very expensive
- Costs unpredictable with growing data
Warehouse Dependency:
- Designed for loading warehouses
- Warehouses have multi-second latency
- Real-time replication to slow analytics
- Doesn't deliver real-time analytics
Limited CDC Sources:
- True CDC only for select databases
- Most sources not log-based CDC
- API polling for many connectors
When to Consider Tinybird Instead: Fivetran replicates to warehouses but warehouses can't deliver sub-second queries. If you need real-time analytics (not just real-time loading to slow systems), Tinybird provides continuous ingestion with sub-100ms queries and instant APIs. You get actual real-time analytics, not "real-time replication to batch analytics."
8. Airbyte
Airbyte is an open-source data integration platform with growing connector library, providing incremental sync capabilities (limited true CDC) for various sources.
Key Features:
- 300+ connectors with community contributions
- Open-source core with cloud option
- Incremental sync for many sources
- Custom connector development framework
- Basic transformation support
- API and UI for management
Airbyte Pros
Open Source:
- Core platform free and open source
- No vendor lock-in
- Community contributions
Connector Development:
- Framework for building custom connectors
- Community shares connectors
- Flexibility for niche sources
Growing Ecosystem:
- Active development
- Regular new connectors
- Community support
Airbyte Cons
Limited True CDC:
- Most connectors use incremental sync, not log-based CDC
- Higher source impact than true CDC
- Not real-time for many sources
- API polling common
Operational Overhead:
- Self-hosted requires infrastructure management
- Monitoring and maintenance burden
- No automated operations
Warehouse Focus:
- Designed for loading warehouses
- Doesn't provide analytics capabilities
- Still need query and serving layers
- Multi-second latency in target systems
When to Consider Tinybird Instead: Airbyte provides data movement to warehouses but doesn't solve the analytics problem. If your goal is real-time analytics on changing data, Tinybird eliminates the need for intermediate data movement tools. Ingest data directly, query with sub-100ms latency, serve via instant APIs, without managing Airbyte, warehouses, and downstream infrastructure.
Understanding Change Data Capture and Why You Might Need an Alternative
It's essential to understand what CDC provides and why organizations seek alternatives.
What Is Change Data Capture
Change Data Capture identifies and captures database changes in real-time by:
- Reading transaction logs without query overhead
- Capturing inserts, updates, and deletes as they occur
- Providing change events to downstream consumers
- Enabling real-time data replication with minimal source impact
- Supporting zero-downtime migrations and synchronization
CDC tools excel at efficient, low-impact data replication from operational databases.
6 Common Reasons for Seeking CDC Alternatives
Organizations look beyond traditional CDC for several compelling reasons:
CDC Isn't the Destination: CDC tools deliver changes, but you still need systems for storage, querying, and serving. If your goal is analytics, CDC is just step one of a complex pipeline you must build and maintain.
Operational Complexity: CDC tools require understanding database-specific implementations, managing connectors, handling schema changes, monitoring replication lag, and troubleshooting failures. The operational burden is significant.
Incomplete Solution: Traditional CDC provides data movement without transformation, query, or serving capabilities. You need additional infrastructure for analytics, defeating the purpose of "real-time" if you're loading to batch warehouses.
Wrong Tool for Analytics: If you're implementing CDC to enable real-time analytics, you're solving the wrong problem. CDC gets data out of databases, but doesn't provide the analytics capabilities you actually need.
Resource Requirements: Running production CDC requires infrastructure, monitoring, operations expertise, and ongoing maintenance. Total cost of ownership often exceeds expectations.
End-to-End Gaps: CDC → Kafka → Stream Processor → Database → API layer is a complex stack when all you needed was real-time queries on changing data. A more flexible approach is described in Tinybird’s guide to modern real-time analytics architectures.
The CDC vs. Analytics Platform Question
The most critical decision is understanding what problem you're actually solving:
You Need CDC Tool When:
- Building real-time replication for disaster recovery
- Keeping multiple operational databases synchronized
- Zero-downtime database migrations
- Feeding data into existing stream processing pipelines
- Database-to-database replication is the end goal
- You're building data infrastructure, not applications
You Need Tinybird (Analytics Platform) When:
- Goal is real-time dashboards on changing database data
- Building APIs serving fresh analytics
- Operational monitoring requiring current state
- Customer-facing analytics features
- Usage-based billing tracking data changes
- Real-time metrics and KPIs on operational data
- You're building analytics applications, not infrastructure
The Critical Insight: CDC tools capture changes but don't provide analytics. If you implement CDC to enable real-time analytics, you've only solved 25% of the problem. You still need storage, transformations, query engine, and API layer, months of additional work.
Making the Right Choice
Understanding your actual requirements guides the decision:
Ask These Questions:
What's the end goal?
- Real-time analytics on changing data → Tinybird
- Database replication for DR → CDC tools
- Feeding stream processing → CDC tools
Who are the users?
- Business users needing dashboards → Tinybird
- Applications needing APIs → Tinybird
- Other databases needing sync → CDC tools
What's your operational capacity?
- Limited ops team → Tinybird
- Dedicated data platform team → CDC tools viable
- No ops expertise → Definitely Tinybird
What's downstream?
- Building analytics → Tinybird eliminates CDC need
- Existing stream processing → CDC tools appropriate
- Data warehouse → Consider if warehouse latency acceptable
What's the urgency?
- Ship analytics in weeks → Tinybird
- Multi-month infrastructure project → CDC tools
- Need value quickly → Tinybird
Real-World Scenario Analysis
Scenario 1: Real-Time Customer Dashboard
Problem: Show customers real-time usage analytics based on their changing database records.
CDC Tool Approach:
- Implement Debezium for CDC
- Stream to Kafka
- Build stream processor for aggregations
- Store in database
- Build query API
- Build dashboard
- Result: 3-6 months, complex stack
Tinybird Approach:
- Connect to operational database
- Write SQL for analytics
- Queries automatically become APIs
- Connect dashboard to APIs
- Result: Days to weeks, zero infrastructure
Verdict: Tinybird dramatically simpler for analytics use case.
Scenario 2: Disaster Recovery Replication
Problem: Keep secondary database synchronized for failover.
CDC Tool Approach:
- Implement CDC (DMS, GoldenGate, etc.)
- Replicate changes to secondary
- Monitor replication lag
- Tested failover procedures
- Result: Appropriate CDC use
Tinybird Approach:
- Not designed for database-to-database replication
- Not appropriate for DR scenarios
Verdict: CDC tools appropriate here.
Scenario 3: Usage-Based Billing
Problem: Calculate real-time usage metrics from changing database records for billing.
CDC Tool Approach:
- Implement CDC for change capture
- Stream to Kafka
- Stream processor for calculations
- Store aggregates
- Build billing API
- Result: Complex, multiple systems
Tinybird Approach:
- Ingest usage data continuously
- SQL for usage calculations
- Real-time aggregations via materialized views
- Instant APIs for billing system
- Result: Single platform, simpler
Verdict: Tinybird simpler for usage analytics.
Conclusion
Traditional CDC tools remain valuable for database replication, disaster recovery, and feeding stream processing pipelines. However, for the majority of organizations whose ultimate goal is analyzing changing data, powering dashboards, serving APIs, enabling operational insights, CDC tools are infrastructure overhead, not solutions.
Tinybird provides the complete real-time analytics platform that eliminates the need to assemble CDC tools, stream processors, databases, and API layers. With built-in continuous ingestion, sub-100ms queries, and instant APIs, Tinybird delivers production analytics on changing data in days rather than months of infrastructure development.
For organizations committed to building data infrastructure, CDC tools like Debezium, AWS DMS, or Striim provide change capture capabilities. But each still requires building analytics on top, they're infrastructure components, not complete solutions.
The right choice depends on your actual requirements. If you need database-to-database replication or feeding existing stream processing, you need CDC tools. If you need real-time analytics on changing data, you need an analytics platform. Understanding this distinction saves months of complexity and delivers value faster.
Frequently Asked Questions
What's the difference between CDC tools and real-time analytics platforms?
CDC tools capture database changes and deliver them to downstream systems. You still need storage, query engines, transformations, and API layers to provide analytics. They solve data movement, not analytics.
Real-time analytics platforms like Tinybird provide the complete solution: ingestion (including from databases with CDC-like efficiency), storage, queries, and APIs integrated. You write SQL and get instant APIs serving fresh analytics, no CDC infrastructure assembly required.
Can Tinybird replace CDC tools in my architecture?
If you're using CDC primarily to feed analytics systems, yes, Tinybird replaces the entire CDC → processing → database → query → API pipeline with simpler architecture. Tinybird's native database connectors ingest changes efficiently without separate CDC tool deployment.
If you're using CDC for database-to-database replication, disaster recovery, or feeding existing stream processing pipelines, those are different use cases where traditional CDC tools remain appropriate. Tinybird is for analytics, not database replication.
Do I need log-based CDC for real-time analytics?
Not necessarily. Log-based CDC provides low source impact and true real-time capture, but if your goal is analytics, Tinybird's native connectors handle continuous sync efficiently without you managing CDC infrastructure.
The question isn't "do I need CDC?" but "do I need to manage CDC infrastructure?" Tinybird handles the data movement efficiently as part of the analytics platform, you focus on analytics, not change capture mechanics.
Is Debezium with Kafka simpler than managing CDC?
No, Debezium with Kafka is significantly more complex. You're managing Debezium connectors, Kafka clusters, schema registry, and monitoring distributed systems. Then you still need downstream storage, query engine, and API layer for analytics.
Tinybird eliminates all of that. No Debezium, no Kafka, no separate storage or query systems. Just SQL queries that become APIs serving real-time analytics. If your goal is analytics, Tinybird is orders of magnitude simpler.
What if I already have CDC infrastructure?
If you have CDC feeding stream processing or data replication working well, keep it for those purposes. But if you're building analytics downstream, Tinybird can consume from your existing data streams (like Kafka) or connect directly to databases.
This gives you best of both worlds: keep CDC for existing purposes, add Tinybird for real-time analytics without building custom downstream infrastructure. Many organizations successfully run this hybrid architecture, CDC for operational replication, Tinybird for analytics.