Apache Spark is an open-source distributed cluster-computing framework and a unified analytics engine for big data processing, with built-in modules for streaming, graph processing, SQL and machine learning. The Spark software provides an interface for programming the entire clusters with implicit data parallelism and fault tolerance. It can primarily process data from a variety of data repositories, including NoSQL databases, the Hadoop Distributed File System (HDFS), and relational data stores, like Apache Hive. It supports in-memory processing to boost the performance of big data analytics applications, and also perform conventional disk-based processing if and when the data sets are too large to fit into any available memory system.
Apache Spark has been lightning-fast cluster computing technology, that is designed for fast computation. Based on Hadoop MapReduce, it extends the MapReduce model to efficiently use it for more types of computations, like interactive queries and stream processing. Some of the main features of Apache Spark is its speed to help allowing applications in Hadoop Cluster to run 100 times faster in memory and 10 times faster on disk. It supports multiple languages and built-in APIs in Java, Scala, or Python. Its advanced analytics supports ‘Map,’ ‘Reduce,’ SQL queries, Streaming data, Machine learning (ML), and Graph Algorithms. If you are business who would like to get in touch with customers of Apache Spark, our Apache Spark Customers List will be the right one.
Companies Using Apache Spark, Market Share & Customers List
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Companies Currently Using Apache Spark (Sample Data)
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