3 Reasons to Use Apache Spark
Learn why Apache Spark is a great computing framework for all SWE projects that have a focus on big data, large userbases, and multiple locations.
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If you are a developer contemplating a software development project that must support Big Data, a large userbase, and/or multiple locations, Apache Spark should definitely be on your shortlist of considerations for a computing framework. In this article, we look at three reasons you should use Apache Spark in your Big Data projects.
"With thousands of contributing developers and global use of the features and tools, Spark libraries and functionality are growing by the day."
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.
3 Compelling Reasons to Use Apache Spark
Apache Spark is scalable and provides great performance for streaming and batch data with a physical execution engine, a scheduler, and a query optimizer designed to streamline processing and ensure solid performance. Even with large datasets, Apache Spark will produce results quickly and efficiently.
Spark is not restrictive. It supports Cloud applications, Kubernetes, Apache Mesos, and Hadoop and can handle disparate data. Spark can be leveraged in a standalone mode and supports hundreds of types of data sources including Apache Hive, Apache Cassandra, Apache HBase, HDFS, and more!
It’s Easy (and Comprehensive)!
Apache Spark has more than 80 high-level operators and supports projects that require parallel applications. Developers can leverage familiar application languages, develop in SQL, R, Python, Scala, and Java and combine approaches and applications to include streaming functionality, analytics, and SQL foundations. The Spark libraries include support for machine learning, streaming, data frames, and graphics.
One of the most important factors driving the popularity of Apache Spark is the developer community support. With thousands of contributing developers and global use of the features and tools, libraries and functionality are growing every day. Functioning as a tool to process large datasets, Spark is extremely popular, and its influence and use continue to grow.
"If you are contemplating a software development project to support Big Data, Apache Spark should definitely be on your short list of considerations for a computing framework."
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Published at DZone with permission of Kartik Patel. See the original article here.
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