awesome-repositories.com
Blog
MCP
awesome-repositories.com

Descoperă cele mai bune repository-uri open source cu căutare AI.

ExploreazăCăutări recomandateAlternative open-sourceSoftware self-hostedBlogHartă site
ProiectDespreCum realizăm clasamentulPresăServer MCP
LegalConfidențialitateTermeni
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
debezium avatar

debezium/debezium

0
View on GitHub↗
12,421 stele·2,847 fork-uri·Java·apache-2.0·10 vizualizăridebezium.io↗

Debezium

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.

Features

  • Change Data Capture - Provides a distributed platform for streaming row-level database modifications as real-time events.
  • Event Streaming Infrastructure - Parses database transaction logs to broadcast structural and data changes to external applications.
  • Message Queue Connectors - Integrates as a plugin for streaming database transaction logs into distributed message queues.
  • Change Data Capture Streams - Streams row-level modifications from databases to message brokers for real-time data integration.
  • Asynchronous Log Streaming - Broadcasts database modifications as asynchronous event streams to decouple capture from downstream consumers.
  • Change Data Capture - Distributed platform turning database changes into real-time event streams.
  • Real-Time Data Integration Platforms - Synchronizes operational database changes across software architectures in real-time.
  • Data Warehouse Integrations - Maintains analytical stores by streaming live database updates into data warehouses for real-time intelligence.
  • Database Adapters - Uses modular adapters to translate proprietary database log formats into a unified event structure.
  • Database Snapshots - Performs snapshot-based initial state capture to ensure a consistent baseline for data synchronization.
  • Transaction Orderers - Maintains the original commit order of database operations to preserve data integrity during streaming.
  • Schema Registries - Encodes data changes using a central schema registry to maintain consistent definitions across distributed systems.

Istoric stele

Graficul istoricului de stele pentru debezium/debeziumGraficul istoricului de stele pentru debezium/debezium

Căutare AI

Explorează mai multe repository-uri excelente

Descrie ce ai nevoie în limbaj simplu — AI-ul sortează mii de proiecte open source selectate în funcție de relevanță.

Start searching with AI

Întrebări frecvente

Ce face debezium/debezium?

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.

Care sunt principalele funcționalități ale debezium/debezium?

Principalele funcționalități ale debezium/debezium sunt: Change Data Capture, Event Streaming Infrastructure, Message Queue Connectors, Change Data Capture Streams, Asynchronous Log Streaming, Real-Time Data Integration Platforms, Data Warehouse Integrations, Database Adapters.

Care sunt câteva alternative open-source pentru debezium/debezium?

Alternativele open-source pentru debezium/debezium includ: redpanda-data/connect — Connect is a Kafka data integration platform and stream processing engine used to build declarative pipelines that… datalinkdc/dinky — Dinky is a real-time data platform for developing, deploying, and operating streaming applications based on Apache… zendesk/maxwell — Maxwell is a MySQL change data capture tool and binlog streaming application that converts database modifications into… linkedin/databus — Databus is a distributed change data capture system and database transaction log streamer. It extracts data… rethinkdb/rethinkdb — RethinkDB is a distributed, document-oriented database designed to store and manage JSON-formatted data across… alibaba/canal — Canal is a database replication middleware that performs change data capture by simulating a database replica. It…

Alternative open-source pentru Debezium

Proiecte open-source similare, clasificate după numărul de funcționalități comune cu Debezium.
  • redpanda-data/connectAvatar redpanda-data

    redpanda-data/connect

    8,681Vezi pe GitHub↗

    Connect is a Kafka data integration platform and stream processing engine used to build declarative pipelines that move and transform messages between Kafka topics and external sources. It functions as a Kafka Connect framework and a change data capture tool, streaming real-time database modifications to synchronize data across distributed environments. The project differentiates itself through a dedicated mapping language for mutating and reshaping message payloads and the ability to execute custom processing logic within a sandboxed WebAssembly runtime. It also provides an observability pip

    Goamqpcqrsdata-engineering
    Vezi pe GitHub↗8,681
  • datalinkdc/dinkyAvatar DataLinkDC

    DataLinkDC/dinky

    3,740Vezi pe GitHub↗

    Dinky is a real-time data platform for developing, deploying, and operating streaming applications based on Apache Flink. It functions as a SQL streaming IDE and a real-time data pipeline orchestrator, providing a web-based environment for writing and verifying queries with integrated logic plan visualization and lineage tracking. The platform acts as a distributed cluster manager, allowing the registration, monitoring, and administration of multiple processing clusters from a centralized interface. It also serves as a change data capture integration tool, synchronizing real-time database cha

    Javadatalakedatawarehouseflink
    Vezi pe GitHub↗3,740
  • zendesk/maxwellAvatar zendesk

    zendesk/maxwell

    4,254Vezi pe GitHub↗

    Maxwell is a MySQL change data capture tool and binlog streaming application that converts database modifications into structured JSON events. It functions as a data pipeline that reads MySQL binary logs to synchronize changes across external indices, search engines, and distributed messaging systems such as Kafka. The project provides capabilities to maintain persistent audit trails by recording a chronological history of all database modifications. It enables real-time data synchronization and event-driven architecture integration by streaming database changes to external platforms to trigg

    Java
    Vezi pe GitHub↗4,254
  • linkedin/databusAvatar linkedin

    linkedin/databus

    3,678Vezi pe GitHub↗

    Databus is a distributed change data capture system and database transaction log streamer. It extracts data modifications from source logs and streams them to downstream consumers to keep secondary data stores and caches consistent. The system functions as a source-agnostic data pipeline and distributed event relay. It uses a network of processes to decouple database sources from consumers, routing change streams across a network to multiple targets regardless of the original source system. The project covers real-time data synchronization and distributed pipeline monitoring. This includes t

    Java
    Vezi pe GitHub↗3,678
Vezi toate cele 30 alternative pentru Debezium→