Best CDC Tools (2026): Top 10 Compared

Fivetran is a managed data movement platform operated by Fivetran Inc. Its database connectors use log-based change data capture to read the transaction log of a source system and apply inserts, updates and deletes to a destination warehouse or lake. The product homepage lists more than 900 sources and destinations, covering relational databases such as PostgreSQL, MySQL, Oracle and SQL Server, ERPs including SAP, NetSuite and Workday, and SaaS applications such as Salesforce, HubSpot and Stripe. The company reports handling 33.5 million schema changes per month, which indicates the scale at which the automated schema drift handling operates.

Core capabilities extend past replication. Pipelines land raw data in the destination, then SQL-based transformation models run inside that destination, with dbt Core integration for teams that already maintain models. Destinations include Snowflake, BigQuery, Databricks, Azure and Amazon S3. Deployment options cover the standard cloud service and hybrid deployment, in which the data processing layer runs inside the customer network while control remains in the Fivetran cloud, an arrangement aimed at regulated buyers who cannot route production data through a vendor tenant.

Pricing is metered in monthly active rows. The pricing page lists four plans: Free, Standard, Enterprise and Business Critical. The Free plan includes 500,000 monthly active rows for connections, 3,500 rows for activations and 5,000 monthly model runs. Paid plans do not publish a fixed entry price; consumption is quoted per volume, with the effective rate declining as usage rises, and every paid plan includes 5,000 transformation model runs per month. Higher tiers gate compliance and residency controls. The platform suits organisations that would rather pay for maintained connectors than staff a pipeline team. It fits less well where volumes are large and predictable, since row-based metering can then exceed the cost of a self-managed alternative. Buyers evaluating it should model a month of realistic change volume against the free allowance before signing.

Debezium is an open source change data capture platform distributed under the Apache License 2.0, with the exception of the ANTLR grammars in its DDL parser module, which carry the MIT licence. The project reads the transaction logs of operational databases and emits row-level change events, so that downstream systems can react to inserts, updates and deletes as they are committed rather than polling on a schedule. It is community developed, with connectors maintained in a single public repository.

Connector coverage in the repository includes PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, MongoDB and a generic binlog source, plus a JDBC sink for writing change events back into relational targets. Two deployment shapes are supported. The common one runs connectors on Kafka Connect, using the distributed, scalable and fault-tolerant service to place change events onto Kafka topics. The alternative is the embedded engine, which runs a connector inside an application process and hands events directly to application code, avoiding a Kafka cluster where durable log storage is not required. Debezium Server offers a standalone runtime for sending events to other messaging systems.

There is no price. Cost appears instead as infrastructure and staff time: brokers to run, connector offsets and snapshots to manage, schema registry decisions to make, and upgrades to test. Several commercial products in this category either embed Debezium or implement the same log-reading approach, which makes it a reasonable baseline for evaluating what managed alternatives actually add. Debezium suits teams that already operate Kafka and want full control of event format, ordering and retention. It is a poor fit for analytics teams without platform engineering support, or for organisations that require a vendor support contract, though commercial distributions from Red Hat and others can close that gap. Any evaluation should include the cost of on-call cover for the connectors, since a stalled offset is an operational incident rather than a support ticket.

Airbyte is an open-source data integration platform that moves data into warehouses and lakes and, more recently, into a context layer intended for AI agents. The homepage states more than 600 connectors across both directions of that flow. Database sources support incremental replication, including log-based change data capture for PostgreSQL, MySQL and SQL Server, while SaaS sources use API-based incremental sync. A connector development kit and a low-code builder let teams add sources the catalogue does not cover, which is the main practical difference from closed catalogues.

The product exists in several editions. Core is the open-source distribution, deployed on the customer's own infrastructure with no hosting fee. Cloud is the managed service. Self-Managed Enterprise and Enterprise Flex target organisations that need on-premises or multi-region deployment with support and role-based access. Orchestration integrations cover Airflow, Dagster and Prefect, and the platform exposes a Terraform provider and API for teams that manage pipelines as code.

The pricing page separates data replication from the newer Airbyte Agents product. For replication, Core is listed as always free, Standard starts at $10 per month on volume-based pricing, and Pro and Enterprise Flex are capacity-based and priced through sales using a Data Workers unit. Agents plans are separate: Free at $0 per month with 1,000 agent operations, Individual at $29 per month with 5,000 operations, and Team at $299 per month with 10,000 operations, with overages at $0.004 and $0.005 per operation respectively. Airbyte fits teams that want to start self-hosted and move to a managed plan later, or that need a long tail of niche connectors. It fits less well where a team wants zero operational responsibility and a single predictable invoice from day one. The Core edition also serves as a low-risk way to test connector quality before paying for the hosted plan.

Qlik Replicate, sold within the Qlik Talend Data Integration portfolio, is universal replication software that replicates, synchronises, distributes, consolidates and ingests data across major databases, data warehouses and Hadoop, both on premises and in the cloud. It occupies the segment that most modern ELT vendors do not serve: change capture from legacy transaction systems. Sources listed on the product page include mainframe systems such as IMS/DB, DB2 for z/OS and VSAM, enterprise applications including SAP and Salesforce, and the usual relational estate of Oracle, SQL Server, PostgreSQL and MySQL.

The product offers several capture modes rather than one. Transactional CDC applies changes in committed order to preserve referential integrity. Batch-optimised CDC groups transactions for efficient warehouse ingestion. A warehouse-optimised mode uses native APIs for targets such as Snowflake and Azure Synapse, and a message-oriented mode publishes changes into Apache Kafka and comparable brokers. Qlik describes the capture layer as log-based, zero-footprint technology, meaning no triggers or agents are installed in the source database. Deployment is either client-managed on premises or on virtual machines, or through Qlik Cloud Data Integration as a managed service.

Pricing is not published. The Qlik Talend Cloud pricing page names four editions, Starter, Standard, Premium and Enterprise, and states that customers subscribe to an amount of capacity measured by a combination of data volume moved, number of job executions and execution duration. A free trial is offered, but every commitment requires a quote. The product suits large regulated organisations moving data off mainframes and SAP into cloud analytics platforms, where source coverage matters more than list price. It is not a sensible choice for a small analytics team replicating a handful of PostgreSQL tables, where the licensing overhead and the platform's breadth deliver little in return. Procurement timelines should allow for the capacity sizing exercise that precedes any quote.

Estuary Flow is a real-time data movement platform that combines log-based change data capture, streaming and batch loading in a single pipeline model. The homepage claims delivery latency under 100 milliseconds and more than 200 connectors spanning databases, warehouses, applications and cloud services, with three petabytes processed per month and 99.9 percent stated uptime. Captured change streams are written to a durable collection layer, which allows the same capture to feed multiple destinations and to be replayed into a new target without re-reading the source database.

That collection model is the architectural distinction worth noting. Because change events persist independently of any one destination, adding a warehouse later does not require another snapshot of the production system, which matters for large tables where the initial backfill is the expensive step. Deployment covers three shapes: a public managed service in EU or US regions, private deployment inside the customer's own network, and bring-your-own-cloud on customer-owned infrastructure. Private and BYOC deployments require an annual contract.

Pricing is unusually transparent for this category. The Developer plan is free, limited to 10 GB per month and two concurrent connector instances. The Cloud plan charges $0.50 per GB of data moved plus $100 per month for each of the first six connector instances, with additional instances at $50 per month, billed monthly after a 30-day trial. Enterprise pricing is custom and includes volume discounts. The structure rewards a small number of high-volume pipelines and penalises many low-volume ones, since the per-connector fee applies regardless of throughput. Estuary suits teams that need genuine sub-second replication and want to model costs before committing. It fits less well where the requirement is a long list of niche SaaS connectors, which larger catalogues cover more completely. Consolidating several small sources behind fewer connector instances is the obvious way to control the fixed component of the bill.

Striim is a real-time data integration platform that unifies data across databases, applications and clouds by pairing change data capture with stream processing. The distinguishing feature is that transformation happens in flight: streaming SQL filters, joins, masks and enriches change events between capture and delivery, rather than loading raw rows and transforming them in the destination. For teams that must redact fields before data leaves a boundary, or that need enrichment applied before an operational target receives an event, this removes a downstream stage.

The vendor lists more than 100 connectors. Sources include Oracle, PostgreSQL, MySQL, MongoDB and SQL Server; targets include Snowflake, BigQuery, Azure Synapse, Databricks, Kafka and the major cloud platforms; and application connectors cover Salesforce, ServiceNow, HubSpot, Stripe and Zendesk. The platform also converts relational and unstructured records into vectors for AI workloads, and includes schema drift detection, pipeline monitoring and table-level latency metrics. Deployment is either Striim Cloud, a managed service on AWS, Azure and Google Cloud, or Striim Platform, self-managed on premises or on a cloud virtual machine.

The pricing page lists Striim Developer as free, serverless, supporting up to 25 million events per month with community support and access to more than 200 connectors on trial. Striim Cloud and Striim Platform both state contact us for pricing, with Cloud billed on data moved and Platform sold for unlimited volume under a self-managed licence. A separate Validata line is also quote-only. Striim suits enterprises with operational, low-latency requirements where processing must occur before delivery, particularly in financial services and retail. It is over-specified for a team that only needs raw tables landed in a warehouse on a fifteen-minute cadence, and the absence of published pricing lengthens procurement. Trialling the Developer edition against a live source is the cheapest way to judge whether the streaming SQL layer earns its place.

Hevo Data is a managed ELT platform aimed at analytics teams that do not want to operate pipeline infrastructure. The vendor reports more than 150 pre-built connectors and over 2,000 customers, naming DoorDash, ThoughtSpot and Postman among them. Database replication uses change data capture to sync high-volume databases in near real time without loading the production system, while SaaS sources such as Salesforce, HubSpot, Shopify and Google Analytics are ingested through their APIs. Sources outside the catalogue are built on request rather than through a public developer kit.

Beyond ingestion, Hevo runs transformations in the same product. Teams can model and run dbt projects, SQL jobs and the vendor's own Transformer jobs together, which keeps the load and the model layer under one schedule and one alerting surface. Configuration is deliberately shallow: a source and a destination are connected through a form, schema mapping is automatic with manual override, and pipeline failures surface in a monitoring view. The intended user is an analytics engineer or a data-literate analyst rather than a platform engineer.

The pricing page publishes four plans. Free costs nothing and covers up to one million events per month with limited connectors, hourly scheduling and up to five users. Starter is $265 per month billed annually, or $299 billed monthly, with 5, 20 or 50 million event options. Professional is $750 per month billed annually, or $849 monthly, with 20, 50 or 100 million event options. Business Critical is custom priced. A 14-day trial with support is offered without a card. Hevo suits mid-market teams that value a predictable invoice and quick setup. It fits less well where a rare source is required, or where event volumes are large enough that per-event tiers become expensive against usage-based competitors. Comparing the event ceiling of each tier against current daily change volume is the first step in sizing a plan.

AWS Database Migration Service is Amazon's managed replication service, used both for one-off migrations and for ongoing change data capture between databases. AWS states that customers have migrated more than 1.5 million databases with the service. It supports homogeneous and heterogeneous movement from Oracle, SQL Server, PostgreSQL, MySQL, MongoDB, MariaDB and other engines, with an accompanying schema conversion capability that AWS reports converts up to 90 percent of a schema automatically. Replication tasks run in full load, full load plus CDC, or CDC-only mode, the last of which reads the source transaction log continuously.

Operationally, the service is infrastructure rather than a pipeline product. A replication instance, or a serverless equivalent, sits between source and target and runs tasks that the customer configures and monitors. Security integrates with IAM policies, SSL/TLS and AWS Secrets Manager, and Multi-AZ deployment provides redundancy for long-running replication. There is no transformation layer beyond table mapping rules and simple column transformations, so modelling belongs downstream.

Pricing has three shapes: on-demand replication instances billed hourly across T2, T3, C4, C5, C6i, R4, R5 and R6i classes, DMS Serverless billed on data-handling capacity units where one DCU equals 2 GB of RAM, and Database Savings Plans for committed use. T3 instances incur CPU credit charges of $0.075 per vCPU hour when average utilisation exceeds baseline over 24 hours. Accounts created before 15 July 2025 receive 750 hours per month of a Single-AZ dms.t3.micro instance with 50 GB storage; later accounts choose between a free plan and a paid plan with $100 in credits. Data transfer into DMS, and between DMS and RDS or EC2 in the same availability zone, is free. The service suits AWS-centric teams. It is a weak choice when destinations are outside AWS. Long-running replication tasks also warrant CloudWatch alarms, since a failed task is not restarted automatically.

BladePipe is a data integration and change data capture platform that builds end-to-end pipelines between more than 60 data sources for analytics and AI workloads. Its scope covers incremental synchronisation, full migration, in-flight data processing and bidirectional sync, the last of which is less common in this category and matters for active-active database topologies. The vendor advertises sub-three-second sync latency and states it is faster than larger competitors, a claim that should be validated against a representative workload rather than accepted from the marketing page.

Connector coverage centres on operational databases and analytical stores: MySQL, PostgreSQL, Oracle, SQL Server, MongoDB and Kafka as sources, and Redshift, ClickHouse, Doris, StarRocks and Snowflake among targets, alongside cloud services such as AWS RDS, DynamoDB and S3. More recent connectors write into vector and model endpoints for AI pipelines. Schema migration and DDL synchronisation are included at every tier, so structural changes in a source propagate without a manual re-map. Three deployment models are offered: managed cloud SaaS, bring-your-own-cloud, and on-premises enterprise, all supporting high-availability configurations.

The pricing page lists a free Community edition for on-premises use, capped at five sync tasks, 500 tables per task and a single user, with community support only. The Cloud plan is usage-based at $0.01 per million rows of data, billed monthly after a free trial, and raises limits to 30 sync tasks with no table cap. The Enterprise on-premises edition is priced by contract and includes multitenancy and role-based access control, with a 90-day free period. BladePipe suits cost-sensitive teams replicating between databases at volume. It is a weaker fit for organisations that require a large SaaS connector catalogue or an established vendor relationship. A proof of concept on production-scale tables is advisable before relying on the advertised latency figures. Support expectations should also be agreed in writing, since the free edition carries community support only.

Skyvia is a cloud data platform from Devart that groups several data jobs into one subscription: integration, replication, backup and querying. For change data capture specifically, it supports real-time capture and incremental updates when moving data from applications and databases into warehouses, alongside conventional ETL, ELT, import, export and two-way synchronisation modes. The vendor lists more than 200 ready-to-use connectors spanning cloud applications such as Salesforce, HubSpot and Shopify, databases including PostgreSQL, Oracle, SQL Server and MySQL, cloud storage services, and warehouses including Snowflake, BigQuery, Redshift and Azure Synapse.

The product is built for users without engineering support. Pipelines are configured through a browser interface with no code, backups run daily on a schedule or on demand, and a visual query builder plus a SQL editor allow data to be inspected directly in the browser rather than through a separate client. That breadth is the trade-off: each capability is adequate rather than deep, and buyers whose sole requirement is high-throughput log-based replication will find more specialised tools elsewhere in this comparison.

Pricing is published by record volume. The Free plan costs nothing and covers 10,000 records per month, daily scheduling and two scheduled integrations that expire after 30 days. Basic is $79 per month billed annually or $99.79 billed monthly. Standard is $159 billed annually or $199 monthly. Professional is $399 billed annually or $499 monthly and carries unlimited records. Enterprise is quoted and provides a dedicated integration environment. Annual billing saves 20 percent at each paid level. Skyvia suits small businesses and departmental teams that need several data tasks handled cheaply under one contract. It is not appropriate for high-volume streaming replication, where record ceilings and scheduling granularity become limiting. Record counts should be estimated across all four capabilities, since integration, replication and backup jobs draw on the same monthly allowance.