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google/tensorstore

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View on GitHub↗
1,522 stars·142 forks·C++·2 vuesgoogle.github.io/tensorstore↗

Tensorstore

Library for reading and writing large multi-dimensional arrays.

Features

  • Big Data and Distributed Computing - Reading and writing large multi-dimensional arrays.
  • Stream Processing - Reads and writes large multi-dimensional arrays.

Historique des stars

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Questions fréquentes

Que fait google/tensorstore ?

Library for reading and writing large multi-dimensional arrays.

Quelles sont les fonctionnalités principales de google/tensorstore ?

Les fonctionnalités principales de google/tensorstore sont : Big Data and Distributed Computing, Stream Processing.

Quelles sont les alternatives open-source à google/tensorstore ?

Les alternatives open-source à google/tensorstore incluent : aklivity/zilla — 🦎 A multi-protocol edge & service proxy. Seamlessly interface web apps, IoT clients, & microservices to Apache Kafka®… apache/beam — Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded… apache/flink — Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite… apache/kafka — Kafka is a distributed event streaming platform designed for capturing, storing, and processing real-time data streams… apache/samza — Mirror of Apache Samza. airtai/faststream — FastStream is an asynchronous Python framework designed for building event-driven microservices. It provides a unified…

Alternatives open source à Tensorstore

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Tensorstore.
  • aklivity/zillaAvatar de aklivity

    aklivity/zilla

    690Voir sur GitHub↗

    🦎 A multi-protocol edge & service proxy. Seamlessly interface web apps, IoT clients, & microservices to Apache Kafka® via declaratively defined, stateless APIs.

    Java
    Voir sur GitHub↗690
  • apache/beamAvatar de apache

    apache/beam

    8,612Voir sur GitHub↗

    Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded batch data and unbounded real-time streams. It provides a system for building scalable, data-parallel workflows that operate across compute clusters using a single programming model. The framework utilizes a cross-runner pipeline abstraction that decouples the data processing logic from the underlying execution backend, allowing the same pipeline to run on different distributed compute engines. It supports multi-language pipeline development by translating high-level code fro

    Java
    Voir sur GitHub↗8,612
  • apache/flinkAvatar de apache

    apache/flink

    26,086Voir sur GitHub↗

    Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve

    Java
    Voir sur GitHub↗26,086
  • airtai/faststreamAvatar de airtai

    airtai/faststream

    5,234Voir sur GitHub↗

    FastStream is an asynchronous Python framework designed for building event-driven microservices. It provides a unified abstraction layer for interacting with various message brokers, enabling developers to manage event production and consumption through a consistent interface while maintaining access to native provider-specific features. The framework centers on a decorator-based routing model that binds application logic directly to broker topics, supported by a built-in dependency injection container that resolves resources at runtime. The framework distinguishes itself through its deep int

    Python
    Voir sur GitHub↗5,234
Voir les 30 alternatives à Tensorstore→