The Reasons for the Rise of Apache Spark

The data processing engine called Apache Spark has been on everybody’s lips in the world of Big Data analytics. The engine has received rave reviews for being a step ahead of almost all its predecessors. The primary features of Apache Spark are its highly sophisticated analytics, speed and ease of use.

Apache Spark is a highly capable processing engine, being able to perform a whole host of new processes in the field of analytics such as interactive queries, iterative algorithms and streaming. Its rise is not just down to pure chance. The engine’s advantages speak for themselves and hence more and more professionals can be seen opting for Big Data training courses.

Ease of Use

Apache Spark is an easy-to-use engine, and the fact that it supports multiple languages makes it a favorite for developers from a wide range of platforms such as Java, Python and Scala. The engine features more than 80 operators, all built in! Both data scientists and developers in the Big Data business have been benefited by Apache Spark.

Speed

Certification in Big Data analytics requires high-speed processing engines and that is exactly what Apache Spark is. Based on the Resilient Distributed Dataset (RDD) concept, Spark features in-memory storage of intermediate processing data, which greatly reduces disc read/write processes. Slower processing engines do not have in-memory storage capabilities, and the increased disc read/write processes result in slower data processing.

Reducing the Development Lifecycle

Apache Spark’s increased speed is not just for show. Big Data development cycles can be hours long, thanks to continuous processes of developing, testing and debugging. A slow processing engine can severely impact this cycle. However, Spark’s exceptional processing speed reduces the time taken to complete the processes of the development cycle significantly.

Integration with Hadoop

Apart from running independently, Apache Spark can be run on the YARN cluster manager, part of Hadoop 2. It can also read existing Hadoop data without any hassles. Spark supports HDFS, HBase and other Hadoop data sources, which enables pure Hadoop data migration.

Real Time Streaming

Handling and processing stored data are fundamental features of Spark. The engine also allows real time data manipulation thanks to Spark Streaming. Spark Streaming has received rave reviews for being easy to use and capable of recovering from situations of lost work. Developing streaming applications is much faster with Spark Streaming, and its integrated framework has been hailed by developers as well.

Graph Processing

Graph processing is a major tool in Big Data analytics and can be used to analyze important aspects such as advertising and social data. Many of the advances in data mining and machine learning can be attributed to this particular capability of Apache Spark.

Sophisticated Analytics Support

Apache Spark’s predecessors were mostly capable of simple operations such as ‘map’ and ‘reduce’. But Spark supports several complex analytics operations such as machine learning, streaming data, SQL queries and graph algorithms. Combining these operations in one workflow is a boon to Big Data businesses.

Singular System

All of Apache Spark’s capabilities make it perfect to be the ultimate system for real time data storage and streaming, as well as data analysis and manipulation. Before Apache Spark came to life, most companies relied on two separate systems to manage all these tasks. As a result, a lot of company resources were stretched trying to facilitate effective deployment, development and maintenance. Spark allows simultaneous implementation of both data batch and stream processing.

Active Community

Apache Spark boasts of one of the most active programmer communities. As of September 2015, more than 600,000 lines of code were written in the Apache Spark platform. Since 2012, the platform has seen a steady rise of contributors as well, with more than 130 contributors in calculated in 2015.

Big Data certification is becoming big, and much of it is down to Apache Spark, a platform on the rise and never looking down.