Java has been the go-to coding language for decades, but as advancements in big data processing continue to emerge, Java developers are forced to learn new skills and explore additional programming languages. That is especially true when developers start working with massive amounts of data and need more elegant solutions, faster.
As an alternative to Hadoop, Apache Spark is gaining popularity in the software development world. Spark is a fast, data-processing platform that is perfect for working with big data. Its creators call it a "unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing."
Apache Spark can process analytics and machine learning workloads, perform ETL processing and execution of SQL queries, streamline machine learning applications, and more. But, one of the most important differences when working with Apache Spark is that it allows for the user to perform multiple operations simultaneously