Change Data Capture Tools

Canal is a database replication middleware that performs change data capture by simulating a database replica. It monitors transaction logs to stream incremental data modifications to downstream systems in real time, acting as an event streaming infrastructure that transforms low-level binary logs into structured, consumable message streams. The project distinguishes itself through a high-throughput architecture that utilizes concurrent multi-threaded parsing and stateful log position tracking to ensure reliable data delivery. It employs a pluggable sink architecture that decouples data extraction from destination storage, allowing for flexible routing to various message queues or secondary databases. Users can manage data consistency and throughput through configurable message ordering and batching strategies, while dynamic configuration injection enables runtime adjustments to routing rules without requiring service restarts. The platform includes comprehensive operational tools for monitoring system health and performance, including metrics for transaction latency and network bandwidth. It supports secure network connectivity for data transmission and provides specialized integration for cloud-based environments, including the ability to retrieve archived logs from object storage. The service is designed for containerized deployment, incorporating automated resource management to maintain synchronization pipelines.

Debezium is a distributed change data capture platform that streams row-level database modifications as real-time events. By parsing database transaction logs, the system broadcasts structural and data changes to message brokers, enabling reactive processing and data integration across distributed architectures. The platform utilizes log-based capture to extract modifications directly from transaction logs, ensuring minimal impact on source system performance while maintaining the original commit order of operations. It employs database-specific connector adapters to translate proprietary binary formats into a unified event structure, supported by schema-registry-backed serialization to maintain consistent data definitions. To ensure a complete baseline for synchronization, the system performs snapshot-based initial states before transitioning to continuous event streaming. The tool supports a broad range of data integration tasks, including the maintenance of analytical stores and the synchronization of data across operational systems. Users can refine the data stream by applying filters to include or exclude specific tables, columns, or data types, and the system maintains an accurate representation of data models by parsing structural statements during the capture process. The project is implemented as a plugin for distributed message queues, facilitating integration into existing event-driven pipelines.

Airbyte is a data integration platform designed to synchronize information between diverse applications, databases, and data warehouses. It functions as an extract, transform, and load orchestrator that manages automated data movement workflows across cloud, on-premise, and hybrid environments. The platform provides a standardized interface for connectors, enabling the movement of structured and unstructured data while maintaining stateful checkpoints for reliable incremental syncing. The platform distinguishes itself through a containerized architecture that isolates connectors to prevent dependency conflicts and a log-based change capture system that monitors source databases for real-time modifications. It includes a dedicated connectivity layer that exposes enterprise data and system actions to artificial intelligence agents, allowing for context-aware operations and automated decision-making. Users can manage schema evolution automatically and extend the platform's capabilities by developing custom integration modules using provided software development kits. Beyond core synchronization, the system supports enterprise-grade data governance, including role-based access control, audit logging, and centralized authentication management. It offers comprehensive observability tools to track sync performance and latency, alongside infrastructure-as-code support for automating pipeline deployments. The platform is built to scale compute resources dynamically, accommodating both high-frequency incremental updates and large-scale historical data backfills.

gh-ost is a triggerless online schema migration tool for MySQL. It functions as a replication client and table management utility that synchronizes data from a source table to a shadow table using binary logs, allowing for table structure modifications without locking original tables or causing downtime. The tool distinguishes itself by using binary-log-based replication instead of triggers to stream row-based events to a shadow table. It implements load-aware throttling and dynamic performance tuning to adjust migration speed based on server load and replication lag. Users can monitor and adjust the migration process in real time through a socket-based interactive control interface. The project covers a broad range of database operation capabilities, including atomic table swapping for zero-downtime cut-overs and parallel execution of multiple schema migrations. It provides verification tools for migration integrity, such as replica simulation, checksum validation, and dry-run migrations. The system also supports various database topologies and includes an event-driven hook system for executing external scripts during the migration lifecycle.

RisingWave is a cloud-native streaming database and real-time analytics engine that uses standard SQL to process continuous data streams. It functions as a streaming data lakehouse, combining the capabilities of a streaming SQL database with a platform that integrates streaming ingestion with open table formats. The system is distinguished by its use of the PostgreSQL wire protocol, allowing it to integrate with existing SQL tools and drivers. It employs a decoupled compute and storage architecture, persisting streaming state and materialized views in cloud object storage to enable independent scaling and rapid recovery. The platform covers a broad range of real-time data operations, including change data capture, streaming ETL pipelines, and the maintenance of incremental materialized views. It supports complex stream processing such as windowed aggregations, event-time tracking with watermarks, and the continuous export of processed data to downstream sinks. The project can be deployed via Kubernetes and Helm, Docker Compose, or as a managed instance.

Benthos is a declarative stream processor and data integration pipeline used to route, transform, and filter information between disparate services. It functions as an at-least-once message broker and change data capture engine, using a transaction model to guarantee message delivery despite system crashes or server faults. The system is defined by an observability-first approach, featuring built-in HTTP health probes, performance metrics export, and distributed request flow tracing. It utilizes a plugin architecture that allows the core engine to be extended with custom binaries for new input and output connectors. The project manages data flow through a single configuration file to define pipelines. These pipelines support real-time database change events, buffered stream processing, and data transformation using a specialized mapping language.

Benthos is a stream processing engine and data integration pipeline used for routing, transforming, and connecting data streams between diverse sources and sinks. It functions as event routing middleware and a change data capture tool, streaming real-time database modifications as discrete events for downstream processing. The system utilizes a declarative pipeline configuration, where data flow and processing logic are defined in a single static file. It features a specialized domain-specific language for mapping, filtering, and enriching data payloads, allowing for complex transformations without custom code. The platform provides an observability-driven data plane with integrated telemetry, performance metrics, and message flow tracing. Reliability is managed through a transaction model that ensures at-least-once delivery guarantees to prevent data loss during system crashes. The engine is extensible through a plugin architecture that supports loading external binary modules to add new source and sink connectors.