Online Hadoop Training | Best Hadoop Online Training in Hyderabad

Introduction to Hadoop 

Hadoop builds on a massive file system (Google File System or GFS) and a parallel application model (Map Reduce) originally developed at Google. Google has an unbelievable number of servers compared to typical large enterprises (in all likelihood more than a million). Search is a relatively easy task to parallelize: many search requests can be run in parallel because they only have to be loosely synchronized (the same search done at the same time doesn’t have to get exactly the same response).Hadoop Online Training In Hyderabad

GFS was developed as a file system for applications running at this scale. Map Reduce was developed as a means of performing data analysis using these resources.Hadoop Online Training

Hadoop is an Open Source re implementation of GFS and Map Reduce  Google’s systems run a unique and proprietary software “stack” so no one else could run Google’s MapReduce even if Google permitted it. Hadoop is designed to run on a  conventional LINUX stack.    Google  has  encouraged the  development of Hadoop, recognizing the value in a broader population of people trained in the methodology and tools. Much of the development of Hadoop has been driven by Yahoo!. Yahoo! is also a large Hadoop user, internally running a cluster of more than 40,000 servers.online hadoop training

Operationally we talk about a Hadoop “cluster”: a set of servers dedicated to a particular instance of Hadoop that may consist of just a few to the clusters of more than 4,000 servers in use at Yahoo!.Hadoop Online Training

Today a typical Hadoop server might be two sockets, a total of 8 cores (two 4- core servers), 48 GB of DRAM, and 8-16 directly attached disks, typically cost- per-byte optimized (e.g., 2 or 3 TB 3.5” SATA drives). When implemented with high-volume commodity technology, the majority of the server cost is the disk drive complement, and each server will have 20-50 TB of storageHadoop Online Training.