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Awesome GitHub RepositoriesLakehouse Storage Layers

High-performance storage layers that combine data lake scalability with data warehouse consistency.

Distinct from Streaming Data Lakehouses: Focuses on the storage layer architecture rather than the streaming ingestion pipelines specifically.

Explore 2 awesome GitHub repositories matching data & databases · Lakehouse Storage Layers. Refine with filters or upvote what's useful.

Awesome Lakehouse Storage Layers GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • delta-io/deltadelta-io का अवतार

    delta-io/delta

    8,596GitHub पर देखें↗

    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.

    Scalaacidanalyticsbig-data
    GitHub पर देखें↗8,596
  • apache/gravitinoapache का अवतार

    apache/gravitino

    2,866GitHub पर देखें↗

    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.

    Javaai-catalogdata-catalogdatalake
    GitHub पर देखें↗2,866
  1. Home
  2. Data & Databases
  3. Data Processing Pipelines
  4. Data Processing Frameworks
  5. Streaming Data Lakehouses
  6. Lakehouse Storage Layers

सब-टैग एक्सप्लोर करें

  • Storage Backend ConfigurationConfiguration of credentials and physical paths for lakehouse storage layers. **Distinct from Lakehouse Storage Layers:** Focuses on the configuration of the storage layer rather than the architecture of the layer itself.