3 repository-uri
Systems that integrate streaming ingestion with open table formats for unified batch and stream processing.
Distinct from Unified Batch and Stream Processing Engines: Focuses on the Lakehouse architecture (integrating with open table formats like Iceberg) rather than just a processing engine.
Explore 3 awesome GitHub repositories matching data & databases · Streaming Data Lakehouses. 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
Integrates streaming ingestion with open table formats like Apache Iceberg for unified batch and stream processing.
Delta is a lakehouse table format that brings ACID transactions and data warehouse consistency to large scale data lakes on cloud object storage. It serves as an ACID transaction manager, coordinating atomic commits and serializable isolation for concurrent reads and writes across distributed compute engines. The project provides a multi-engine interoperability layer that uses format translation to allow diverse SQL engines and processing frameworks to read and write the same tables. It functions as a data versioning system, utilizing a transaction log to enable time travel, historical snapsh
Unifies data lake scalability with data warehouse consistency to create a high-performance storage layer.
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
Sets storage backend credentials and defines physical locations for lakehouse data.