The term Big Data has created a lot of hype already in the business world. Chief managers know that their marketing strategies are most likely to yield successful results when planned around big data analytics. For simple reasons, use of big data analytics helps improve business intelligence, boost lead generation efforts, provide personalized experiences to customers and turn them into loyal ones. However, it’s a challenging task to make sense of vast amounts of data that exists in multi-structured formats like images, videos, weblogs, sensor data, etc.
In order to store, process and analyze terabytes and even petabytes of such information, one needs to put into use big data frameworks. In this blog, I am offering an insight and analogy between two such very popular big data technologies – Apache Hadoop and Apache Spark.
Let’s First Understand What Hadoop and Spark are?
Hadoop: Hadoop, an Apache.org. Project, was the first big data framework to become popular in the open source community. Being both a software library and a big data framework, Hadoop paves the way for distributed storage and processing of large datasets across computer clusters using simple programming models. Hadoop is a framework composed of modules that allow automated handling of common hardware failure occurrences…read full blog here- Apache Hadoop vs Apache Spark: Two Popular Big Data Frameworks Compared