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2 repositorios

Awesome GitHub RepositoriesMulti-Engine Execution Backends

Capability to execute SQL queries on different distributed processing engines like Spark, Tez, or MapReduce.

Distinct from Query Execution Engines: Distinct from Query Execution Engines: focuses on the ability to switch between multiple execution backends, not a single engine.

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

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • ibis-project/ibisAvatar de ibis-project

    ibis-project/ibis

    6,574Ver en GitHub↗

    Ibis is a portable Python dataframe library and multi-backend query engine that provides a unified interface for executing data transformations across diverse compute engines. It functions as a Python SQL expression compiler and dialect transpiler, allowing users to define data logic once and execute it across cloud warehouses, embedded databases, and distributed clusters without rewriting code. The project distinguishes itself through a database backend abstraction that decouples transformation logic from the underlying execution engine. It enables polyglot data workflows by mixing raw SQL s

    Executes data transformations across different SQL databases and cloud warehouses using a single unified Python interface.

    Pythonbigqueryclickhousedatabase
    Ver en GitHub↗6,574
  • apache/hiveAvatar de apache

    apache/hive

    6,012Ver en GitHub↗

    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

    Executes queries on Spark, Tez, or MapReduce to balance performance and resource usage.

    Javaapachebig-datadatabase
    Ver en GitHub↗6,012
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