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42 个仓库

Awesome GitHub RepositoriesChange Data Capture

Processes for identifying and streaming database changes to external systems in real-time.

Distinguishing note: None of the candidates matched; this specifically addresses the automated replication of database changes to external services.

Explore 42 awesome GitHub repositories matching data & databases · Change Data Capture. Refine with filters or upvote what's useful.

Awesome Change Data Capture GitHub Repositories

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  • pingcap/tidbpingcap 的头像

    pingcap/tidb

    40,166在 GitHub 上查看↗

    TiDB is a horizontally scalable, distributed SQL database designed to provide consistent transactional storage and high-performance analytical processing within a single unified architecture. It utilizes a decoupled compute-storage design and a distributed key-value storage layer to ensure horizontal scalability and efficient range-based queries. By employing a consensus-based replication algorithm, the system maintains high availability and automatic failover across multiple nodes and geographical regions. The platform distinguishes itself through its hybrid transactional and analytical proc

    TiDB transmits data from a database cluster to external services like message queues or cloud storage by configuring and managing automated replication tasks.

    Gocloud-nativedatabasedistributed-database
    在 GitHub 上查看↗40,166
  • rethinkdb/rethinkdbrethinkdb 的头像

    rethinkdb/rethinkdb

    26,996在 GitHub 上查看↗

    RethinkDB is a distributed, document-oriented database designed to store and manage JSON-formatted data across scalable clusters. It utilizes a custom log-structured storage engine with B-Tree indexing to ensure high-performance disk I/O and data persistence. The system maintains high availability through automatic sharding and replication, employing a primary-replica voting consensus mechanism to handle node failures and ensure consistent cluster operations. A defining characteristic of the platform is its reactive changefeed engine, which allows applications to subscribe to live data update

    "Maintains persistent cursors on tables to push real-time document modifications to subscribers as a continuous stream of change events."

    C++
    在 GitHub 上查看↗26,996
  • dolthub/doltdolthub 的头像

    dolthub/dolt

    23,592在 GitHub 上查看↗

    Dolt is a relational database engine that integrates version control directly into the database management layer. It functions as a version-controlled SQL database that tracks every row and schema change using a commit-based history, allowing users to branch, merge, and audit data modifications. By implementing a wire-protocol-compatible server, the system enables standard SQL clients and tools to interact with versioned data as if they were connecting to a traditional relational database. The platform distinguishes itself by applying repository-style workflows to data management, including s

    Pushes and pulls entire database states between instances to facilitate distributed collaboration and off-site backups.

    Gocommand-linedata-version-controldata-versioning
    在 GitHub 上查看↗23,592
  • vonng/ddiaVonng 的头像

    Vonng/ddia

    22,648在 GitHub 上查看↗

    This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi

    Extracts database write events into streams to synchronize downstream systems.

    Pythonbookdatabaseddia
    在 GitHub 上查看↗22,648
  • redis/go-redisredis 的头像

    redis/go-redis

    22,159在 GitHub 上查看↗

    This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

    Captures and synchronizes data modifications from external databases to a cache in near real-time.

    Gogogolangredis
    在 GitHub 上查看↗22,159
  • airbytehq/airbyteairbytehq 的头像

    airbytehq/airbyte

    21,472在 GitHub 上查看↗

    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 de

    Tracks source database modifications in real time using log-based change capture.

    Pythonbigquerychange-data-capturedata
    在 GitHub 上查看↗21,472
  • apache/shardingsphereapache 的头像

    apache/shardingsphere

    20,737在 GitHub 上查看↗

    ShardingSphere is a distributed SQL database middleware that provides sharding, read-write splitting, and distributed transaction management for relational databases. It functions as a layer that intercepts SQL queries to distribute data across multiple physical database instances for horizontal scaling. The project is distinguished by its ability to operate as either a standalone transparent database proxy or via direct integration as a JDBC driver. It features a SQL dialect translator that parses queries into abstract syntax trees to convert syntax between different database engines, enabli

    Synchronizes data between different database systems to maintain consistency across disparate environments.

    Java
    在 GitHub 上查看↗20,737
  • vitessio/vitessvitessio 的头像

    vitessio/vitess

    20,788在 GitHub 上查看↗

    Vitess is a database clustering system for horizontal scaling of MySQL. It functions as a middleware layer that abstracts complex sharding and physical topology, allowing applications to interact with a distributed database environment through a unified interface. By intercepting and routing SQL queries across multiple shards, it enables large-scale data management while maintaining the appearance of a single database instance. The platform distinguishes itself through its ability to perform online schema migrations and distributed transaction coordination without requiring application downti

    Captures and propagates data modifications from the primary database to replicas or external systems in real-time to support event-driven architectures.

    Gocncfdatabase-clusterkubernetes
    在 GitHub 上查看↗20,788
  • rqlite/rqliterqlite 的头像

    rqlite/rqlite

    17,586在 GitHub 上查看↗

    rqlite is a distributed relational database that replicates SQLite data across a cluster using the Raft consensus algorithm. It functions as a fault-tolerant storage system that provides high availability and a web API for executing SQL queries and managing relational data without requiring native database drivers. The system distinguishes itself by using an HTTP SQL interface to expose database operations and cluster management. It features a real-time change data capture stream that pushes database mutations to external HTTP endpoints via webhooks and supports the scaling of read throughput

    Streams database modifications in real time to external systems via webhooks for immediate data synchronization.

    Goconsensusdatabasedistributed-database
    在 GitHub 上查看↗17,586
  • janeczku/calibre-webjaneczku 的头像

    janeczku/calibre-web

    17,500在 GitHub 上查看↗

    Calibre-web is a self-hosted web application that provides a browser-based interface for browsing, managing, and reading digital book collections stored in a library database. It functions as a comprehensive library management system, allowing users to organize large collections, edit metadata, and perform automated content updates through a centralized administrative dashboard. The platform distinguishes itself by integrating directly with external infrastructure to extend the capabilities of a standard digital library. It supports remote storage mapping to host files on cloud providers, uti

    Maintains real-time consistency between the library database and remote storage updates.

    Fluentcalibreebookebook-manager
    在 GitHub 上查看↗17,500
  • dbt-labs/dbt-coredbt-labs 的头像

    dbt-labs/dbt-core

    13,051在 GitHub 上查看↗

    dbt-core is a command-line framework for transforming data within a warehouse using modular SQL and version control. It functions as a data transformation engine that enables users to define data structures and business logic through declarative configuration files, which the system then compiles into executable code. By managing complex data dependencies through a directed acyclic graph, it ensures that transformation tasks execute in the correct order while maintaining a manifest-driven state to track lineage and execution history. The project distinguishes itself through an adapter-based d

    Provides automated impact analysis by comparing development code against production state to preview transformation results before deployment.

    Rustanalyticsbusiness-intelligencedata-modeling
    在 GitHub 上查看↗13,051
  • debezium/debeziumdebezium 的头像

    debezium/debezium

    12,421在 GitHub 上查看↗

    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 bin

    Provides a distributed platform for streaming row-level database modifications as real-time events.

    Javaapache-kafkacdcchange-data-capture
    在 GitHub 上查看↗12,421
  • yugabyte/yugabyte-dbyugabyte 的头像

    yugabyte/yugabyte-db

    10,349在 GitHub 上查看↗

    YugabyteDB is a distributed SQL database and relational data store designed for horizontal scalability and high availability across multiple nodes or regions. It functions as a cloud-native system that ensures continuous availability and supports PostgreSQL compatible query languages and drivers. The system includes specialized capabilities as a vector database for AI, utilizing high-dimensional indexing to perform similarity searches. It is engineered as a multi-region cloud database that synchronizes data across different geographic locations to maintain global availability. The project co

    Propagates schema definition changes across clusters to maintain consistent table structures automatically.

    Ccloud-nativecppdatabase
    在 GitHub 上查看↗10,349
  • mongodb/node-mongodb-nativemongodb 的头像

    mongodb/node-mongodb-native

    10,180在 GitHub 上查看↗

    The MongoDB Node.js Driver is a programmatic interface and NoSQL database client used to manage document storage and execute operations within a MongoDB database. It serves as an asynchronous database interface and connection manager that enables Node.js applications to integrate with MongoDB servers. The project implements client-side field encryption to secure sensitive data and queries locally before transmission. It also provides a BSON serialization library to convert JavaScript objects into a binary format for efficient storage and network transmission. The driver covers a broad range

    Implements Change Streams to identify and monitor real-time data modifications within collections.

    TypeScriptdatabasemongodbnode-js
    在 GitHub 上查看↗10,180
  • tporadowski/redistporadowski 的头像

    tporadowski/redis

    9,987在 GitHub 上查看↗

    Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

    Maintains real-time consistency between primary databases and the in-memory store through change streaming.

    Credisredis-for-windowsredis-msi-installer
    在 GitHub 上查看↗9,987
  • electric-sql/electricelectric-sql 的头像

    electric-sql/electric

    9,909在 GitHub 上查看↗

    Electric is a Postgres data synchronization engine and replication proxy designed to enable local-first software. It replicates data from Postgres databases to client-side stores in real time using logical replication, allowing applications to maintain a local embedded database for offline access and low-latency updates. The system distinguishes itself by using shapes to filter and authorize specific subsets of database rows and columns before streaming them to clients or edge workers. It further supports multi-user collaboration by integrating a conflict-free replicated data type framework t

    Streams database changes as a message log to keep a Redis cache up-to-date.

    Elixircrdtcrdtselixir
    在 GitHub 上查看↗9,909
  • apache/seatunnelapache 的头像

    apache/seatunnel

    9,427在 GitHub 上查看↗

    SeaTunnel is a distributed data integration engine designed to synchronize structured and unstructured data across diverse sources and sinks. It functions as a multi-engine execution framework that can run data integration tasks across different distributed computing backends to optimize workload performance. The project is distinguished by a visual data pipeline designer for configuring workflows without manual code and a specialized change data capture tool for streaming incremental database updates. It also includes an enrichment pipeline that integrates large language models and embedding

    Optimizes data movement across multiple tables and databases using JDBC multiplexing and log parsing.

    Javaapachebatchcdc
    在 GitHub 上查看↗9,427
  • risingwavelabs/risingwaverisingwavelabs 的头像

    risingwavelabs/risingwave

    9,093在 GitHub 上查看↗

    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 independen

    Identifies and streams row-level database changes in real-time to synchronize external state.

    Rustapache-icebergdata-engineeringdatabase
    在 GitHub 上查看↗9,093
  • benthosdev/benthosbenthosdev 的头像

    benthosdev/benthos

    8,681在 GitHub 上查看↗

    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 w

    Streams real-time database modifications as discrete events for downstream processing.

    Go
    在 GitHub 上查看↗8,681
  • redpanda-data/connectredpanda-data 的头像

    redpanda-data/connect

    8,681在 GitHub 上查看↗

    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

    Provides real-time streaming of database modifications by polling transaction logs or using change-tracking mechanisms.

    Goamqpcqrsdata-engineering
    在 GitHub 上查看↗8,681
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  3. Change Data Capture

探索子标签

  • Change Filters1 个子标签Rules for including or excluding specific tables, columns, or data types from a change stream. **Distinct from Change Data Capture:** Distinct from Change Data Capture: focuses on the configuration of inclusion/exclusion rules for the stream rather than the capture process itself.
  • Database Synchronization2 个子标签Real-time consistency maintenance between primary databases and caches. **Distinct from Change Data Capture:** Distinct from Change Data Capture: focuses on the synchronization outcome rather than the capture mechanism.
  • Full Instance SynchronizationSynchronizing all tables from a complete database instance to downstream systems in a single job. **Distinct from Database Synchronization:** Focuses on the scope (entire instance) rather than just the state of consistency between a DB and cache.
  • Schema Replication1 个子标签Automatically propagating database schema changes across distributed clusters. **Distinct from Change Data Capture:** Distinct from Change Data Capture: focuses on replicating structural DDL changes rather than streaming DML data changes.
  • Transformation Impact Previews1 个子标签Tools for analyzing and visualizing the differences between development code and production state before applying changes. **Distinct from Change Data Capture:** Distinct from Change Data Capture: focuses on pre-deployment impact analysis of transformation logic rather than real-time data replication.