Apache Spark For The Impatient
Apache Spark has revolutionized the world of Big Data. Faster, more scalable and more sustainable, this open source distributed computing platform offers more powerful features than many other proprietary solutions.
Like Hadoop, Spark is a framework which provides a number of inter-connected platforms, systems and standards for Big Data projects.Spark is open-source under Apache Software Foundation. One of the most interesting aspects of an open source solution is how active its community is. The developer community enhances the platform’s features and helps other programmers implement solutions or solve problems.
Spark is seen by techies in the industry as a more advanced product than Hadoop. It is newer, and designed to work by processing data in memory chunks. Apache Spark enables programmers to perform operations on a large volume of data in clusters quickly and with fault tolerance. Spark has proven very popular and is used by many large companies for huge, multi petabyte data storage and analysis. This has partly been because of its speed.
Additionally, Spark has proven itself to be highly suited to Machine Learning applications. Machine Learning is one of the fastest growing and most exciting areas of computer science, where computers are being taught to spot patterns in data, and adapt their behavior based on automated modeling and analysis of whatever task they are trying to perform.
Spark uses cluster computing for its computational (analytics) power as well as its storage. This means it can use resources from many computer processors linked together for its analytics. With distributed storage, the huge datasets gathered for Big Data analysis can be stored across many smaller individual physical hard discs.