Showing 8 open source projects for "as built"

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  • 1
    Play Framework

    Play Framework

    A high velocity web framework

    ...Make your changes and simply hit refresh! All you need is a browser and a text editor. Underneath the covers Play uses a fully asynchronous model built on top of Akka.
    Downloads: 1 This Week
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  • 2
    Gatling

    Gatling

    Modern Load Testing as Code

    Gatling is a high-performance load testing tool built on the JVM that emphasizes realism, scalability, and developer ergonomics. Test scenarios are scripted in a concise Scala-based DSL, allowing you to model user journeys with think times, feeders (dynamic data), checks, and assertions all in code. Its asynchronous, non-blocking engine (backed by Netty) can drive very high concurrency from a single injector, reducing the need for large injector farms.
    Downloads: 5 This Week
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  • 3
    Chipyard

    Chipyard

    An Agile RISC-V SoC Design Framework with in-order cores

    Chipyard is a framework and generator for constructing custom RISC‑V SoC hardware. Built at UC Berkeley, it leverages Chisel/FIRRTL to generate full-stack systems—from CPU cores to peripherals—and includes simulators, FPGA deployment tools, and integration with Rocket Chip and other RISC‑V ecosystems.
    Downloads: 0 This Week
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  • 4
    Apache Spark

    Apache Spark

    A unified analytics engine for large-scale data processing

    ...Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming (microbatches) and Structured Streaming, it delivers low-latency event processing suitable for real-time analytics. The built-in MLlib library provides scalable machine learning algorithms, while GraphX enables graph computations integrated with data pipelines. Spark supports multiple languages—Scala, Java, Python, R—and connects with many storage systems like HDFS, S3, Cassandra, and streaming platforms like Kafka, making it a versatile choice for big data workloads in analytics, ETL, and data science.
    Downloads: 5 This Week
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    Forever Free Full-Stack Observability | Grafana Cloud

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  • 5
    Finatra

    Finatra

    Fast, testable, Scala services built on TwitterServer and Finagle

    Finatra builds on TwitterServer and uses Finagle, therefore it is highly recommended that you familiarize yourself with those frameworks before getting started. The version of Finatra documented here is version 2.x. Version 2.x is a complete rewrite over v1.x and as such many things are different. Finatra at its core is agnostic to the type of service or application being created. It can be used to build anything based on TwitterUtil: c.t.app.App. For servers, Finatra builds on top of the...
    Downloads: 0 This Week
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  • 6
    Scalding

    Scalding

    A Scala API for Cascading

    Scalding is a Scala DSL built on Cascading that simplifies writing Hadoop MapReduce jobs. It lets users describe data transformations using Scala’s functional abstractions, while abstracting away low-level Hadoop boilerplate. It enables expressive and testable pipeline definitions and integrates with various input/output formats.
    Downloads: 0 This Week
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  • 7
    Graphcool Framework

    Graphcool Framework

    Graphcool is an open-source backend development framework

    ...Users could deploy Graphcool either locally (e.g. via Docker) or on a managed cloud offering. The framework also provided features like schema evolution, migrations, data loaders for performance, and built-in tooling to manage endpoints and deployments.
    Downloads: 0 This Week
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  • 8
    Apache PredictionIO

    Apache PredictionIO

    Machine learning server for developers and ML engineers

    Apache PredictionIO® is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. Quickly build and deploy an engine as a web service on production with customizable templates; respond to dynamic queries in real-time once deployed as a web service; evaluate and tune multiple engine variants systematically; unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics; speed up machine learning modeling with systematic processes and pre-built evaluation measures; support machine learning and data processing libraries such as Spark MLLib and OpenNLP; implement your own machine learning models and seamlessly incorporate them into your engine; simplify data infrastructure management.
    Downloads: 0 This Week
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