awesome-repositories.com
ब्लॉग
awesome-repositories.com

AI-संचालित खोज के साथ बेहतरीन ओपन-सोर्स रिपॉजिटरी खोजें।

एक्सप्लोर करेंक्यूरेटेड खोजेंओपन-सोर्स विकल्पसेल्फ-होस्टेड सॉफ्टवेयरब्लॉगसाइटमैप
प्रोजेक्टहमारे बारे मेंहम रैंकिंग कैसे करते हैंप्रेसMCP सर्वर
कानूनीगोपनीयताशर्तें
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 रिपॉजिटरी

Awesome GitHub RepositoriesCompute Engine Integrations

Mechanisms that link SQL engines, data processing frameworks, and databases to a shared table format.

Distinguishing note: Candidates refer to UI frameworks or build-time linking, not the runtime integration of SQL/processing engines with tables.

Explore 2 awesome GitHub repositories matching data & databases · Compute Engine Integrations. Refine with filters or upvote what's useful.

Awesome Compute Engine Integrations 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

    Links SQL engines, data processing frameworks, and databases to read from or write to existing tables.

    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

    Connects centralized metadata to various processing engines to enable unified data access.

    Javaai-catalogdata-catalogdatalake
    GitHub पर देखें↗2,866
  1. Home
  2. Data & Databases
  3. Compute Engine Integrations