Awesome GitHub RepositoriesDistributed Data Processing Engines

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

This project is a collection of foundational machine learning algorithms and data science tools implemented in Python. It focuses on building the logic of these tools using basic programming primitives rather than relying on specialized libraries. The implementation covers several core domains, including a linear algebra library for matrix and vector operations, a statistical analysis toolkit for probability and hypothesis testing, and a framework for map-reduce distributed processing. It also includes implementations for natural language processing, graph theory for network analysis, and var

Pentaho Kettle 是一个企业级 ETL 数据集成平台，旨在在不同源和目标数据库之间提取、转换和加载数据。它充当元数据驱动的编排器，利用可视化工作流设计器来创建和管理复杂的数据任务序列和转换管道。 该系统的特点是其分布式数据处理引擎，可在服务器节点集群上执行工作负载以提高吞吐量。它采用基于插件的架构，允许通过外部 JAR 文件扩展平台，以提供与各种数据库和云服务的连接。 该平台涵盖了广泛的数据集成功能，包括批量加载、远程文件管理和数据结构转换。它提供用于数据质量验证、管道自动化和作业生命周期管理的工具，以及用于跟踪服务器健康状况和实时执行状态的监控实用程序。

Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to process real-time data streams. It functions as a data pipeline orchestrator that allows users to define and deploy declarative data flow graphs connecting streaming sources to processing components. The system operates as a multi-tenant distributed compute engine that isolates workloads and limits resource usage across shared clusters using dedicated pools and access control. It is also a secure distributed processing engine that employs encrypted node communication and SSL-secur

Data-Juicer is an open-source framework for cleaning, filtering, deduplicating, and transforming multimodal datasets to prepare them for training large language and vision models. It functions as a distributed data pipeline engine that runs processing jobs across Ray clusters, handling billions of samples with automatic operator fusion and adaptive parallelism. The framework provides a library of operators that leverage large language models for semantic extraction, filtering, and data synthesis within processing pipelines. The project distinguishes itself through a YAML-based data recipe sys

Cube Studio 是一个云原生 MLOps 平台和基于 Kubernetes 的 AI 编排器，专为机器学习全生命周期设计。它提供了一个用于大规模模型微调的分布式训练框架、用于硬件虚拟化的 GPU 资源管理器，以及一个使用可视化有向无环图（DAG）来管理端到端工作流的 ML 流水线编排器。 该平台的特色在于其专业的 LLM 推理服务器，支持检索增强生成（RAG）和私有知识库构建。它拥有专门用于大语言模型监督微调和强化学习的系统，并辅以可视化超参数搜索工具。 该系统涵盖了广泛的运营能力，包括多模态数据标注、分布式数据流水线和多集群工作负载调度。它还提供基于浏览器的交互式开发环境、容器镜像管理以及用于版本控制和部署可扩展推理 API（带流量拆分）的模型注册中心。 其基础设施包括集成的集群健康监控和支持单点登录（SSO）的基于角色的访问控制（RBAC）。

This project is a dataset management framework and cross-framework data loader that provides a unified interface for reading data formats compatible with TensorFlow, JAX, and PyTorch. It serves as a library of curated public datasets provided as data streams and includes tools for building, versioning, and documenting large-scale datasets. The system differentiates itself through a distributed data processing engine capable of managing massive datasets across clusters using parallelized pipelines. It utilizes builder-based construction to standardize how data is downloaded and prepared, while

This project is an interactive, web-based notebook environment designed for distributed data science and large-scale computing. It serves as a development tool for executing code and performing data analysis specifically within the Apache Spark framework, providing a browser-based interface that combines code execution with reactive data visualization. The platform distinguishes itself through its deep integration with distributed infrastructure, allowing users to manage cluster resources, configure runtime dependencies, and isolate execution processes for individual notebooks. It supports co

Data engineering practice repository providing tutorials, distributed processing engines, and Python data pipeline automation scripts. The system encompasses automated data validation, distributed compute aggregation, embedded columnar querying, lazy evaluation planning, partitioned storage export, and cloud storage retrieval. The capability surface covers cloud integration and storage, data engineering and pipelines, data processing and analytics, data quality and testing, database and storage, file management, and monitoring and observability.