Awesome GitHub RepositoriesTable Managers

RisingWave is a cloud-native streaming database and real-time analytics engine that uses standard SQL to process continuous data streams. It functions as a streaming data lakehouse, combining the capabilities of a streaming SQL database with a platform that integrates streaming ingestion with open table formats. The system is distinguished by its use of the PostgreSQL wire protocol, allowing it to integrate with existing SQL tools and drivers. It employs a decoupled compute and storage architecture, persisting streaming state and materialized views in cloud object storage to enable independen

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

Iceberg is an open table format and big data table manager designed for huge analytic datasets in cloud storage. It provides a specification for tracking large-scale datasets to maintain transactional consistency and structural integrity. The project utilizes a standardized REST catalog interface to manage table metadata, ensuring interoperability between different compute engines. This allows diverse query engines to connect to a single table interface and maintain consistency across different processing frameworks. Its core capabilities include managing large-scale analytic tables, coordin

Moto is a cloud service mockery framework and API mock server that simulates AWS infrastructure locally. It allows developers to test cloud-dependent code and verify infrastructure-as-code templates without deploying real resources or incurring costs. The project functions as an SDK interceptor that can patch existing service clients to redirect requests to a local mock environment. It can also be run as a standalone HTTP server, enabling any programming language to interact with the simulated endpoints. The framework covers a vast array of simulated capabilities, including data storage, com

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

lakeFS is a data lake versioning system that provides Git-like branching and commits for large datasets stored in object storage. It functions as a version control layer, enabling the creation of immutable snapshots, atomic commits, and zero-copy branching to create isolated environments for data experimentation without duplicating physical files. The system serves as an S3-compatible storage gateway and an Iceberg REST catalog, allowing standard cloud storage protocols and compatible clients to manage versioned tables. It acts as a data quality gatekeeper by using an event-driven hook system

GeoPandas este o bibliotecă Python care extinde pandas cu suport nativ pentru date geospațiale. Aceasta tratează geometriile geografice — puncte, linii și poligoane — ca un tip de coloană de primă clasă în cadrul DataFrames, permițând utilizatorilor să stocheze, să manipuleze și să analizeze date spațiale vectoriale alături de atribute tabelare tradiționale. Biblioteca este construită pe componente geospațiale consacrate: utilizează Shapely pentru toate operațiunile geometrice, Fiona și GDAL pentru citirea și scrierea formatelor de fișiere spațiale standard, PyProj pentru reproiecția coordonatelor și un index spațial R-tree (din Shapely) pentru a accelera interogările spațiale. Ceea ce distinge GeoPandas este integrarea perfectă a fluxurilor de lucru de analiză spațială completă în ecosistemul pandas. Utilizatorii pot efectua transformări ale sistemului de referință de coordonate pentru a alinia datele între diferite proiecții, pot calcula proprietăți geometrice precum aria și lungimea, pot genera buffere și centroizi și pot efectua operațiuni pe seturi, cum ar fi intersecții și reuniuni. Biblioteca suportă, de asemenea, filtrarea bazată pe locație, join-uri spațiale care combină seturi de date pe baza relațiilor geometrice și analize de suprapunere care produc rezultate agregate. Pentru explorare, oferă capabilități de vizualizare a hărților, generând grafice statice și hărți interactive direct din tabele spațiale. Dincolo de acești diferențiatori principali, GeoPandas gestionează întregul ciclu de viață al datelor geografice: importul și exportul în formate comune precum Shapefile, GeoJSON și GeoPackage; gestionarea tabelelor spațiale care leagă geometria de coloanele de atribute; și interogarea sau filtrarea entităților după locație, condiții de atribut sau predicate spațiale. Documentația sa acoperă instalarea, o referință API cuprinzătoare și ghiduri de utilizare care parcurg sarcinile geospațiale comune.

Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di

Gravitino is a federated metadata lake and unified data catalog designed to manage tables, files, and AI models across diverse data sources and cloud storage. It serves as a centralized interface for governing schemas, access controls, and tagging across relational databases, messaging queues, and object stores. The project distinguishes itself by unifying the management of AI assets, such as machine learning models and their version lineages, alongside traditional tabular data. It also implements the Iceberg REST specification to provide a standardized metadata server and proxy for lakehouse