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    MongoDB Atlas runs apps anywhere

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    Build Agents and Models on One Platform

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  • 1
    The Tengo Language

    The Tengo Language

    A fast script language for Go

    Tengo is a small, dynamic, fast, secure script language for Go. Tengo is fast and secure because it's compiled/executed as bytecode on stack-based VM that's written in native Go. Securely Embeddable and Extensible. Compiler/runtime written in native Go (no external deps or cgo). Executable as a standalone language / REPL. Use cases, rules engine, state machine, data pipeline, transpiler.
    Downloads: 0 This Week
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  • 2
    Kestra

    Kestra

    Kestra is an infinitely scalable orchestration and scheduling platform

    Build reliable workflows, blazingly fast, deploy in just a few clicks. Kestra is an open-source, event-driven orchestrator that simplifies data operations and improves collaboration between engineers and business users. By bringing Infrastructure as Code best practices to data pipelines, Kestra allows you to build reliable workflows and manage them with confidence. Thanks to the declarative YAML interface for defining orchestration logic, everyone who benefits from analytics can participate in the data pipeline creation process. ...
    Downloads: 3 This Week
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  • 3
    GenStage

    GenStage

    Producer and consumer actors with back-pressure for Elixir

    GenStage is a specification and set of behaviours for building demand-driven data pipelines on the BEAM. It formalizes the roles of producers, consumers, and producer-consumers, using back-pressure so that fast producers don’t overwhelm downstream stages. Developers implement callbacks like handle_demand and handle_events to control how items are emitted, transformed, and consumed across asynchronous boundaries. Because stages are OTP processes, you gain fault tolerance, supervised restarts, and concurrency tuned via configurable demand and partitioning. ...
    Downloads: 0 This Week
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  • 4
    PipeRider

    PipeRider

    Code review for data in dbt

    PipeRider automatically compares your data to highlight the difference in impacted downstream dbt models so you can merge your Pull Requests with confidence. PipeRider can profile your dbt models and obtain information such as basic data composition, quantiles, histograms, text length, top categories, and more. PipeRider can integrate with dbt metrics and present the time-series data of metrics in the report. PipeRider generates a static HTML report each time it runs, which can be viewed...
    Downloads: 0 This Week
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