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Apache Spark, designed by AMPLab of the University of California, Berkley and hosted by Apache Software Foundation is an open source software framework for high-speed cluster computing. It has a flexible and agile form of a computing and works on various other open source software frameworks and supports existing data of the aforementioned frameworks. It has an in-memory data processing engine and is a distributary platform for the development and deployment of the complex multi-layered applications. Spark offers Spark-shell for a beginner to start running and writing applications, thus making it an ideal software.


  • High-speed processing: It runs 100 times faster than Hadoop’s MapReduce in memory and 10 times faster on disk.

  • Stream processing in real time: It can process and manipulate data in real time. It can rapidly develop the streaming application. It has the capability to recover lost works and has an only one semantic delivery, without extra codes and configurations. Streaming data can be easily joined with past data, and same codes for batch and streaming process can be reused

  • Easy to integrate with the various open-source frameworks: Spark uses Hadoop for processing and storage of data. It is solely independent of Hadoop and has its own cluster computing network. It is flexible and runs on other frameworks and their supports their existing data such as Mesos, YARN, and EC2. It also has the capability to run as an utterly standalone or on the cloud.

  • Active community: Since its inception in the year 2014, the spark is growing more active with time as the number of developers contributing to it is rapidly increasing and making its data richer. The community of developers and application created by them on spark is swiftly increasing due to high-speed processing, the flexibility of language and support for the existing tools of the developers.

  • Library-Friendly: It supports highly sophisticated analytics such as SQL (SELECT Query) and has a system that supports a vast number of libraries and their resources. For machine learning, it supports Mllib, DataFrames, for Application Programming Interface (API) - GraphX and spark streaming for building a scalable fault- tolerant streaming application.

  • Unity in API: DataFrames, a high-level structured API gives better performance and easy to use structure along with DataSets that provides type safety.

  • Speedy compiler: Spark uses upgraded tungsten engines that collapse the query plan into a single function and eliminates unwanted function calls. It uses the CPU register that is used by the compilers for intermediate data storage.

  • Access to data sources: It is easier to use since it has access to various data sources such as Cassandra, HDFS, S3, Azure storage service, Oracle, and HBase.

  • Supports various programming applications: Developers may use a variety of programming language such as Java, Python, R, Scala. Spark supports as many as 80 high-level operators that support programming languages to build parallel applications. The overall feature increases the ability and the efficiency of the platform for the developers and renders it easier than other open source cluster computing systems.


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