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.
‘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.
Here are three compelling reasons to use Apache Spark:
It’s Fast! – 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.
It’s Flexible! – Spark is not restrictive. It supports Cloud applications, Kubermetes, 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 etc.
It’s Easy (and Comprehensive)! – Apache Spark has more than eighty 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 Apache Spark popularity is the developer community support. With thousands of contributing developers and global use of the features and tools, the libraries and functionality are growing by the day. As a tool to process large datasets, Spark is extremely popular and its influence and use continues 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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