Spark is a distributed open-source cluster-computing framework and includes an interface for programming a full suite of clusters with comprehensive fault tolerance and support for data parallelism. Spark can be used effectively to provide support for Java, Scala, Python and R programming and is suitable for SQL, streaming data, processing graphs and for machine learning.
If you are a developer, contemplating a software development project that must support Big Data, a large user base and/or multiple locations, Apache Spark should definitely be on your short list of considerations for a computing framework. In this article, we look at three reasons you should use Apache Spark in your Big Data projects.
Apache Spark Optimizes Data and Performance!
What is Apache Spark? The Apache Spark framework includes Spark Core to manage memory and interact with storage systems, Spark Streaming to process live data streams, Spark SQL supporting SQL with HiveQL, MLlib supporting machine-learning algorithms, regression, clustering and filtering, and GraphX supporting graph manipulation and computations. This framework makes it easier to stream data and to quickly process analytics and algorithms, so your applications will run faster and your enterprise can manage Big Data and high volume data.
Can Spark Consulting Help Me Simplify the Complexities of Apache Spark?
Apache Spark enables programmers with an application-programming interface that focuses on data structure. Apache Spark programming allows Spark consultants to expand the capabilities of development and programming, map functions across data, and simplify data results. The tool supports Hadoop YARN, Apache Mesos, Hadoop Distributed File System, Cassandra, OpenStack Swift, Amazon 53, Kudu, and MapR File System. It offers the Apache Spark developer a powerful tool to work in an integrated environment and simplify the programming environs.