Apache Hadoop VS Apache Spark

Apache Hadoop VS Apache Spark

Apache Hadoop VS Apache Spark : What is Big Data?

Apache Hadoop VS Apache Spark :  What size of Data is considered to be big and will be termed as Big Data?  We have many relative assumptions for the term Big Data. It is possible that, the amount of data say 50 terabytes can be considered as Big Datafor Start-up’s but it may not be Big Data for the companies like Google and Facebook. It is because they have infrastructure to store and process this vast amount of data.

When data itself becomes big is Big Data.

What is this Apache Hadoop and Apache Spark?

What made IT professional to talk about these buzz words and why the demand for Data Analytics and Data Scientists are growing exponentially?

Both Big Data frameworks – they provide some of the most popular tools used to carry out common Big Data-related tasks.

Understanding Hadoop:

Apache Hadoop is an open-source software framework designed to scale up from single servers to thousands of machines and run applications on clusters of commodity hardware. Hadoop does a lot of things really well.  It has been evolving and gradually maturing with new features and capabilities to make it easier to setup and use. There is a large ecosystem of applications that now leverage Hadoop.  Hadoop framework is divided into two layers. First layer is storage layer and known as Hadoop Distributed File System (HDFS) while second layer is the processing layer and known as MapReduce. Storage layer of Hadoop i.e. HDFS is responsible for storing data while MapReduce is responsible for processing data in Hadoop Cluster.