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Apache Spark is a general-purpose engine for large-scale data processing. It supports rapid application development for big data and allows for code reuse across batch, interactive, and streaming applications. Apache Spark delivers in-memory processing for big data and enables faster application development. The most popular use cases for Apache Spark include building data pipelines and developing machine learning models. MapR is the choice for production Spark applications. The MapR Platform including Spark consists of the complete Spark stack engineered to support advanced analytic applications, along with patented innovations in the MapR Platform, plus key open source projects that complement Spark. This enables advanced analytics including batch processing, machine learning, SQL, and graph computation. Because Spark runs seamlessly on MapR, it benefits from the platform’s patented enterprise-grade features such as web-scale storage, high availability, mirroring, snapshots, NFS, integrated security, global namespace, etc. MapR was the first in the industry and remains the only one to support the entire Spark stack. This includes Spark SQL, Spark Streaming, MLlib, GraphX, and SparkR. MapR has added significant innovations to improve Spark performance, reliability, and flexibility.