7 Best Change Data Capture (CDC) Tools in 2026 (Compared & Reviewed)

The best Change Data Capture (CDC) tools in 2026 include Estuary, Debezium, Qlik Replicate, Oracle GoldenGate, Striim, AWS Database Migration Service (DMS), and Skyvia.

These tools differ primarily in how they capture and deliver data changes. Some rely on log-based CDC for continuous, low-latency replication, others focus on managed or cloud-native CDC, and some provide incremental change tracking suited for simpler synchronization needs rather than real-time streaming.

Choosing the right CDC tool depends on several factors, including whether you need real-time or near-real-time replication, how much operational overhead your team can support, the databases and destinations you work with, and whether your environment is cloud, hybrid, or on-premises.

This guide provides an objective comparison of the leading CDC tools in 2026. You’ll learn where each option fits best and how to choose the right CDC solution based on latency, scalability, reliability, deployment model, and operational complexity.

What Is a CDC Tool?

A Change Data Capture (CDC) tool is software that identifies and captures changes made to data in a source system and delivers those changes to downstream systems. Instead of copying full tables repeatedly, CDC tools track inserts, updates, and deletes and replicate only what has changed.

CDC tools typically work in one of three ways:

• Log-based CDC: These tools read database transaction logs to capture changes as they occur. This approach provides low latency, minimal impact on the source database, and accurate ordering of changes. Log-based CDC is commonly used for real-time replication and streaming analytics.
• Incremental CDC: Incremental CDC tools detect changes by comparing timestamps, version columns, or primary keys. While simpler to set up, this approach is usually less real-time and can introduce higher load or missed edge cases if not configured carefully.
• Continuous vs scheduled capture: Some CDC tools stream changes continuously with sub-second or near real-time latency, while others run on frequent schedules (for example, every few minutes). The right choice depends on how fresh your downstream data needs to be and how much operational overhead your team can support.

Why Use CDC Tools?

CDC tools are a core component of modern data architectures because they enable systems to stay synchronized without relying on heavy batch jobs.

• Real-time analytics: CDC tools keep analytics platforms, dashboards, and AI models continuously updated with fresh data, enabling faster and more accurate decision-making.
• Operational synchronization: They ensure that downstream systems, such as search indexes, caches, or microservices, reflect changes in operational databases with minimal delay.
• Reduced load compared to batch replication: By capturing only changes instead of full datasets, CDC tools significantly reduce database load, network usage, and processing costs.
• Improved data freshness: CDC eliminates long delays between updates, making it possible to work with near real-time or real-time data rather than hours-old snapshots.

How We Evaluated the Best CDC Tools

To identify the best Change Data Capture tools in 2026, this guide evaluates each option using practical, production-focused criteria. These factors help compare tools objectively across different architectures and team requirements.

• CDC method: Whether the tool uses log-based CDC, incremental capture, or a hybrid approach. Log-based CDC is generally preferred for low latency and accuracy.
• Latency and delivery guarantees: How quickly changes are delivered downstream and whether the tool supports at-least-once or exactly-once delivery semantics.
• Operational complexity: The amount of infrastructure, configuration, and ongoing maintenance required. This includes cluster management, upgrades, monitoring, and failure recovery.
• Scalability and reliability: How well the tool handles growing data volumes, high change rates, and mission-critical workloads without data loss or instability.
• Integration and ecosystem: Support for common databases, data warehouses, streaming systems, and cloud services, along with the maturity of connectors and tooling.
• Cloud and hybrid support: The ability to operate in cloud, on-premises, or hybrid environments, which is critical for organizations modernizing legacy systems.
• Pricing transparency: Whether costs are predictable and clearly tied to usage, and how pricing scales as data volumes and change rates increase.

Best Change Data Capture (CDC) Tools in 2026

Below are the leading Change Data Capture (CDC) tools in 2026, evaluated based on real-time replication capabilities, scalability, reliability, and operational fit. Each tool serves a different use case, from open-source log-based CDC to fully managed enterprise platforms.

1. Estuary

Estuary is a right-time data platform designed to move operational data reliably using change data capture (CDC), continuous streaming, or scheduled batch pipelines — all within one system. Unlike traditional CDC tools that focus only on replication, Estuary supports end-to-end data movement across operational databases, SaaS systems, data warehouses, and data lakes.

Estuary captures changes directly from database logs for supported sources, ensuring low-latency delivery with exactly-once semantics where supported. At the same time, it supports historical backfills, replays, and scheduled batch processing, allowing teams to manage real-time and batch workflows without stitching together multiple tools.

The platform is fully managed but offers enterprise-grade deployment flexibility, including private networking, secure connectivity, and Bring Your Own Cloud (BYOC) options. This makes Estuary suitable for cloud, hybrid, and regulated environments where operational simplicity and data correctness are equally important.

Strengths

• Unified CDC and batch pipelines: Run continuous CDC streams and scheduled backfills in the same platform.
• Exactly-once delivery guarantees: Designed for correctness when streaming database changes into analytics and operational systems.
• Broad connector ecosystem: Supports major relational databases, SaaS tools, data warehouses, and cloud storage systems.
• Enterprise deployment options: Private networking, secure access controls, and BYOC support for compliance and data residency needs.
• Low operational overhead: No brokers, clusters, or stream processors to manage.
• Schema enforcement and evolution: Built-in handling of schema changes with controlled evolution and replay support.

Limitations

• Requires CDC to be enabled on supported source databases.
• Not intended as a general-purpose message broker for arbitrary application events.
• Advanced use cases benefit from familiarity with CDC concepts and data modeling.

Best for

Teams that want a single platform for CDC, streaming ingestion, and batch pipelines, especially when data correctness, operational simplicity, and enterprise deployment flexibility are priorities. Estuary is particularly well-suited for syncing operational databases into analytics systems, powering real-time reporting, and maintaining consistent data movement across cloud and hybrid environments.

2. Debezium

Debezium is a widely used open-source Change Data Capture (CDC) platform built on Apache Kafka. It focuses on log-based CDC, capturing row-level inserts, updates, and deletes directly from database transaction logs and emitting them as ordered event streams.

Rather than acting as a full data integration platform, Debezium serves as a CDC engine that streams database changes into Kafka topics. From there, downstream systems consume those events for analytics, replication, or event-driven applications.

Debezium supports many popular databases, including MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, and Db2, and is commonly used as the CDC foundation in Kafka-centric architectures.

Strengths

• True log-based CDC: Reads database transaction logs (binlog, WAL, redo logs) with minimal impact on source systems.
• Strong ordering guarantees: Preserves transaction order and change semantics, which is critical for correctness.
• Broad database support: Works across many relational and NoSQL databases.
• Kafka-native integration: Fits naturally into event-driven and streaming architectures built around Kafka.
• Open source and extensible: Transparent internals, active community, and no licensing fees.

Limitations

• Requires Kafka infrastructure: Debezium depends on Kafka and Kafka Connect, which increases operational complexity.
• Not a full pipeline solution: Does not handle delivery, transformations, or batch backfills by itself.
• Operational overhead at scale: Managing connectors, offsets, schema evolution, and failures requires experienced engineers.
• Limited out-of-the-box governance: Lineage, access controls, and observability usually require additional tooling.

Best for

Engineering teams that want open-source, log-based CDC and are already running Kafka (or plan to), especially for building event-driven systems, database replication pipelines, or real-time analytics workflows where full control and transparency matter.

Debezium is ideal when CDC accuracy and flexibility are more important than having a fully managed, end-to-end CDC platform.

3. Qlik Replicate

Qlik Replicate is an enterprise-grade Change Data Capture (CDC) platform designed for high-volume, low-latency data replication across on-premises, cloud, and hybrid environments. It focuses on reliably moving database changes in near real time from operational systems into data warehouses, lakes, and other databases.

Qlik Replicate uses log-based CDC where supported, capturing inserts, updates, and deletes directly from source database logs and continuously applying them to target systems. It is widely used in large enterprises for operational replication, analytics pipelines, and system modernization projects.

Unlike open-source CDC tools, Qlik Replicate emphasizes ease of setup, monitoring, and reliability over deep customization, making it attractive for teams that want strong CDC guarantees without managing streaming infrastructure themselves.

Strengths

• High-performance log-based CDC: Designed for large, mission-critical databases with sustained change volumes.
• Broad platform support: Works with many enterprise databases and targets, including Oracle, SQL Server, DB2, SAP, Snowflake, Redshift, and BigQuery.
• Near real-time replication: Low-latency delivery suitable for analytics and operational sync.
• Simplified operations: Visual configuration, centralized monitoring, and built-in error handling.
• Enterprise reliability: Mature product with strong support, SLAs, and production hardening.

Limitations

• Commercial pricing: Licensing and total cost of ownership can be high for smaller teams.
• Limited transformation capabilities: Focuses on replication; complex transformations are typically handled downstream.
• Less flexible than open-source stacks: Custom logic and deep pipeline control are constrained compared to DIY Kafka-based solutions.
• CDC-focused only: Not designed for general-purpose event streaming or batch processing.

Best for

Enterprises that need reliable, high-throughput CDC from operational databases into analytics platforms or secondary systems, and that prioritize stability, support, and ease of operation over open-source flexibility.

Qlik Replicate is best suited for organizations running large-scale, mission-critical CDC pipelines where correctness and uptime matter more than customization.

4. Oracle GoldenGate (OCI GoldenGate)

Oracle GoldenGate is a long-established, enterprise-grade Change Data Capture (CDC) platform built for high-volume, low-latency replication across heterogeneous systems. It is widely used in large enterprises to replicate mission-critical data with strong consistency and availability guarantees.

GoldenGate captures changes directly from database transaction logs and delivers them in real time to one or more target systems. While it originated as an on-premises product, Oracle now offers OCI GoldenGate, a fully managed cloud service that supports Oracle and non-Oracle databases across cloud, on-premises, and hybrid environments.

GoldenGate is often chosen when data correctness, uptime, and enterprise support are more important than simplicity or cost.

Strengths

• Mature log-based CDC: Proven technology with decades of use in mission-critical systems.
• Very low latency replication: Designed for near-zero data lag even at high volumes.
• Broad database support: Strongest for Oracle databases, with support for SQL Server, MySQL, PostgreSQL, and others.
• High availability options: Active-active replication, bi-directional sync, and disaster recovery scenarios.
• Enterprise support and SLAs: Backed by Oracle with long-term stability guarantees.

Limitations

• High cost: Licensing and operational costs are among the highest in the CDC market.
• Complex setup and operation: Requires experienced DBAs and careful configuration.
• Oracle-centric: Works best in Oracle-heavy environments; non-Oracle use cases can be more complex.
• Limited flexibility for modern analytics pipelines: Often paired with other tools for transformations and downstream processing.

Best for

Large enterprises running Oracle-centric or mixed enterprise database environments that need mission-critical CDC, strict uptime requirements, and proven replication at scale.

Oracle GoldenGate is best suited for core operational replication, disaster recovery, and enterprise modernization projects, where reliability and vendor support outweigh cost and complexity concerns.

5. Striim

Striim is an enterprise-grade Change Data Capture (CDC) and real-time data streaming platform designed for continuous, low-latency data movement across databases, cloud services, and analytics systems. Originally built by engineers from the Oracle GoldenGate team, Striim focuses on delivering reliable, real-time CDC with in-flight processing.

Striim captures changes directly from database logs and streams them as events, while also supporting real-time filtering, enrichment, and transformations using its SQL-like streaming language. It is available as a fully managed cloud service as well as for enterprise and hybrid deployments.

Striim is commonly used in scenarios where real-time replication and transformation must happen together, especially in regulated or high-availability environments.

Strengths

• Low-latency, log-based CDC: Streams inserts, updates, and deletes in near real time.
• In-flight processing: Supports filtering, enrichment, joins, and aggregations on streaming data.
• Enterprise reliability: Built-in fault tolerance, monitoring, and high availability.
• Broad source and target support: Databases, cloud warehouses, messaging systems, and storage.
• Managed and enterprise options: Suitable for large-scale, production-critical deployments.

Limitations

• Commercial pricing: Costs can be high for large data volumes or many pipelines.
• Learning curve: Requires familiarity with Striim’s streaming concepts and query language.
• Limited batch-first workflows: Less flexible for heavy batch processing or historical reprocessing.
• Vendor-specific runtime: Less portable than open-source CDC stacks.

Best for

Enterprises that need real-time CDC with built-in stream processing, strong uptime guarantees, and minimal tolerance for data lag or loss.

Striim is best suited for mission-critical CDC pipelines where real-time replication and transformation must happen together, and where enterprise support and reliability are top priorities.

6. AWS Database Migration Service (DMS)

AWS Database Migration Service (DMS) is a cloud-native data replication and migration service designed to move data into and within the AWS ecosystem. While it is often used for one-time migrations, AWS DMS also supports ongoing change data capture (CDC) to keep source and target databases synchronized in near real time.

AWS DMS captures changes from database transaction logs and continuously applies them to a target system such as Amazon Redshift, Amazon S3, Aurora, RDS databases, or other supported engines. It is fully managed by AWS, which removes the need to operate CDC infrastructure manually.

DMS is commonly used for AWS-centric architectures where CDC is required to replicate operational data into analytics systems or to support gradual cloud migrations.

Strengths

• Fully managed CDC service: No infrastructure or CDC tooling to operate.
• Log-based change capture: Streams inserts, updates, and deletes with low source impact.
• Tight AWS integration: Works seamlessly with RDS, Aurora, Redshift, S3, and other AWS services.
• Supports heterogeneous replication: Move data across different database engines.
• Simple setup for common use cases: Well-documented workflows for migrations and replication.

Limitations

• Primarily AWS-focused: Less suitable for multi-cloud or on-prem-first architectures.
• Limited transformations: Designed for replication, not complex in-flight processing.
• Latency can vary: Near real-time, but not sub-second in all scenarios.
• Operational tuning required at scale: Task sizing, instance types, and throughput need monitoring.

Best for

Teams operating primarily on AWS that need a managed, low-effort CDC solution for database replication, analytics ingestion, or incremental cloud migrations.

AWS DMS is best suited for organizations that want CDC without running Kafka, Debezium, or custom replication infrastructure, and are comfortable with AWS-native tooling and constraints.

7. Skyvia

Skyvia is a cloud-based data integration platform that offers incremental data replication often positioned as CDC, but technically distinct from log-based, real-time CDC tools. Instead of reading database transaction logs, Skyvia relies on incremental queries and change tracking mechanisms to detect and sync data changes on a scheduled basis.

Skyvia is designed for simplicity and accessibility, targeting teams that want to keep systems reasonably in sync without managing infrastructure or complex CDC pipelines. It is commonly used for syncing operational databases, SaaS applications, and cloud data platforms with minimal configuration.

While Skyvia does not deliver sub-second, log-based CDC, it fills an important niche for lightweight, low-ops replication where strict real-time guarantees are not required.

Strengths

• No-code, cloud-native platform: Easy to configure without deep engineering expertise.
• Incremental replication: Transfers only changed data to reduce load and bandwidth.
• Broad connector support: Databases, SaaS applications, and file-based systems.
• Fully managed service: No infrastructure, agents, or CDC runtimes to maintain.
• Affordable entry point: Accessible pricing for small teams and simple use cases.

Limitations

• Not true log-based CDC: Changes are detected via queries or timestamps, not transaction logs.
• Higher latency: Sync frequency depends on schedules, not continuous streaming.
• Limited scalability for very large datasets: Not ideal for high-volume, high-velocity change streams.
• Fewer delivery guarantees: Less control over ordering and exactly-once semantics.

Best for

Teams that want a simple, low-cost way to replicate data incrementally between systems, prioritize ease of use over real-time guarantees, and do not require log-based CDC or sub-second latency.

Skyvia is best suited for business teams, small data stacks, and operational sync use cases where simplicity and speed of setup matter more than advanced CDC correctness or scale.

Comparison Table: Best CDC Tools in 2026

The best Change Data Capture (CDC) tools in 2026 include Estuary for unified real-time CDC and batch pipelines, Debezium for open-source log-based replication, Qlik Replicate and Oracle GoldenGate for enterprise-scale CDC, Striim for low-latency streaming with in-flight processing, AWS DMS for cloud-native CDC on AWS, and Skyvia for lightweight incremental replication.

The right CDC tool depends on whether you need true log-based real-time CDC, a fully managed platform, enterprise-grade reliability, or a simpler incremental sync approach with lower operational complexity.

How to Choose the Right CDC Tool

Choosing the right Change Data Capture (CDC) tool depends on how your data changes are generated, how quickly they need to be delivered, and how much operational complexity your team can support. The following factors help narrow down the best option for your architecture.

1. Log-Based CDC vs Incremental Replication

Not all CDC tools capture changes the same way.

Log-based CDC tools read database transaction logs to capture inserts, updates, and deletes as they occur. This approach offers low latency and minimal impact on source systems. Tools like Debezium, Qlik Replicate, Oracle GoldenGate, Striim, and Estuary fall into this category.

Incremental replication tools rely on timestamps, primary keys, or polling queries to detect changes. These tools are simpler to set up but typically operate in batches and cannot guarantee true real-time delivery. Skyvia is an example of this approach.

If you need accurate, continuous replication with minimal lag, log-based CDC is the preferred option.

2. Real-Time vs Near Real-Time Requirements

Latency requirements vary by use case.

Choose real-time or near real-time CDC tools when data freshness is critical, such as for operational dashboards, fraud detection, or customer-facing systems. Log-based platforms like Estuary, Striim, Debezium, and GoldenGate are designed for continuous delivery.

If updates every few minutes or hours are sufficient, incremental or batch-oriented tools may be acceptable and easier to manage.

Clearly defining acceptable latency helps avoid over-engineering or under-delivering.

3. Managed vs Self-Hosted Operations

Operational overhead is often a deciding factor.

Self-hosted CDC tools provide flexibility and control but require teams to manage infrastructure, scaling, monitoring, and upgrades. Debezium and GoldenGate deployments typically fall into this category unless paired with managed services.

Managed CDC platforms reduce operational burden by handling infrastructure, fault tolerance, and scaling automatically. Tools such as Estuary, Striim Cloud, AWS DMS, and Skyvia are designed for teams that want faster time to value with less ongoing maintenance.

Teams with limited streaming or infrastructure expertise often benefit from managed solutions.

4. Cloud, Hybrid, or On-Prem Deployment

Your deployment model influences which tools are viable.

Some CDC tools are optimized for specific cloud ecosystems, such as AWS DMS for AWS-centric environments or Oracle GoldenGate for Oracle-heavy stacks.

Others support hybrid and multi-cloud architectures, allowing data to move consistently between on-premises databases and cloud destinations. Estuary, Debezium, Qlik Replicate, and Striim are commonly used in these scenarios.

If your architecture spans multiple environments, prioritize tools with flexible networking and deployment options.

5. Transformation and Data Handling Needs

CDC tools vary in how much transformation they support during data movement.

Some tools focus strictly on replication and expect transformations to happen downstream. Others allow light filtering, enrichment, or routing as data flows through the pipeline.

If you need to combine CDC with streaming ingestion and batch backfills in one system, platforms that unify these patterns can reduce the need for multiple tools and orchestration layers.

6. Cost Predictability and Scale

CDC workloads often grow over time, so pricing models matter.

Usage-based pricing tied to data volume or compute can scale efficiently but should remain predictable as change rates increase. Enterprise tools may carry higher licensing costs but offer stability and support guarantees.

Evaluating how pricing scales with data growth helps avoid surprises as pipelines move into production.

Summary

The right CDC tool depends on whether you need log-based or incremental capture, real-time or batch delivery, managed or self-hosted operations, and cloud or hybrid support. By aligning tool selection with latency requirements, operational capacity, and long-term scalability, teams can build reliable CDC pipelines that keep data synchronized without unnecessary complexity.

Conclusion

Choosing the right Change Data Capture (CDC) tool depends on how your organization needs to move data, how quickly that data must arrive, and how much operational complexity your team can manage. Log-based CDC tools like Debezium and Oracle GoldenGate are well suited for teams that need precise, low-latency change capture and are comfortable managing infrastructure or enterprise software. Platforms such as Qlik Replicate and Striim provide enterprise-grade replication with strong reliability and monitoring, while cloud-native options like AWS DMS simplify CDC for teams operating primarily within a single cloud ecosystem.

For teams that want to combine real-time CDC with streaming and batch pipelines in a single system, Estuary offers a unified approach that reduces the need to stitch together multiple tools. By supporting sub-second CDC, scheduled backfills, and managed delivery across cloud and hybrid environments, it addresses many of the operational challenges that arise when scaling CDC pipelines.

There is no single best CDC tool for every use case. The right choice is the one that aligns with your latency requirements, source systems, deployment model, and long-term maintenance strategy. By clearly understanding these factors, teams can select a CDC solution that keeps data accurate, timely, and ready for analytics or operational use as data volumes and complexity grow.

Learn more about Estuary if you need sub-second CDC with batch backfills in a single managed platform. A free tier is available to explore CDC pipelines without infrastructure setup.

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