awesome-repositories.com
Blog
awesome-repositories.com

Descubre los mejores repositorios open-source con nuestra búsqueda potenciada por IA.

ExplorarBúsquedas curadasAlternativas open-sourceSoftware autohospedableBlogMapa del sitio
ProyectoAcerca deCómo clasificamosPrensaServidor MCP
Aviso legalPrivacidadTérminos
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

6 repositorios

Awesome GitHub RepositoriesData Lake Acceleration

Tools and platforms designed to optimize query performance and data processing speeds directly on open table formats within data lakes.

Distinguishing note: None of the existing candidates were provided; this category specifically targets performance optimization for open table formats in data lakes.

Explore 6 awesome GitHub repositories matching data & databases · Data Lake Acceleration. Refine with filters or upvote what's useful.

Awesome Data Lake Acceleration GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • clickhouse/clickhouseAvatar de ClickHouse

    ClickHouse/ClickHouse

    48,229Ver en GitHub↗

    ClickHouse is a high-performance, columnar analytical database designed for real-time query execution and large-scale data aggregation. It functions as a distributed data warehouse capable of processing petabytes of information, while also providing an embedded engine that integrates directly into applications for native query capabilities without external dependencies. The system is built to handle high-throughput ingestion and complex analytical workloads, delivering millisecond-level latency for interactive dashboards and operational monitoring. The platform distinguishes itself through ad

    Accelerates performance-critical workloads by querying open table formats directly in place and writing results back to native storage.

    C++aianalyticsbig-data
    Ver en GitHub↗48,229
  • prestodb/prestoAvatar de prestodb

    prestodb/presto

    16,711Ver en GitHub↗

    Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

    Reads data from table formats by accessing metadata directly from storage or metastores.

    Javabig-datadatahadoop
    Ver en GitHub↗16,711
  • apache/dorisAvatar de apache

    apache/doris

    15,526Ver en GitHub↗

    Doris is a distributed SQL data warehouse designed for high-performance analytical workloads and real-time data processing. It functions as a unified platform that integrates traditional relational warehousing with lakehouse query capabilities, allowing users to execute analytical operations directly against external data lakes without requiring data migration. The system distinguishes itself through a shared-nothing, massively parallel processing architecture that utilizes vectorized query execution and columnar storage to maintain sub-second latency. It supports dynamic schema evolution, en

    Enables direct analysis of external data lakes without requiring data migration.

    Javaagentaibigquery
    Ver en GitHub↗15,526
  • apache/hudiAvatar de apache

    apache/hudi

    6,097Ver en GitHub↗

    Apache Hudi is an open-source table format that brings ACID transactions, incremental processing, and multi-modal indexing to data lakes. It provides atomic commits with snapshot isolation, rollback, and optimistic concurrency control for reliable data lake operations, while supporting upserts, record-level updates, and deletions in large analytical datasets. The project distinguishes itself through a timeline-based architecture that coordinates all write operations, enabling features like time-travel querying, incremental change streaming, and multi-modal query views that include snapshot, i

    Guarantees atomic commits, rollback, and snapshot isolation for reliable data lake operations.

    Javaapacheflinkapachehudiapachespark
    Ver en GitHub↗6,097
  • treeverse/lakefsAvatar de treeverse

    treeverse/lakeFS

    5,406Ver en GitHub↗

    lakeFS es un sistema de versionado de lagos de datos que proporciona ramificaciones (branching) y commits similares a Git para grandes conjuntos de datos almacenados en almacenamiento de objetos. Funciona como una capa de control de versiones, permitiendo la creación de instantáneas inmutables, commits atómicos y ramificaciones de copia cero para crear entornos aislados para la experimentación de datos sin duplicar archivos físicos. El sistema sirve como una puerta de enlace de almacenamiento compatible con S3 y un catálogo REST de Iceberg, permitiendo que los protocolos de almacenamiento en la nube estándar y los clientes compatibles gestionen tablas versionadas. Actúa como un guardián de calidad de datos mediante el uso de un sistema de hooks basado en eventos para validar conjuntos de datos contra políticas de gobernanza antes de que los cambios se fusionen en producción. La plataforma cubre amplias capacidades para la gobernanza de datos, incluyendo colaboración mediante pull requests, control de acceso basado en roles y seguimiento del linaje de datos. Proporciona integración para la orquestación de flujos de trabajo, pipelines de aprendizaje automático y varios motores de cómputo de big data, soportando conectividad de almacenamiento multi-nube y sincronización de identidad mediante SSO y SCIM. El software se puede instalar utilizando binarios, contenedores o Helm charts para su despliegue en Kubernetes.

    Maintains versioned views of underlying data and associated metadata specifically for Delta Lake tables.

    Go
    Ver en GitHub↗5,406
  • oceanbase/miniobAvatar de oceanbase

    oceanbase/miniob

    4,318Ver en GitHub↗

    MiniOB is an open-source educational relational database kernel designed for learning the internals of database systems. It implements a dual-engine storage architecture combining B+ Tree and LSM-Tree, supports SQL parsing and query execution, and provides transactional processing with multi-version concurrency control. The system communicates with clients using the MySQL wire protocol and includes a vector database extension for storing and querying high-dimensional vectors. The project distinguishes itself through its comprehensive coverage of core database concepts in a single, learnable c

    Persists rows to disk and reads them back while preserving atomicity and isolation for each operation.

    C++classroomcplusplusdatabase
    Ver en GitHub↗4,318
  1. Home
  2. Data & Databases
  3. Data Lake Acceleration

Explorar subetiquetas

  • Transactional Data Lake EnginesEngines that guarantee atomic commits, rollback, and snapshot isolation for reliable data lake operations. **Distinct from Data Lake Acceleration:** Distinct from Data Lake Acceleration: focuses on transactional guarantees rather than query performance optimization.
  • Transactional Data Lake Storage2 sub-etiquetasOpen table formats that bring ACID transactions and incremental processing to data lakes. **Distinct from Data Lake Acceleration:** Distinct from Data Lake Acceleration: focuses on transactional storage rather than query performance optimization.