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
·

4 रिपॉजिटरी

Awesome GitHub RepositoriesEvent Stream Aggregators

Tools that collect and present a chronological stream of events from a specific API.

Distinct from GitHub API Aggregators: Focuses on the aggregation of event streams into a unified view rather than just metadata fetching.

Explore 4 awesome GitHub repositories matching data & databases · Event Stream Aggregators. Refine with filters or upvote what's useful.

Awesome Event Stream Aggregators GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • devhubapp/devhubdevhubapp का अवतार

    devhubapp/devhub

    10,100GitHub पर देखें↗

    Devhub is a cross-platform developer tool and event aggregator designed to monitor GitHub activities. It provides a unified interface for tracking issues, notifications, and user actions across multiple repositories, consolidating these updates into a single view to reduce notification clutter. The application utilizes a multi-column dashboard for organizing data streams via customizable filters and saved searches. This interface allows for the management of review queues, the monitoring of specific user actions, and the display of notification context without requiring navigation to the sour

    Collects repository updates and user actions into a single view to reduce notification clutter.

    TypeScript
    GitHub पर देखें↗10,100
  • apache/pinotapache का अवतार

    apache/pinot

    6,098GitHub पर देखें↗

    Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

    Consumes events from streaming sources to create a unified, queryable SQL view across microservice architectures.

    Java
    GitHub पर देखें↗6,098
  • square/cubesquare का अवतार

    square/cube

    3,878GitHub पर देखें↗

    Cube is a time-series analytics platform and event data store designed for real-time performance monitoring. It functions as a metrics engine that ingests timestamped event streams and persists raw logs to enable the computation of statistical summaries, quantiles, and histograms. The system distinguishes itself through a reactive processing model that automatically invalidates metric caches when new events arrive, ensuring query results remain current. It supports both real-time event streaming via persistent connections and the calculation of post hoc statistics from stored event sets. The

    Converts raw event streams into aggregate statistics, quantiles, and histograms for high-level system observation.

    JavaScript
    GitHub पर देखें↗3,878
  • bluesky-social/indigobluesky-social का अवतार

    bluesky-social/indigo

    1,357GitHub पर देखें↗

    Indigo is a Go-based framework and toolkit designed for building, hosting, and scaling services within the AT Protocol ecosystem. It provides the foundational infrastructure for decentralized social networking, enabling developers to implement relay services, manage cryptographically signed user repositories, and handle identity resolution across federated environments. The project distinguishes itself through a robust architecture that decouples content hosting from discovery, allowing for independent moderation and algorithmic feed generation. It utilizes content-addressed storage and Merkl

    Collects and streams data records from multiple personal data servers into a unified feed for real-time network monitoring and indexing.

    Goatprotoblueskydweb
    GitHub पर देखें↗1,357
  1. Home
  2. Data & Databases
  3. Data Processing Pipelines
  4. Data Transformation
  5. Data Aggregation Tools
  6. GitHub API Aggregators
  7. Event Stream Aggregators

सब-टैग एक्सप्लोर करें

  • Aggregate Store BuildersSystems that consume streaming events to create unified, queryable SQL views. **Distinct from Event Stream Aggregators:** Distinct from Event Stream Aggregators: focuses on building persistent, queryable SQL stores rather than just presenting event streams.
  • Statistical SummariesComputational tools for deriving quantiles and histograms from event streams. **Distinct from Event Stream Aggregators:** Distinct from Event Stream Aggregators by focusing on the specific statistical outputs (quantiles, histograms) rather than just chronological stream consolidation.