Showing 4 open source projects for "build"

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  • 1
    doobie

    doobie

    Functional JDBC layer for Scala

    ...New versions are not binary compatible with prior versions, although in most cases user code will be source compatible. Starting with the 0.5.x we’re trying to be a bit more careful about versioning. If you want to build and run the tests for yourself, you’ll need a local postgresql database.
    Downloads: 1 This Week
    Last Update:
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  • 2
    Prisma 1

    Prisma 1

    Database Tools incl. ORM, migrations and admin UI

    ...Access, Type-safe database access with the auto-generated Prisma client (in JavaScript, TypeScript, Go). Migrate, declarative data modeling and migrations (optional). Manage, visual data management with Prisma Admin. It is used to build GraphQL, REST, gRPC APIs and a lot more. Prisma currently supports MySQL, PostgreSQL, MongoDB. Prisma is a great fit for building REST& gRPC APIs where it can be used in place of traditional ORMs. It provides many benefits such as type safety, a modern API and flexible ways for reading and writing relational data.
    Downloads: 0 This Week
    Last Update:
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  • 3
    Cosmos DB Spark

    Cosmos DB Spark

    Apache Spark Connector for Azure Cosmos DB

    Azure Cosmos DB Spark is the official connector for Azure CosmosDB and Apache Spark. The connector allows you to easily read to and write from Azure Cosmos DB via Apache Spark DataFrames in Python and Scala. It also allows you to easily create a lambda architecture for batch-processing, stream-processing, and a serving layer while being globally replicated and minimizing the latency involved in working with big data.
    Downloads: 0 This Week
    Last Update:
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  • 4
    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
    Last Update:
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