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Showing 2 open source projects for "data flow"

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  • Go from Data Warehouse to Data and AI platform with BigQuery Icon
    Go from Data Warehouse to Data and AI platform with BigQuery

    Build, train, and run ML models with simple SQL. Automate data prep, analysis, and predictions with built-in AI assistance from Gemini.

    BigQuery is more than a data warehouse—it's an autonomous data-to-AI platform. Use familiar SQL to train ML models, run time-series forecasts, and generate AI-powered insights with native Gemini integration. Built-in agents handle data engineering and data science workflows automatically. Get $300 in free credit, query 1 TB, and store 10 GB free monthly.
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  • 99.99% Uptime for MySQL and PostgreSQL on Google Cloud Icon
    99.99% Uptime for MySQL and PostgreSQL on Google Cloud

    Enterprise Plus edition delivers sub-second maintenance downtime and 2x read/write performance. Built for critical apps.

    Cloud SQL Enterprise Plus gives you a 99.99% availability SLA with near-zero downtime maintenance—typically under 10 seconds. Get 2x better read/write performance, intelligent data caching, and 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server with built-in vector search for gen AI apps. New customers get $300 in free credit.
    Try Cloud SQL Free
  • 1
    Claude-Flow

    Claude-Flow

    The leading agent orchestration platform for Claude

    Claude-Flow v2 Alpha is an advanced AI orchestration and automation framework designed for enterprise-grade, large-scale AI-driven development. It enables developers to coordinate multiple specialized AI agents in real time through a hive-mind architecture, combining swarm intelligence, neural reasoning, and a powerful set of 87 Modular Control Protocol (MCP) tools. The platform supports both quick swarm tasks and persistent multi-agent sessions known as hives, facilitating distributed AI...
    Downloads: 0 This Week
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  • 2
    FlowLens MCP

    FlowLens MCP

    Open-source MCP server that gives your coding agent

    ...It works together with a companion browser extension: when a user reproduces a bug or a complicated UI interaction, the extension captures a rich session log, including screen/video recording, network traffic, console logs, DOM events, storage changes, and more, and exports it. The MCP server then loads this captured “flow” and exposes it to the AI agent via the Model Context Protocol (MCP), letting the agent examine, search, filter, and reason about the session just as a human developer would, without needing the agent to re-run the flow or rely on minimal reproduction data (logs, screenshots).
    Downloads: 0 This Week
    Last Update:
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