TL;DR This guide compares 11 change data capture (CDC) tools for streaming database changes in near real time.
Open-source: Airbyte, Debezium and Kafka Connect give you log-based CDC without licence costs, at the price of running more yourself.Cloud-native: AWS Database Migration Service, Azure Data Factory and Google Cloud Dataflow fit teams already standardised on one cloud.Enterprise replication: Oracle GoldenGate, Qlik Replicate, IBM InfoSphere, Striim and Hevo Data add support, SLAs and broad legacy source coverage.Why CDC at all: it replaces repeated full reloads with incremental change streams, cutting source load, warehouse cost and data latency.Change data capture tools track every insert, update and delete in your source databases and stream those changes downstream in near real time. Without CDC, keeping a warehouse current means repeated full reloads that grow slower and more expensive as your data does. The eleven CDC tools below differ in latency, source coverage and how much infrastructure you have to run yourself.
What is Change Data Capture? Change Data Capture (CDC) is identifying and capturing changes made to data in a database. It enables monitoring and tracking of modifications such as inserts, updates, and deletes, allowing systems to stay synchronized and updated with real-time changes. CDC is commonly used in data integration, replication, and warehousing scenarios, facilitating efficient and timely updates across different applications and ensuring data consistency.
CDC typically involves:
Assigning timestamps or sequence numbers to changes to maintain an order and track them accurately. Log-based implementation, where the database transaction log is examined for changes. CDC can also be carried out through the trigger-based approach, where triggers on tables capture changes when they occur. Propagating the captured changes to other systems or data repositories to ensure consistent and up-to-date information flow across different distributed system components. How Do the Top CDC Tools Compare? Tool Type CDC Method Latency Deployment Pricing Model Airbyte Open-source ELT Log-based, via embedded Debezium Near real time Cloud, hybrid (Flex), self-managed Free OSS; capacity-based paid tiers Debezium Open-source CDC Log-based Real time Self-managed, needs Kafka Free, open source Striim Streaming integration Log-based Real time Cloud, on-premises Free tier, then subscription AWS DMS Cloud-native replication Log-based Near real time AWS Hourly, per instance Oracle GoldenGate Enterprise replication Log-based Real time On-premises, OCI Enterprise licence Qlik Replicate Enterprise replication Log-based, agentless Real time On-premises, cloud Enterprise licence IBM InfoSphere Enterprise replication Q and SQL replication Low latency On-premises, IBM Cloud Enterprise licence Kafka Connect Streaming framework Via source connectors Real time Self-managed, Confluent Free; Confluent tiers paid Azure Data Factory Cloud-native ETL CDC activity and data flows Near real time Azure Consumption-based Hevo Data Managed ELT Log-based Near real time Cloud Event-based tiers Google Cloud Dataflow Stream processing Event-driven, needs Pub/Sub Real time Google Cloud Consumption-based
Here are the eleven CDC tools worth comparing, covering open-source, cloud-native and enterprise replication options:
Which Are the Best CDC Tools? 1. Airbyte Airbyte is an open data movement platform that replicates data from many sources into warehouses, lakes and databases. It supports log-based CDC , reading the database transaction log rather than querying tables, and uses Debezium as an embedded library to capture INSERT, UPDATE and DELETE operations continuously. That means you get log-based CDC without standing up and operating Kafka yourself.
Key features of Airbyte include:
Availability of Connectors: Airbyte provides 600+ pre-built connectors . If none covers your source, the Connector Development Kit (CDK) and Connector Builder let you build a custom one.AI-powered Connector Creation: The Connector Builder supports an AI-assist functionality that reads through your preferred platform’s API documentation. Based on the API docs, it auto-fills most configuration fields, simplifying your custom connector development journey. Simplify GenAI Workflows: With Airbyte, you can simplify your AI workflows by loading semi-structured and unstructured data into popular vector databases, including Milvus, Pinecone, and Qdrant. Migrating data into a vector database allows you to train AI models quickly. Homogeneous and Heterogeneous Migrations: Airbyte supports both homogeneous and heterogeneous migrations. This allows you to replicate data between the same sources (e.g., MySQL to MySQL) and with different database engines (e.g., MySQL to PostgreSQL).Developer-friendly Data Pipeline: Airbyte’s PyAirbyte is an open-source Python library that allows you to utilize the Airbyte connectors. This is effective for custom data integration and transformation requirements using Python programming. Enterprise Flex: Airbyte runs the control plane for orchestration, scheduling and monitoring, while the data plane runs inside your own cloud, VPC or data centre. Replicated records, credentials and encryption keys never leave your boundary, connections are outbound only, and you can bring your own KMS. It adds role-based access control, column hashing, SSO, audit logging, region pinning and air-gapped deployment.Pricing Airbyte offers a free open-source version alongside paid plans spanning managed Cloud and Enterprise Flex. Pricing is capacity-based rather than per row, so costs stay predictable as replication volume grows. For details, contact the Airbyte sales team .
Pros Cons Log-based CDC without running Kafka yourself Near real time rather than sub-second latency 600+ connectors covering databases and SaaS sources Self-managed deployment needs Kubernetes or Docker skills Handles homogeneous and heterogeneous replication Transformation relies on dbt rather than being built in Enterprise Flex keeps replicated data inside your own VPC Flex pricing is custom and needs a sales conversation Capacity-based pricing keeps costs flat as volume grows CDC support varies by source database
2. Debezium Debezium is an open-source distributed platform designed to capture changes in data. It is built on top of Apache Kafka, a popular streaming platform. Debezium is developed to monitor the transaction logs and capture events representing the modifications to the data. It provides connectors for various database management systems (DBMS) like PostgreSQL, MySQL, and MongoDB. These connectors allow you to capture database changes in real time and stream them to Kafka topics for further processing.
Here’s key aspects of Debezium:
If a Debezium source connector generates a change event for a table without an existing target topic, the topic is created during runtime. The change events are subsequently ingested into Kafka. Debezium lets you mask the values of specific columns in a schema. This feature is especially useful when your dataset contains sensitive data. Pricing As Debezium is an open-source platform, it is free of cost for use.
Pros Cons Free, open source and the de facto CDC standard Requires Kafka, which is significant infrastructure Mature connectors for PostgreSQL, MySQL and MongoDB No commercial support or SLA Column masking protects sensitive fields in transit You own hosting, monitoring and upgrades Creates target topics automatically at runtime Steep learning curve for teams new to Kafka
3. Striim Striim is a software outlet designed for real-time data integration and streaming analytics. It allows your organization to continuously collect, process, and deliver data from various sources, including databases, applications, and sensors. Striim also facilitates data migration from on-premises databases to cloud environments without downtime and keeps them up-to-date using CDC.
Here are some features of Striim:
Multiple stream sources, windows, and caches can be combined in a single query and chained together in directed graphs, known as data flows. These data flows can be built through the UI or the TQL scripting language. You can easily deploy and scale across a Striim cluster without writing additional code. You can use Striim for OpenAI and parse any type of data from one of Striim’s 100+ streaming sources into the JSONL format. It can be easily uploaded to OpenAI for creating AI models. Pricing Striim offers a free Developer edition alongside paid tiers covering automated data streams, Cloud Enterprise and Cloud Mission Critical. Pricing scales with throughput and features, so contact Striim for current figures.
Pros Cons Real-time streaming analytics alongside replication Enterprise pricing beyond the free Developer tier TQL scripting and UI both available for data flows TQL is a proprietary language to learn Migrates on-premises databases to cloud without downtime Smaller community than open-source alternatives 100+ streaming sources with OpenAI output support Cluster deployment adds operational complexity
4. AWS Database Migration Service Managed by AWS, Database Migration Service (DMS) helps you replicate your databases. You can set up CDC to capture changes while you are migrating your data from the source to the target data. Additionally, you can create a task to capture the ongoing changes from the source data. This ensures that any modifications that occur during the migration process are also replicated in the target system.
Here are the features of AWS DMS:
DMS supports a wide range of database engines including Oracle, SQL Server, PostgreSQL, MySQL, MongoDB and MariaDB, so you can replicate regardless of the platform you are starting from. AWS offers a serverless option with AWS DWS Serverless. This option automatically provisions, monitors, and scales resources, simplifying the migration process and eliminating the need for manual configuration. It is particularly beneficial for scenarios where diverse database engines are involved. Pricing AWS DMS charges hourly based on the replication instance size and log storage used. Check the AWS pricing page for current rates in your region.
Pros Cons Broad database engine support for migration and replication Strains on very large volumes and long-running tasks Serverless option provisions and scales automatically Databases only, so SaaS sources need another tool Captures ongoing changes during a migration Targets inside AWS, limiting cross-cloud replication Fully managed with deep AWS integration Schema conversion often needs manual work
5. GoldenGate (Oracle) Oracle GoldenGate is a real-time data integration and replication platform from Oracle Corporation. It facilitates the real-time movement of data between different types of databases and platforms without impacting the performance of the source system. It allows you to capture data changes as they occur and replicate them timely to the target system.
The features of Oracle GoldenGate include:
GoldenGate offers Stream Analytics, which gives you access to features such as time series, machine learning, geospatial, and real-time analytics. Along with Oracle repositories, GoldenGate allows you to connect with many non-Oracle databases and data services for data integration. The databases supported by OCI are Microsoft SQL Server, IBM DB2, Teradata, MongoDB, MySQL, PostgreSQL, etc. Pricing OCI offers different pricing versions for different needs, and you can contact their sales team for more details.
Pros Cons Real-time replication with minimal source impact Among the most expensive options here Connects to Oracle and many non-Oracle databases Complex to configure and administer Stream Analytics adds time series and geospatial analysis Requires specialist GoldenGate skills Proven at very large enterprise scale Deepens commitment to the Oracle ecosystem
6. Qlik Replicate Qlik Replicate is a real-time data replication and ingestion platform. It enables organizations to implement CDC by continuously capturing and delivering changes from various data sources to target systems with minimal latency. Qlik Replicate supports a wide range of databases, data warehouses, and cloud platforms.
Two key features of Qlik Replicate
Zero-footprint architecture: Qlik Replicate can capture changes from source systems without installing agents or performing intrusive operations on production servers. Wide platform support: It offers connectivity to a vast array of databases, data warehouses, and cloud platforms, allowing for flexible data movement across diverse environments. Pricing Qlik Replicate's pricing is not publicly available as it typically follows an enterprise pricing model. To get accurate pricing information, organizations need to contact Qlik's sales team for a custom quote tailored to their specific needs and usage scenarios.
Pros Cons Zero-footprint architecture needs no agents on source servers Pricing is not published and requires a sales quote Very broad platform and database support Enterprise model rules out smaller teams Minimal latency on high-volume replication Roadmap now tied to Qlik after acquisition Well suited to legacy and mainframe sources Overpowered for straightforward warehouse syncing
7. IBM InfoSphere IBM InfoSphere Data Replication is a comprehensive data replication solution that supports CDC across various data sources and targets. It enables real-time data integration, replication, and synchronization, allowing organizations to maintain consistent and up-to-date data across multiple systems.
Two key features:
Q Replication: A high-performance, low-latency replication technology that uses IBM MQ to queue changes for efficient data movement. SQL Replication: Provides flexible, SQL-based replication capabilities for various databases and platforms. Pricing To obtain accurate pricing information, you would need to contact IBM's sales team.
Pros Cons Q Replication delivers very low latency via IBM MQ High licence cost and long implementation SQL Replication adds flexibility across platforms Requires specialist IBM skills to operate Strong fit for mainframe and legacy estates Ties you into the IBM ecosystem Proven reliability in regulated industries Pricing is not published
8. Kafka Connect Kafka Connect is an open-source component of Apache Kafka, designed to facilitate data integration between Kafka and other systems. It provides a framework for building and running reusable connectors that move large collections of data into and out of Kafka. Confluent's version enhances this with additional features and support.
Two key features of Kafka Connect are,
Distributed mode: Allows scaling of data integration tasks across multiple workers for improved performance and fault tolerance. Connector ecosystem: Offers a wide range of pre-built connectors for various data sources and sinks, simplifying integration with different systems. Pricing Kafka Connect itself is open-source and free to use. However, Confluent offers enterprise features and support through Confluent Platform, which follows a tiered pricing model.
Pros Cons Distributed mode scales tasks across workers with fault tolerance A framework, so you still need CDC source connectors Large ecosystem of prebuilt connectors Requires running and tuning a Kafka cluster Free and open source at the core Enterprise features sit behind Confluent pricing Proven at very high event throughput Significant operational overhead for small teams
9. Azure Data Factory Azure Data Factory is Microsoft's cloud-based data integration service that enables the creation, scheduling, and management of data pipelines. It supports CDC through its Copy Activity and Change Data Capture (CDC) features, allowing users to efficiently replicate changes from various data sources to target systems in Azure or other environments.
Key features of Data Factory are,
Mapping Data Flows: A visually designed data transformation tool that enables code-free ETL/ELT processes, including CDC operations. Integration Runtime: Provides a flexible compute infrastructure for executing data movement and transformation activities across different network environments. Pricing Azure Data Factory follows a consumption-based pricing model. Costs are primarily based on the number of pipeline runs, data flow execution time, and data movement operations.
Pros Cons Mapping Data Flows enable code-free CDC operations Deepens commitment to the Azure ecosystem Integration Runtime works across network environments Consumption pricing is hard to forecast 90+ connectors with no separate maintenance cost CDC support varies by source connector Visual pipeline design suits non-engineers Debugging complex pipelines is slow
10. Hevo Data Hevo Data is a no-code data pipeline platform that enables real-time replication using Change Data Capture (CDC). It is designed to simplify data integration from multiple sources into cloud data warehouses without requiring manual intervention or complex coding. Hevo supports a wide variety of databases and SaaS applications and automates the detection and application of changes.
Two key features of Hevo Data are:
Auto Schema Mapping: Hevo automatically maps source schema to the destination schema, ensuring seamless ingestion even when the schema evolves.Minimal Setup: With a fully managed architecture and intuitive UI, you can set up CDC pipelines in minutes without coding or managing infrastructure.Pricing Hevo Data offers tiered paid plans starting with a 14-day free trial. You can contact their sales team for enterprise pricing and additional features.
Pros Cons No-code setup with CDC pipelines running in minutes 150+ connectors, fewer than the leaders Auto schema mapping copes with evolving sources Event-based pricing is hard to forecast at scale Fully managed, with no infrastructure to run Cloud only, with no self-hosted option 14-day free trial for evaluation Connector gaps cannot be filled by your own team
11. Google Cloud Dataflow Google Cloud Dataflow is a fully managed stream and batch data processing service that supports real-time analytics and ETL. While not a CDC tool in the traditional sense, it can implement log-based or event-driven CDC workflows when integrated with Pub/Sub, Cloud SQL, or BigQuery. It supports the Apache Beam SDK for custom data processing pipelines.
Two key features of Cloud Dataflow are:
Unified Stream and Batch Processing: Write your CDC pipeline once and execute it as batch or streaming jobs without changing the code.Autoscaling and Serverless Architecture: Cloud Dataflow automatically scales resources to meet throughput demands, reducing operational overhead.Pricing Google Cloud Dataflow follows a consumption-based pricing model, charging on CPU, memory and data processing time. You can estimate costs with the Google Cloud Pricing Calculator.
Pros Cons One Beam pipeline runs as batch or streaming unchanged Not a CDC tool on its own, needs Pub/Sub or Datastream Serverless with automatic scaling Requires Apache Beam knowledge to use well Handles very high throughput reliably Costs climb quickly on sustained streaming jobs Integrates cleanly with BigQuery and Cloud SQL Deepens commitment to Google Cloud
What Are the Benefits of Change Data Capture? CDC offers several advantages over traditional data transfer methods, making it valuable for various data management tasks. Here are some key benefits of using CDC:
Real-time Data: Unlike batch processing, which transfers data periodically, CDC captures the modifications as they happen, enabling real-time data movement and analysis. This is crucial for applications that require up-to-date information, such as fraud detection, stock market analysis, and personalized recommendations. However, you can also use CDC to send data in batches. Reduced Resource Consumption: CDC only transfers the changed data, minimizing the amount of data transfer and processing compared to full data transfers. This translates to lower bandwidth usage, less strain on system resources, and improved overall efficiency. Faster and more Efficient Data Migration: With the CDC technique, you can experience smoother and faster data migration with minimal downtime by continuously capturing changes. Since the target system is constantly updated with the latest changes, it minimizes the disruption to ongoing operations. Simplified Application Integration: It allows for easier integration between applications that use different database systems. By capturing changes in a standardized format, CDC enables seamless understanding and utilization of data from other systems.Which CDC Tool Should You Choose? The right CDC tool depends on three things: which databases you need to read from, how much infrastructure you are willing to operate, and whether latency has to be measured in seconds or minutes. Airbyte, Debezium and Kafka Connect give you log-based capture without licence cost. AWS DMS, Azure Data Factory and Google Cloud Dataflow suit teams already committed to one cloud. GoldenGate, Qlik Replicate, IBM InfoSphere and Striim bring support contracts and broad legacy coverage at enterprise prices.
What Else Should You Know About CDC Tools? 1. What is Change Data Capture (CDC), and how is it different from traditional ETL?
2. Which CDC tools are best suited for organizations looking for open-source solutions?
3. Can CDC tools be used for real-time analytics and machine learning workflows?
4. Are there CDC tools that don’t require writing code to get started?
5. How should I choose the right CDC tool for my organization?
Suggested Reads:
Data Replication Tools
Data Ingestion Tools
Data Pipeline Tools
Data Sync Tools
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