Showing 4 open source projects for "database documentation"

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  • 1
    Slick database

    Slick database

    Slick (Scala Language Integrated Connection Kit) is a modern database

    Slick is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred. You can write your database queries in Scala instead of SQL, thus profiting from the static checking, compile-time safety and compositionality of Scala. Slick features an extensible query compiler which can generate code for different...
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  • 2
    Skunk

    Skunk

    A data access library for Scala + Postgres

    Skunk is a Postgres library for Scala. Skunk is powered by cats, cats-effect, scodec, and fs2. Skunk is purely functional, non-blocking, and provides a tagless-final API. Skunk gives very good error messages. Skunk embraces the Scala Code of Conduct. Skunk is pre-release software! Code and documentation are under active development! Skunk is published for Scala 2.12/2.13/3.1 and can be included in your project.Query and Command types are usually inferrable, but specifying a type ensures that...
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  • 3
    SnappyData

    SnappyData

    Memory optimized analytics database, based on Apache Spark

    SnappyData (aka TIBCO ComputeDB) is a distributed, in-memory optimized analytics database. SnappyData delivers high throughput, low latency, and high concurrency for a unified analytics workload. By fusing an in-memory hybrid database inside Apache Spark, it provides analytic query processing, mutability/transactions, access to virtually all big data sources and stream processing all in one unified cluster. One common use case for SnappyData is to provide analytics at interactive speeds over...
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  • 4
    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.
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