awesome-repositories.com
Blog
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetÀ proposNotre méthodologiePresseServeur MCP
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 dépôts

Awesome GitHub RepositoriesMulti-Sink Routing

Mapping source tables to multiple designated sink tables to organize data distribution.

Distinct from Data Sinking: Focuses on the routing and mapping logic for multiple sinks rather than the basic act of writing to external storage.

Explore 2 awesome GitHub repositories matching data & databases · Multi-Sink Routing. Refine with filters or upvote what's useful.

Awesome Multi-Sink Routing GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • hazelcast/hazelcastAvatar de hazelcast

    hazelcast/hazelcast

    6,570Voir sur GitHub↗

    Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

    Distributes processing stage outputs to multiple destination sinks simultaneously to facilitate parallel data routing.

    Javabig-datacachingdata-in-motion
    Voir sur GitHub↗6,570
  • apache/flink-cdcAvatar de apache

    apache/flink-cdc

    6,430Voir sur GitHub↗

    This project is a streaming data integration framework that captures real-time database changes and synchronizes them with downstream systems. It operates as a distributed streaming ETL and database synchronizer, reading database logs and snapshots to propagate row-level modifications to target sinks. The system supports declarative data integration, allowing users to define source-to-sink data flows using SQL or YAML configurations. It distinguishes itself by automating schema evolution to maintain synchronization when source structures change and ensuring exactly-once delivery and processin

    Maps specific source tables to designated sink tables to organize data distribution across multiple target systems.

    Javabatchcdcchange-data-capture
    Voir sur GitHub↗6,430
  1. Home
  2. Data & Databases
  3. Data Sinking
  4. Multi-Sink Routing