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 是一个数据湖版本控制系统，为存储在对象存储中的大型数据集提供类似 Git 的分支和提交功能。它作为一个版本控制层，支持创建不可变快照、原子提交和零拷贝分支，从而在不复制物理文件的情况下为数据实验创建隔离环境。 该系统充当 S3 兼容的存储网关和 Iceberg REST 目录，允许标准云存储协议和兼容客户端管理版本化表。它通过使用事件驱动的钩子系统在更改合并到生产环境之前根据治理策略验证数据集，从而充当数据质量守门人。 该平台涵盖了广泛的数据治理功能，包括 Pull Request 协作、基于角色的访问控制和数据血缘追踪。它为工作流编排、机器学习管线和各种大数据计算引擎提供了集成，支持多云存储连接以及通过 SSO 和 SCIM 进行身份同步。 该软件可以使用二进制文件、容器或 Helm Chart 安装，以便在 Kubernetes 上部署。

GeoPandas 是一个 Python 库，通过对地理空间数据的原生支持扩展了 pandas。它将地理几何图形（点、线和多边形）视为 DataFrame 中的一等列类型，使用户能够将矢量空间数据与传统的表格属性一起存储、操作和分析。该库构建在成熟的地理空间组件之上：它使用 Shapely 进行所有几何运算，使用 Fiona 和 GDAL 读取和写入标准空间文件格式，使用 PyProj 进行坐标重投影，并使用 R-tree 空间索引（来自 Shapely）来加速空间查询。 GeoPandas 的独特之处在于它将完整的空间分析工作流无缝集成到了 pandas 生态系统中。用户可以执行坐标参考系统转换以对齐不同投影的数据，计算面积和长度等几何属性，生成缓冲区和质心，并进行交集和并集等集合运算。该库还支持基于位置的过滤、基于几何关系合并数据集的空间连接，以及产生聚合结果的叠加分析。在探索方面，它提供了地图可视化功能，可直接从空间表生成静态图表和交互式地图。 除了这些核心差异外，GeoPandas 还处理地理数据的全生命周期：从 Shapefile、GeoJSON 和 GeoPackage 等常见格式导入和导出；管理将几何图形与属性列链接的空间表；以及按位置、属性条件或空间谓词查询或过滤要素。其文档涵盖了安装、全面的 API 参考以及引导用户完成常见地理空间任务的用户指南。

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