9 Best Change Data Capture (CDC) Tools in 2026

Change data capture tools detect inserts, updates, and deletes in source databases and replicate those changes to downstream systems without repeatedly reloading entire tables. This helps teams keep data warehouses, applications, search indexes, and analytics systems current while reducing repeated query load on production databases.

The best CDC tools in 2026 include Estuary, Debezium, Fivetran, Qlik Replicate, Oracle GoldenGate, Striim, AWS Database Migration Service, Airbyte, and Skyvia. However, these platforms do not all provide the same type of change data capture.

Some continuously read database transaction logs and stream changes as they occur. Others read logs through scheduled jobs, use database-native change tracking, or rely on timestamp-based incremental queries. These differences affect latency, delete capture, transaction ordering, recovery, source impact, and the infrastructure your team must manage.

This guide compares nine leading change data capture tools based on:

• CDC architecture and capture method
• Continuous versus scheduled delivery
• Initial snapshot and backfill support
• Deployment and operational requirements
• Schema-change handling
• Pricing model
• Best-fit use cases and limitations

Best CDC Tools in 2026 at a Glance

What is Change Data Capture (CDC)?

Change Data Capture is the process of identifying inserts, updates, and deletes in a source database and propagating those changes to downstream systems.

Unlike full-table extraction, CDC processes only new changes after the initial snapshot. This reduces repeated source queries and helps keep warehouses, applications, search indexes, and analytics systems current.

The three CDC methods compared

When trigger-based CDC makes sense: Trigger-based CDC can be useful when transaction-log access is unavailable. However, because triggers execute during source writes and require database-level maintenance, teams should evaluate their performance impact and operational overhead carefully.

What to Look for in a Change Data Capture Platform

The best change data capture platform depends on your source systems, latency requirements, deployment preferences, and operational capacity. Before comparing individual CDC tools, evaluate these six factors.

CDC method

Confirm whether the platform uses transaction-log-based CDC, database-native change tracking, triggers, or scheduled incremental queries. Log-based CDC is generally the strongest option for capturing inserts, updates, and deletes with low latency and less repeated query load after the initial snapshot.

Continuous or scheduled delivery

Some CDC tools run continuously, while others read accumulated changes on a schedule. Choose based on your actual freshness requirement. Operational applications may need changes within seconds, while analytics workloads may tolerate delivery every few minutes.

Source and destination support

Check support for your exact database engine, version, managed-cloud environment, and destination. A large connector catalog does not necessarily mean broad log-based CDC coverage.

Reliability and recovery

A production-ready CDC platform should maintain durable checkpoints, recover after outages, monitor replication lag, and clearly explain how it handles retries, duplicates, and expired transaction logs.

Schema evolution

Review how the tool handles added columns, data-type changes, renamed fields, new tables, and primary-key changes. Most platforms manage additive schema changes more safely than breaking changes.

Deployment and total cost

Compare managed SaaS, private deployment, BYOC, and self-hosted options. Include not only vendor fees, but also infrastructure, networking, monitoring, upgrades, and engineering time.

No single CDC tool is best for every architecture. The right platform is the one that meets your required latency, supports your systems, recovers reliably, and fits the level of infrastructure your team is prepared to operate.

The 9 best change data capture tools in 2026

The following CDC tools cover different architectures, deployment models, and use cases, from open-source event streaming to managed real-time pipelines and enterprise database replication. They are not ranked from best to worst; each tool is evaluated based on its CDC method, operating model, pricing, strengths, and limitations.

1. Debezium

Best for: Open-source CDC and Kafka-based event architectures

CDC method: Transaction-log and native change-stream capture

Deployment: Self-managed through Kafka Connect, Debezium Server, or Debezium Engine

Debezium is an open-source CDC platform that converts committed database inserts, updates, and deletes into structured change events.

It supports PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, MongoDB, Db2, Cassandra, and Google Cloud Spanner, with additional incubating connectors.

Debezium usually performs an initial snapshot of selected tables and then continuously reads changes from the database log. Events include operation type, before-and-after values, timestamps, and source-log positions.

Where Debezium fits

Debezium is a good fit when:

• Kafka and Kafka Connect are already part of your infrastructure.
• Multiple applications need to consume the same database changes.
• You need complete control over event formats, retention, and processing.
• You are building event-driven services or implementing the transactional outbox pattern.
• Your team can operate and monitor CDC infrastructure.

Kafka Connect is Debezium’s most common deployment model, but Kafka is not mandatory. Debezium Server can send changes to systems such as Amazon Kinesis, Google Cloud Pub/Sub, Apache Pulsar, Redis Streams, and NATS JetStream.

Pricing model

Debezium is free and open source under the Apache License 2.0.

There are no Debezium licensing or usage charges. However, teams must account for:

• Kafka, Kafka Connect, or alternative messaging infrastructure.
• Compute, storage, networking, and topic retention.
• Schema registry and monitoring infrastructure.
• Database and connector configuration.
• Engineering time for upgrades, scaling, incident response, and recovery testing.

For teams that already operate Kafka, these costs may fit naturally into the existing platform. For teams introducing Kafka primarily for CDC, infrastructure and operational costs can be significant.

Honest limitations

• Debezium is a CDC engine, not a fully managed data integration platform.
• Teams must operate Kafka Connect, Debezium Server, or an embedded Debezium Engine.
• Delivering changes into warehouses may require separate sink connectors or custom consumers.
• Debezium provides at-least-once delivery by default, so duplicate events can occur after failures or restarts. Supported source connectors can participate in Kafka Connect’s exactly-once delivery mode when the required Kafka and connector settings are enabled, but teams should still verify the guarantee across the complete source-to-destination pipeline.
• Database logs, PostgreSQL replication slots, connector offsets, and schema changes require monitoring.
• The software is free, but infrastructure and engineering costs can be significant.

2. Fivetran

Best for: Broad managed data integration and enterprise warehouse ingestion

CDC method: Log-based or native capture for supported databases, with incremental extraction for other sources

Deployment: SaaS, hybrid deployment, or self-hosted HVR

Fivetran is a managed data movement platform with more than 700 connectors spanning databases, SaaS applications, ERP systems, files, events, and cloud services.

Its main strength is operational simplicity. Fivetran manages connector maintenance, extraction, destination loading, schema updates, retries, and monitoring. This makes it a strong option for organizations that need to centralize data from many different systems without maintaining custom pipelines.

CDC approach

Fivetran’s change capture method depends on the source connector. Supported database connectors can use transaction logs or native database change mechanisms. For example, MySQL can read binary logs, while PostgreSQL supports logical replication.

Some database connectors also provide query-based or snapshot-comparison methods when transaction-log access is unavailable. SaaS and application connectors generally retrieve incremental changes through source APIs rather than database CDC.

For demanding database workloads, Fivetran offers High-Volume Agent connectors. These connectors are designed for high-volume database replication and are available with Fivetran’s SaaS and Hybrid deployment models.

Fivetran HVR is the self-hosted option for enterprise database and file replication. It supports continuous log-based change capture and provides more control over where replication components run.

Where Fivetran fits

Fivetran is a good fit when:

• You need to integrate a large mix of databases, SaaS applications, files, and enterprise systems.
• You want a managed platform that handles connector maintenance and source API changes.
• You need log-based CDC from supported production databases without operating Kafka.
• You want configurable schema change handling for new schemas, tables, and columns.
• You need high-volume database replication through HVA or a self-hosted deployment through HVR.
• Security, access controls, private networking, and centralized governance are important requirements.

Fivetran also supports soft-delete and history modes. Soft-delete mode maintains the current state of a source table, while History Mode preserves previous versions of changed records using a slowly changing dimension structure.

Pricing model

Fivetran primarily measures connection usage in Monthly Active Rows, based on distinct primary keys synced during a calendar month.

A row is generally counted once per month within a given connection and table, even if it changes multiple times. However, the same source data can count separately when replicated through multiple connections or destinations.

Costs can increase when pipelines affect large numbers of unique rows, require repeated resyncs, or replicate the same source data through several connections. Teams should estimate pricing using representative production change patterns rather than total table size alone.

Honest limitations

• The full connector catalog should not be interpreted as 700 log-based CDC connectors. Many SaaS, file, and application connectors use scheduled incremental extraction.
• End-to-end latency varies by connector, configured sync frequency, destination, and deployment model.
• Fivetran’s standard managed connectors are designed primarily to load data into warehouses, lakes, and databases rather than publish a general-purpose event stream for multiple consumers.
• MAR-based costs can grow when a large number of unique rows change each month.
• Log-based database connectors may require DBA assistance to configure replication permissions, transaction logs, network access, and log-retention settings.

3. Estuary

Best for: Managed real-time CDC, historical backfills, streaming transformations, and multi-destination delivery

CDC method: Transaction-log and database-native CDC for supported databases, with incremental and batch ingestion for other sources

Deployment: Public SaaS, private deployment, or bring your own cloud

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

It captures source data into durable datasets called collections. A collection can feed multiple transformations and destinations without rereading the source independently for every target.

For supported databases, Estuary can first backfill the current source data and then continue capturing inserts, updates, and deletes through transaction logs or database-native change mechanisms.

CDC and delivery architecture

Estuary uses source-specific CDC mechanisms, including PostgreSQL logical replication, MySQL binary logs, SQL Server CDC, and MongoDB change streams.

When enabled, an initial backfill captures the current state of selected resources before ongoing change capture continues. Captured records are stored in collections, which can feed multiple materializations across warehouses, databases, search systems, event platforms, and cloud storage.

For supported transactional materializations, Estuary commits destination updates and processing checkpoints together, providing exactly-once processing within that materialization boundary. Delivery semantics differ for non-transactional destinations.

Estuary also supports streaming transformations using SQL and TypeScript, with Python available in private and BYOC deployments. These derivations can filter, join, aggregate, enrich, or restructure records before delivery.

Where Estuary fits

Estuary is a good fit when:

• You need continuously updated data rather than scheduled replication jobs.
• You want historical backfills and ongoing CDC in the same data flow.
• You need one captured dataset to feed multiple destinations.
• You want built-in streaming transformations without deploying a separate transformation engine.
• You require private or BYOC deployment for networking, data residency, or compliance.

Schema handling

Estuary’s connector discovery and AutoDiscover features can detect and apply some source-schema changes. Its schema-evolution workflows help update collections and dependent captures or materializations.

Additive changes, such as new fields, can often be incorporated without rebuilding the pipeline. Breaking changes, including incompatible data types, removed fields, or changes to collection keys, may require explicit schema evolution or a backfill.

Pricing model

Estuary pricing combines data-volume charges, connector task-hour usage, and any applicable private or BYOC deployment fees.

Published pricing includes:

• Data movement: $0.50 per GB processed into or out of Estuary.
• Task hours: The first six connectors cost $0.14 per task hour, equivalent to $100 per month for a continuously active connector. Additional task usage costs $0.07 per hour, equivalent to $50 per month.
• Free plan: Up to two connectors and 10 GB of data movement per month.
• Private and BYOC: These deployments require an annual plan and incur additional fees.

Teams should estimate costs using data volume, the number of active capture and materialization tasks, and any private deployment requirements.

Honest limitations

• Estuary’s connector catalog is smaller than those of the largest integration platforms, so teams should confirm support for less common sources and destinations in the connector directory.
• Private and BYOC deployments require a commercial license, additional setup, and an annual plan.
• Database CDC requires source-specific configuration and permissions, such as PostgreSQL logical replication, MySQL binary logging, SQL Server CDC, or MongoDB change-stream prerequisites.
• Collections, captures, materializations, and derivations introduce concepts that teams accustomed to traditional job-based ETL may need time to learn.

4. Qlik Replicate

Best for: High-volume enterprise replication across databases, mainframes, and SAP systems

CDC method: Primarily log-based CDC

Deployment: Self-managed Qlik Replicate or managed deployment through Qlik Talend Cloud

Qlik Replicate, formerly Attunity Replicate, is an enterprise data replication platform designed for high-volume movement across heterogeneous database environments.

Its strongest use cases include mixed commercial databases, mainframes, SAP systems, and long-lived on-premises infrastructure. The platform provides centralized configuration, monitoring, and task management for continuous replication workloads.

Where it fits

• Large-scale Oracle, SQL Server, DB2, and SAP replication where you need very high throughput and proven enterprise support
• Organizations that need enterprise replication controls, centralized monitoring, and support for heterogeneous legacy systems.
• Organizations already in the Qlik ecosystem that want a CDC solution integrated with Qlik's broader data management and analytics platform

Honest limitations

• Commercial pricing is not publicly disclosed; expect enterprise-level costs
• Limited built-in transformation capabilities; complex data reshaping happens downstream
• Heavier to set up and operate than modern SaaS CDC platforms; benefits from experienced DBA involvement
• Less suited for teams that need rapid iteration or self-service pipeline creation

5. Oracle GoldenGate (OCI GoldenGate)

Best for: Mission-critical Oracle replication, disaster recovery, and low-downtime migration

CDC method: Primarily redo-log-based CDC

Deployment: Self-hosted Oracle GoldenGate or managed OCI GoldenGate

Oracle GoldenGate is a mature logical-replication platform designed primarily for Oracle database environments and advanced enterprise replication requirements.

It supports use cases such as Oracle-to-Oracle replication, heterogeneous database movement, disaster recovery, bidirectional synchronization, and low-downtime migration. These architectures require careful planning around conflict resolution, keys, application behavior, and operational ownership.

The Oracle-centricity trade-off

GoldenGate is most compelling when Oracle is a central source or destination and the organization already has Oracle expertise. It also supports non-Oracle systems, but teams should verify connector maturity, supported versions, and operational requirements for their exact environment.

Organizations without a significant Oracle footprint may find cloud-neutral or fully managed CDC platforms easier to adopt and operate.

Honest limitations

• Commercial licensing, infrastructure, and related Oracle support costs can make GoldenGate more expensive than simpler managed CDC services.
• Experienced Oracle database and infrastructure expertise is typically required for configuration and ongoing administration.
• GoldenGate is most deeply associated with Oracle environments. Teams evaluating heterogeneous replication should verify support and operational requirements for their exact sources and targets.
• Organizations without a substantial Oracle footprint may find cloud-neutral or fully managed CDC platforms easier to adopt and operate.

6. Striim

Best for: CDC with real-time stream processing and in-flight transformations

CDC method: Log-based CDC with streaming ingestion and processing

Deployment: Managed cloud or self-hosted

Striim combines database CDC with real-time stream processing.

In addition to capturing database changes, it can filter, join, enrich, aggregate, and route records while they are moving. This can reduce the need to assemble separate CDC, messaging, and stream-processing systems for latency-sensitive pipelines.

Striim is most useful when in-flight processing is a core requirement. For straightforward source-to-warehouse replication, a simpler managed CDC platform may be easier and less costly to operate.

Where Striim fits and where it does not

Striim is a strong fit when transformations are complex and latency-sensitive. If your CDC pipeline is mostly "replicate these tables as-is to Snowflake", Striim's processing layer adds cost without adding value. Use a simpler managed CDC tool for that pattern.

Honest limitations

• Commercial pricing; costs are not publicly listed and scale with data volume and feature usage
• Striim's SQL-based streaming query language has a learning curve, particularly for teams coming from batch ETL backgrounds
• Teams that require long-term event retention and repeated replay should verify how Striim’s persistence and recovery model fits those requirements.
• Self-hosted deployments require JVM-based infrastructure management

7. AWS Database Migration Service (DMS)

Best for: AWS-native database migrations and ongoing replication

CDC method: Transaction-log-based capture for supported databases

Deployment: Fully managed AWS service using provisioned or serverless capacity

AWS Database Migration Service is a managed AWS service for database migrations and ongoing replication.

It can perform an initial full load and then continue capturing changes from supported database transaction logs. AWS DMS is particularly useful for common AWS migration paths and replication into services such as Amazon Redshift, Amazon S3, Amazon RDS, DynamoDB, and Kinesis Data Streams.

AWS manages the replication service, but teams remain responsible for selecting capacity, configuring source and target endpoints, monitoring lag, and validating migration correctness.

The limitations that matter in practice

AWS DMS is a replication tool, not a data integration platform. It does not support complex transformations during replication. The latency on some source/target combinations can drift into the minutes range during high-volume periods. And the tool is firmly tied to AWS -- if you have data sources on-premises or in another cloud, the integration path is more complex.

A pattern worth noting: AWS DMS works well as a bridge into Kinesis Data Streams, where you can then use more capable stream processing (Kinesis Analytics or Flink on EMR) for transformations before landing in Redshift or S3. This separates replication from more advanced stream processing, although it introduces additional services to operate.

Honest limitations

• Primarily AWS ecosystem only; multi-cloud or on-prem-first architectures are awkward
• Latency can degrade under high load; DMS instances need to be right-sized for your change rate
• Limited transformation capabilities -- designed for replication, not enrichment or reshaping
• Pricing is based on replication-instance usage for AWS DMS Standard or DMS Capacity Unit usage for DMS Serverless, with additional storage and data-transfer costs where applicable.

8. Airbyte

Best for: Open-source flexibility, broad connector coverage, and scheduled database CDC

CDC method: Log-based CDC for supported databases, with incremental extraction for other sources

Deployment: Managed cloud, hybrid, open-source, or self-managed enterprise

Airbyte is an open-source data integration platform with connectors spanning databases, SaaS applications, APIs, files, warehouses, and data lakes.

It supports log-based CDC for selected database sources and cursor-based incremental extraction or full refresh for other connectors. Its open connector framework also allows teams to inspect, modify, or build integrations.

CDC approach

For supported databases, Airbyte reads changes from database-native logs or change mechanisms. The first CDC sync creates an initial snapshot, and later syncs resume from the stored source position.

Airbyte runs CDC through scheduled synchronization jobs rather than a continuously running event stream. This model can work well for analytical pipelines where delivery every few minutes is sufficient, but it may not suit operational applications that require continuously emitted events.

Where Airbyte fits

Airbyte is a good fit when:

• You need to connect a broad mix of databases, SaaS applications, APIs, files, and data warehouses.
• You want access to open-source connector code and the ability to customize it.
• You need log-based CDC from one of Airbyte’s supported database sources.
• Scheduled data delivery is sufficient for analytics and warehouse workloads.
• You want to build a missing integration using Airbyte’s Connector Builder or CDKs.

Airbyte offers several sync modes, including full refresh, incremental append, and incremental append with deduplication. This allows teams to choose whether the destination should preserve every incoming version or maintain the latest version of each record.

Airbyte provides managed, hybrid, open-source, and commercial self-managed deployment options. The operational responsibility varies significantly: Airbyte Cloud manages more of the infrastructure, while self-managed editions require teams to handle scaling, upgrades, monitoring, and connector operations.

Pricing model

Airbyte Core is free and open source, but teams are responsible for infrastructure, upgrades, monitoring, and operational support.

Airbyte Cloud uses usage-based credits for its standard plans. Database and file replication is generally measured by data volume, while API and custom-source usage is measured by rows processed. Higher cloud and enterprise plans may use committed data-worker capacity.

Actual costs depend on source type, data volume, sync frequency, and the number of concurrent replication jobs.

Honest limitations

• Airbyte CDC is schedule-driven rather than continuously streaming. Data freshness depends on sync frequency and job duration.
• Most of Airbyte’s 600-plus connectors are not log-based CDC connectors. SaaS, API, and file connectors generally use incremental or full-refresh extraction.
• CDC requires primary keys for incremental replication on most supported database sources. Tables without primary keys may require full refresh.
• CDC captures row-level INSERT, UPDATE, and DELETE operations. Operations such as TRUNCATE and ALTER are not delivered as change records.
• New CDC tables or columns may require schema approval, a stream refresh, and a new snapshot before replication begins.
• Connector support levels vary. Airbyte-maintained connectors generally receive stronger testing and support than community or marketplace connectors.
• Operating Airbyte Core or Self-Managed Enterprise requires Kubernetes infrastructure, upgrades, monitoring, scaling, and connector maintenance.
• Teams must monitor transaction-log retention, PostgreSQL replication slots, sync failures, and stored CDC offsets to prevent log growth or required resyncs.

9. Skyvia

Best for: No-code, scheduled replication for databases and business applications

CDC method: SQL Server Change Tracking and incremental extraction for other supported sources

Deployment: Fully managed SaaS

Skyvia is a fully managed, no-code data integration platform supporting more than 200 databases, cloud applications, data warehouses, and file sources. It provides replication, ETL, synchronization, and other integration capabilities through a visual interface.

Skyvia is particularly useful for small and mid-sized teams that want to move and synchronize data without building or maintaining pipeline infrastructure.

The CDC distinction

Skyvia supports three database replication ingestion modes:

• New: Replicates newly added records using a datetime or auto-incrementing column.
• New and modified: Replicates records inserted or updated since the previous run.
• Log-Based: Uses database change-tracking and logging capabilities to identify inserted, updated, and deleted records.

As of July 2026, Skyvia’s Log-Based ingestion mode is available for SQL Server. For other supported database sources, incremental replication may use datetime or auto-incrementing columns to identify changed records.

Where Skyvia fits

Skyvia is a good fit when:

• You want no-code replication with minimal infrastructure and engineering overhead.
• You need to integrate databases, SaaS applications, data warehouses, and files through one managed platform.
• You are replicating SQL Server data and want a log-based ingestion option.
• Scheduled data freshness is sufficient for analytics, reporting, or operational synchronization.
• Ease of setup and managed operation are more important than controlling the underlying CDC infrastructure.

Honest limitations

• Log-Based ingestion is currently limited to SQL Server. Other supported database replication paths may depend on datetime or auto-incrementing fields.
• The New and New and modified ingestion modes do not detect deleted database records.
• Source schema changes are not detected automatically. Users must refresh the source metadata and edit the replication when source objects or fields change.
• Replication latency depends on the configured schedule, making Skyvia less suitable for sub-second streaming or workloads requiring continuously emitted change events.
• Teams requiring broad log-based CDC coverage across several database engines, advanced stream processing, or detailed control over delivery behavior may need a more specialized CDC platform.

Full comparison: 9 CDC tools side by side

How to choose the right CDC tool for your stack

The comparison table above tells you what each tool does. This section tells you which one to pick based on your actual situation.

Step 1: Determine whether you need true log-based CDC

Ask your DBA whether your source database exposes its transaction log to external tools. For Postgres, this means logical replication must be enabled (wal_level = logical). For MySQL, binary logging must be on (log_bin = ON). For Oracle, you need Supplemental Logging enabled and either LogMiner access or XStream configured.

If log access is available and your use case requires low latency, delete capture, or high-volume replication, log-based CDC is usually the preferred option. If it is not available and cannot be enabled (common in managed database services with restricted configurations or in legacy environments), then incremental sync tools or trigger-based approaches are your fallback options.

Step 2: Decide on managed versus self-hosted

Self-hosted CDC provides greater control over infrastructure, event formats, and deployment, but it also makes your team responsible for scaling, upgrades, monitoring, recovery, and incident response.

Managed CDC reduces that operational burden but introduces platform fees and less control over underlying infrastructure.

Teams that do not already operate Kafka should compare its infrastructure and operational requirements against a managed CDC platform before adopting Debezium.

Step 3: Match the tool to your freshness requirement

Define how quickly changes must reach the destination.

Operational applications, fraud detection, and live inventory systems may require delivery within seconds. Near-real-time analytics may tolerate several minutes, while scheduled warehouse reporting may only require hourly updates.

Avoid paying for sub-second infrastructure when downstream systems consume data every five or fifteen minutes. At the same time, do not choose a scheduled synchronization tool for a workflow that depends on continuously emitted change events.

Quick decision guide

Getting CDC right: the configuration details that matter

These are the things that trip up most teams in their first production CDC deployment, regardless of which tool they use.

Postgres: wal_level and replication slots

Set  wal_level = logical  in postgresql.conf before enabling CDC. On managed Postgres services (RDS, Cloud SQL, Aurora), this is a parameter group setting that usually requires an instance restart.

PostgreSQL CDC tools that use logical replication generally create or use a replication slot. Monitor pg_replication_slots and pg_stat_replication regularly. An inactive or lagging slot can retain WAL and increase storage usage on the source database.

MySQL: binary logging and row format

Ensure  log_bin = ON  and  binlog_format = ROW  in your MySQL configuration. Statement-based and mixed-format binary logs do not support CDC reliably because they do not record the actual row values for all statement types.

Set  binlog_row_image = FULL  so that both the before and after image of every changed row is included in the binary log. Without this, you will only get the changed columns, which makes it difficult to maintain the correct state at the destination.

Schema evolution: plan for it, do not react to it

Schema changes are the most common cause of silent CDC pipeline failures. Adding a nullable column is usually fine. Renaming a column, changing a data type, or dropping a column can break downstream consumers that do not handle schema evolution gracefully.

Before choosing a CDC tool, test your specific schema evolution scenarios against it. Specifically: add a column to a source table with CDC running and verify that the destination handles it correctly without manual intervention. Most managed tools handle this; many self-managed setups require explicit handling.

Delivery guarantees and destination idempotency

Many CDC pipelines use at-least-once delivery, which means retries can produce duplicate events unless the connector or destination provides stronger guarantees.

For warehouse destinations, stable primary keys and idempotent MERGE or UPSERT operations can help maintain the correct final state. For queues, webhooks, and systems that trigger side effects, consumers may need explicit deduplication or idempotency logic.

Exactly-once claims should be evaluated across the complete source-to-destination path. Confirm whether the guarantee applies only inside the CDC platform or also includes the final destination write and checkpoint.

CDC troubleshooting: common problems and fixes

Conclusion

Choosing among change data capture tools comes down to four questions: how the platform captures changes, how quickly data must arrive, how much infrastructure your team can operate, and whether the tool supports your exact sources and destinations.

For teams that want managed continuous CDC, historical backfills, streaming transformations, and multi-destination delivery, Estuary is a strong option. Teams already operating Kafka may prefer Debezium for greater control over event infrastructure. Fivetran fits organizations that prioritize broad managed connectivity, while Qlik Replicate and Oracle GoldenGate address more specialized enterprise replication requirements.

Whatever you choose, test failure and recovery scenarios before going to production. The tool that looks simplest in a demo is not always the one that remains reliable at 3 a.m., when a connector loses its position, a destination falls behind, or an inactive PostgreSQL replication slot causes WAL to accumulate.

Try Estuary for free

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Related reading

• Why you should reconsider Debezium: challenges and alternatives
• Log-based CDC vs traditional ETL: a practical comparison
• The complete CDC guide for PostgreSQL
• Change Data Capture landscape and architecture patterns
• 13 data ingestion tools in 2026 compared