Apache Spark is an open-source software framework for cluster computing that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It was initially developed at the UC Berkeley’s AMPLab as part of the developing Berkeley Data Analytics Stack (BDAS).
Spark provides developers with an application programming interface (API) to perform complex data analytics on large datasets. Its core features include mapreduce, in-memory computation, cyclic data flow, transactional support, interactive queries and sophisticated caching. These capabilities have enabled it to become one of the most popular Big Data processing frameworks being used for machine learning and data analysis today.
It is written in Scala, Java, Python and R and provides libraries in all these languages for data analytics on distributed data sets. It also supports stream processing using Spark Streaming. Additionally, Spark can interact with a range of storage sources including HDFS, Hive, Cassandra, Kafka and Amazon S3.
Apache Spark lets the user run computations in a distributed manner by distributing the computations across multiple machines. It has a master-slave architecture and is optimized to run in a distributed environment such as on multiple computers or nodes of a cluster.
One of the main advantages of Apache Spark is its speed. It has a much faster execution time for data processing compared to Hadoop MapReduce due to its in-memory processing model. This in-memory storage feature allows the user to process data faster and in a more efficient manner.
Apache Spark is a powerful framework for distributed computing and its features have enabled it to become the most popular Big Data processing framework. It supports a range of programming languages and can interact with a range of storage systems and other data sources, allowing for analyzing large datasets in a much faster and easier way.
