![]() ![]() Once data is loaded into an RDD, Spark performs transformations and actions on RDDs in memory-the key to Spark’s speed. Spark loads data by referencing a data source or by parallelizing an existing collection with the SparkContext parallelize method into an RDD for processing. ![]() RDDs are a fundamental structure in Apache Spark. Resilient Distributed Datasets (RDDs) are fault-tolerant collections of elements that can be distributed among multiple nodes in a cluster and worked on in parallel. It also creates Resilient Distributed Datasets (RDDs), which are the key to Spark’s remarkable processing speed. The Spark Driver is the master node that controls the cluster manager, which manages the worker (slave) nodes and delivers data results to the application client.īased on the application code, Spark Driver generates the SparkContext, which works with the cluster manager-Spark’s Standalone Cluster Manager or other cluster managers like Hadoop YARN, Kubernetes, or Mesos- to distribute and monitor execution across the nodes. ![]() It’s also included as a core component of several commercial big data offerings.Īpache Spark has a hierarchical master/slave architecture. Today, it’s maintained by the Apache Software Foundation and boasts the largest open source community in big data, with over 1,000 contributors. Spark was developed in 2009 at UC Berkeley. (You’ll find more on how Spark compares to and complements Hadoop elsewhere in this article.) The chief difference between Spark and MapReduce is that Spark processes and keeps the data in memory for subsequent steps-without writing to or reading from disk-which results in dramatically faster processing speeds. Spark is often compared to Apache Hadoop, and specifically to MapReduce, Hadoop’s native data-processing component. It even includes APIs for programming languages that are popular among data analysts and data scientists, including Scala, Java, Python, and R. It scales by distributing processing work across large clusters of computers, with built-in parallelism and fault tolerance. Spark's analytics engine processes data 10 to 100 times faster than alternatives. It is designed to deliver the computational speed, scalability, and programmability required for Big Data-specifically for streaming data, graph data, machine learning, and artificial intelligence (AI) applications. Apache Spark is a lightning-fast, open source data-processing engine for machine learning and AI applications, backed by the largest open source community in big data.Īpache Spark (Spark) is an open source data-processing engine for large data sets. ![]()
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