When working with Hadoop Framework to process massive data, we will face many challanges and difficulties which includes the Input/Output(I/O) and network related bottlenecks. To overcome this problem, one needs to understand the different data compression techniques available in Hadoop Framework.
YARN stands for Yet Another Resource Negotiator. It is a centralized cluster resource management and job scheduling platform to deliver scalable operations across the cluster. It was introduced in Hadoop…
There are 3 types of joins, Reduce-Side joins, Map-Side joins, and memory-backed Joins that can be used to join Tables in MapReduce. Map Side Join Joining at the map side…
This post describes some of the most useful Apache Hadoop HDFS commands one would need when working in a Hadoop Cluster
Hadoop Mapreduce framework is a Big data processing framework which consists of MapReduce programming model and Hadoop Distributed File System.
Apache Hadoop can be used in multiple modes to achieve a different set of tasks. There are three modes in which a Hadoop Mapreduce application can be executed.
HDFS is a distributed file system that is designed for storing very large files with streaming data access patterns running on clusters of commodity hardware.
What exactly is Apache Hadoop? Apache Hadoop is an open-source distributed processing framework that is used to store and process large datasets whose size ranges from gigabytes to petabytes of…