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2 Repos

Awesome GitHub RepositoriesMulti-Engine Execution Backends

Capabilities to switch between different distributed processing engines to optimize performance for specific workloads.

Distinct from Execution Engines: Focuses on the ability to target different distributed engines (like Flink, Spark) for the same pipeline.

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

Awesome Multi-Engine Execution Backends GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • apache/seatunnelAvatar von apache

    apache/seatunnel

    9,427Auf GitHub ansehen↗

    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

    Supports running data integration tasks across various processing backends to optimize performance.

    Javaapachebatchcdc
    Auf GitHub ansehen↗9,427
  • apache/hiveAvatar von apache

    apache/hive

    6,012Auf GitHub ansehen↗

    Apache Hive is a SQL-on-Hadoop data warehouse that enables querying and managing petabytes of data stored in distributed storage such as HDFS and cloud storage services. It provides a familiar SQL interface for batch analytics and reporting, supported by a core set of components including the HiveServer2 Thrift service for remote query execution, the Hive Metastore Service for central metadata management, the Hive ACID Transaction Engine for concurrent read-write operations, and the Hive LLAP Interactive Engine for low-latency analytical processing. The WebHCat REST API offers an HTTP interfac

    Supports running Hive queries on Apache Spark for accelerated performance.

    Javaapachebig-datadatabase
    Auf GitHub ansehen↗6,012
  1. Home
  2. Data & Databases
  3. Data Processing Configurations
  4. Execution Engines
  5. Multi-Engine Execution Backends

Unter-Tags erkunden

  • Spark Execution BackendsRunning Hive queries on Apache Spark for faster performance compared to MapReduce or Tez. **Distinct from Multi-Engine Execution Backends:** Distinct from Multi-Engine Execution Backends: specifically focuses on Spark as the execution engine, not the general multi-engine switching capability.