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5 个仓库

Awesome GitHub RepositoriesData Lakes

Unified storage repositories for managing large-scale analytical datasets.

Distinguishing note: Focuses on cloud-native integration for analytical processing.

Explore 5 awesome GitHub repositories matching data & databases · Data Lakes. Refine with filters or upvote what's useful.

Awesome Data Lakes GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • seaweedfs/seaweedfsseaweedfs 的头像

    seaweedfs/seaweedfs

    32,937在 GitHub 上查看↗

    SeaweedFS is a distributed object store and high-performance file system designed to manage massive volumes of unstructured data. It utilizes a decoupled architecture that separates metadata management from raw data storage, allowing for independent scalability and the efficient handling of billions of files. By providing a POSIX-compliant interface, it enables applications to interact with a unified namespace while maintaining the performance characteristics of a distributed object store. The system distinguishes itself through a multi-region data fabric that supports active-active replicati

    Manages large-scale analytical datasets in a unified storage layer for modern data processing.

    Goblob-storagecloud-drivedistributed-file-system
    在 GitHub 上查看↗32,937
  • activeloopai/hubactiveloopai 的头像

    activeloopai/Hub

    9,177在 GitHub 上查看↗

    Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea

    Provides a scalable multimodal data lake for organizing and retrieving large datasets for AI training.

    C++
    在 GitHub 上查看↗9,177
  • activeloopai/deeplakeactiveloopai 的头像

    activeloopai/deeplake

    9,175在 GitHub 上查看↗

    DeepLake is AI data infrastructure consisting of a multimodal data lake, a hybrid search engine, and a serverless vector database. It provides a PostgreSQL-based AI data runtime that combines multimodal storage with streaming pipelines to load and shuffle datasets from cloud storage directly into deep learning training pipelines. The system utilizes lazy indexing to store and slice images, audio, and video without loading entire files into memory. It enables retrieval-augmented generation by persisting high-dimensional embeddings in a serverless vector store and implementing hybrid search tha

    Manages multimodal AI data types optimized for deep learning using lazy loading to prevent memory overflow.

    C++agentagentic-ragai
    在 GitHub 上查看↗9,175
  • ossf/scorecardossf 的头像

    ossf/scorecard

    5,527在 GitHub 上查看↗

    Scorecard is an open source security scanner and software supply chain analysis tool that evaluates the security posture of projects by calculating risk metrics based on best practices. It functions as a security health dashboard, visualizing security gaps through scores and badges to help maintainers identify vulnerabilities. The project provides a system for monitoring repository security through a GitHub Action security auditor that alerts maintainers when security scores drop. It also offers a mechanism for vulnerability remediation guidance, mapping identified security gaps to prescripti

    Stores aggregated security scan results in a public BigQuery dataset for large-scale analysis.

    Go
    在 GitHub 上查看↗5,527
  • slatedb/slatedbslatedb 的头像

    slatedb/slatedb

    2,730在 GitHub 上查看↗

    SlateDB is a cloud-native key-value store and distributed database engine that utilizes a log-structured merge-tree architecture. It serves as a transactional storage layer designed to persist data directly to cloud object storage. The engine differentiates itself by optimizing read performance for remote storage through the use of bloom filters and multi-level block caching. It employs a single-writer multi-reader model and provides the ability to create zero-copy clones via copy-on-write checkpointing. The system supports atomic transactions, range queries, and snapshot-based concurrency c

    Serves as a high-performance indexing layer for large-scale analytical datasets stored in cloud data lakes.

    Rustdatabaseembedded-databaselsm-tree
    在 GitHub 上查看↗2,730
  1. Home
  2. Data & Databases
  3. Data Lakes

探索子标签

  • MultimodalStorage repositories optimized for organizing and retrieving diverse data types like audio, images, and text. **Distinct from Data Lakes:** Distinct from Data Lakes: specifically handles multimodal AI data types optimized for deep learning.