5 Repos
Administrative operations for Iceberg tables, including catalog management and data compaction.
Distinct from Table Managers: Focuses specifically on Iceberg-specific maintenance like compaction, whereas Table Managers is a general coordinating role.
Explore 5 awesome GitHub repositories matching data & databases · Iceberg Table Lifecycle Management. Refine with filters or upvote what's useful.
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
Handles the lifecycle of Iceberg tables, including catalog management and automated compaction.
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
Manages large analytic datasets in Iceberg format with snapshot isolation, branching, tagging, and full DML support.
lakeFS ist ein Data-Lake-Versionierungssystem, das Git-ähnliche Branching- und Commit-Funktionen für große Datensätze in Objektspeichern bietet. Es fungiert als Versionskontrollschicht und ermöglicht die Erstellung unveränderlicher Snapshots, atomarer Commits und Zero-Copy-Branching, um isolierte Umgebungen für Datenexperimente zu schaffen, ohne physische Dateien zu duplizieren. Das System dient als S3-kompatibles Storage-Gateway und Iceberg-REST-Katalog, wodurch Standard-Cloud-Storage-Protokolle und kompatible Clients versionierte Tabellen verwalten können. Es fungiert als Data-Quality-Gatekeeper, indem es ein eventgesteuertes Hook-System nutzt, um Datensätze gegen Governance-Richtlinien zu validieren, bevor Änderungen in die Produktion gemergt werden. Die Plattform deckt umfassende Funktionen für Data-Governance ab, einschließlich Pull-Request-Kollaboration, rollenbasierter Zugriffskontrolle und Data-Lineage-Tracking. Sie bietet Integrationen für Workflow-Orchestrierung, Machine-Learning-Pipelines und verschiedene Big-Data-Compute-Engines und unterstützt Multi-Cloud-Storage-Konnektivität sowie Identitätssynchronisation via SSO und SCIM. Die Software kann mittels Binärdateien, Containern oder Helm-Charts für die Bereitstellung auf Kubernetes installiert werden.
Provides a complete history of modifications for Iceberg tables by staging changes on specific references.
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
Lists historical versions of tables to enable time-travel analysis and data auditing.
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
Provides a metadata service for Iceberg tables via Hive Thrift, JDBC, and REST APIs.