It ingests data in mini-batches and performs RDD (Resilient Distributed Datasets) transformations on those mini-batches of data. Spark Streaming leverages Spark Core's fast scheduling capability to perform streaming analytics. Spark SQL is a component on top of Spark Core that introduces a new data abstraction called SchemaRDD, which provides support for structured and semi-structured data. It provides In-Memory computing and referencing datasets in external storage systems. Spark Core is the underlying general execution engine for spark platform that all other functionality is built upon. The following illustration depicts the different components of Spark. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph algorithms. Advanced Analytics − Spark not only supports ‘Map’ and ‘reduce’.Spark comes up with 80 high-level operators for interactive querying. Therefore, you can write applications in different languages. Supports multiple languages − Spark provides built-in APIs in Java, Scala, or Python.It stores the intermediate processing data in memory. This is possible by reducing number of read/write operations to disk. Speed − Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk.Apart from supporting all these workload in a respective system, it reduces the management burden of maintaining separate tools. Spark is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries and streaming. The main feature of Spark is its in-memory cluster computing that increases the processing speed of an application. It is based on Hadoop MapReduce and it extends the MapReduce model to efficiently use it for more types of computations, which includes interactive queries and stream processing. Apache Spark is a lightning-fast cluster computing technology, designed for fast computation.
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