In the current Data Analytics market, there is a lot of buzz going around Apache Spark. Most of the business experts are labelling Spark on top of Hadoop. If you are in to the Big Data Analytics business or ambitious of entering the market in the coming days, then you should probably know – to what extent does Spark rules over Hadoop? This article endeavours to help you in locating answers to some of your latent questions. Before shedding key focus on Spark vs Hadoop issues, let us initially discuss what Spark and Hadoop are.
Apache Spark and Hadoop, both are the Big Data frameworks, that offers different tools to performs Big Data related tasks, but not accurately the same tasks.
Originally developed in UC Berkeley’s AMPLab, and later distributed as an open-source Project, Apache Spark is a powerful processing engine for Big Data. It is a framework for performing data analytics, which provides faster and more general data processing platform.
Apache Hadoop –
On the other hand, Hadoop is a distributed data infrastructure, which distributes huge data collections across several nodes within the cluster of commodity servers. It further keeps a record of that data, enabling big data processing and analytics more effective. Hadoop is largely considered as the general-purpose framework that supports multiple models.
Hadoop, for many years was traditionally used to run the Map/Reduce jobs, which usually are the long running jobs. To accelerate the process, Spark has been designed to run on top of Hadoop cluster for real-time stream data processing and fast interactive queries that can be completed in a fraction of seconds.