Apache Spark is an open source processing engine used for faster performance, ease of use and sophisticated analytics. Apache Spark can collectively process huge amount of data present in clusters over multiple nodes. Parallel processing framework of Apache Spark enables users to run large scale data analytics applications. It supports in-memory processing boosting the performance of applications based on big data analytics; however, it can also perform disk-based processing when available system memory is unable to store the data sets.
Spark has evolved as an effective alternative for Hadoop. Its speed of processing is high compared to Hadoop as a result of its bottom-up engineering. Its popularity has increased recently for on-disk sorting, involving large data sets. Easy-to-use application program interface (API) is developed to handle large amount of data. It includes multiple operators for manipulating semi-structured data by revamping all the information and familiar data APIs. At the heart of Apache Spark is a unified engine, it includes support for SQL queries, higher-level libraries, machine learning, streaming data and graph processing.
Also, these libraries can be combined seamlessly to create complex workflows. Apache Spark can be deployed in the cloud on the Amazon Elastic Compute Cloud (EC2) service or as a standalone application. Due to its advanced features and functionality, popularity of Apache Spark has increased within the developers, integrators and end-users. It supports multiple languages so that the developers can write applications in Java, Python, Scala or R, further increasing the popularity of Apache Spark.
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Furthermore, adoption and deployment of Spark has been faster as it came on the back of Hadoop. It integrates seamlessly with Hadoop data sources such as Hadoop distributed file system (HDFS), Hive, HBase and Cassandra and Hadoop ecosystem. Spark has matured and it has become a mainstream solution at a perfect time when Internet of Things (IoT) devices are proliferating in the market. IoT devices are anticipated to drive the Apache Spark market during the coming years as the need for processing large data sets is expected to increase.
Apache Spark supports advanced analytics such as streaming data, machine learning (ML), SQL queries and graph algorithms. These four components also form the core of Apache Spark. Full recovery from faults and failures is possible as the objects are stored in resilient distributed datasets (RDD). Real-time queries are enabled with the help of Apache Spark, increasing the efficiency of the data processing system. Spark clearly differentiates between importing data and distributed computation.
