Awesome GitHub RepositoriesData Ingestion Tools

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

TiDB is a horizontally scalable, distributed SQL database designed to provide consistent transactional storage and high-performance analytical processing within a single unified architecture. It utilizes a decoupled compute-storage design and a distributed key-value storage layer to ensure horizontal scalability and efficient range-based queries. By employing a consensus-based replication algorithm, the system maintains high availability and automatic failover across multiple nodes and geographical regions. The platform distinguishes itself through its hybrid transactional and analytical proc

SheetJS is a comprehensive library for parsing, manipulating, and generating complex spreadsheet file formats. It functions as a universal data processor that maps diverse binary, XML, and text-based file structures into a unified internal object model, allowing developers to create, read, and transform workbook data programmatically. The library distinguishes itself through a portable logic layer that provides a consistent execution environment across web browsers, server-side runtimes, and native desktop or mobile applications. By utilizing stream-based processing, it handles large files in

Khoj is a self-hosted artificial intelligence platform designed for personal knowledge management and semantic information retrieval. It functions as a private assistant that indexes your local documents, notes, and external workspaces, allowing you to interact with your data through natural language queries and conversational chat. By maintaining a local-first architecture, the system ensures that your information remains under your control while providing context-aware responses grounded in your personal knowledge base. The platform distinguishes itself through a modular, cross-platform int

Apache Druid is a real-time OLAP database and distributed analytics engine. It functions as a columnar time-series database designed for high-performance analytical queries and the real-time ingestion of streaming and batch datasets. The system provides a framework for high-concurrency analytics, allowing multiple simultaneous users to execute SQL and native queries across large-scale data. It supports mixed data ingestion, combining real-time streaming and batch loading into a single system for unified analysis. The platform includes capabilities for distributed cluster management, enabling

Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

dlt est un outil d'ingestion de données Python et un framework de pipeline ETL conçu pour récupérer des données depuis diverses sources et les persister dans des destinations structurées. Il fonctionne comme un moteur d'inférence de schéma qui détecte automatiquement les types de données et aplatit les structures JSON imbriquées en tables relationnelles, déplaçant les données des sources vers des lakehouses, des entrepôts ou des bases de données vectorielles. Le projet se distingue par une génération de pipeline alimentée par l'IA, utilisant de grands modèles de langage pour échafauder le code d'extraction et les connecteurs pour les API REST. Il prend également en charge le stockage vectoriel multimodal et la population spécialisée de bases de données vectorielles pour prendre en charge les applications d'IA et de machine learning. Le framework couvre un large éventail de capacités, incluant l'évolution automatique du schéma, le chargement incrémentiel de données via le suivi d'état et la validation de la qualité des données par l'application de contrats de données. Il fournit des outils pour la normalisation des données relationnelles, les transformations pré- et post-chargement, et une variété d'adaptateurs de destination pour les bases de données SQL et les magasins d'objets cloud. L'observabilité est gérée via des tableaux de bord d'exécution de pipeline, le suivi de lignage des colonnes et la vérification de version de schéma utilisant des hachages basés sur le contenu.