4 Prerequisites for a Hadoop Hopeful
By Afia Ahmad
In the past, a huge volume of data was produced without storage systems large enough to hold enormous data. It took months or years to process the data that managed to get stored. As a result, the value of this data was lost.
However, in the early 2000s, Big Data analysts became popular after harvesting insights into this large set of data. But how did all these analysts provide insights without any framework for storing such data? Hadoop was the solution to all the storage problems when it started in 2006 as an Apache Nutch project.
Hadoop is an open source, Java-written program responsible for storing and processing large volumes of data with inexpensive commodity hardware. Each module performs certain tasks designed for computer systems using Big Data analytics.
4 prerequisites for successful functioning of Hadoop
Hadoop has become such a dominant data analytics platform that learning it would be vital to understand your business. However, before understanding the requirements for Hadoop, you need to have some basic knowledge of programming language (Java) and Operating System (Linux). If not, you can still give it a shot. Here are the following 4 necessities for Hadoop:-
1) Hadoop Distributed File System (HDFS)
A file system is generally named depending on a computer’s operating system. However, Hadoop uses its own file system so any computer running any supporting operating system can access it.
Hadoop is a distributed file system, which means that data is distributed across thousands of nodes making computation much faster. It is based on the Google File System (GFS).The HDFS component splits data into several blocks and then runs them across a cluster of computers for reliable and quick data access. Each HDFS cluster contains a Name Node, Data Node and Client Machine. Name Node and Data Node work on the Master-Slave Architecture Model. To learn more about these terms, check out Hadoop tutorials online.
HDFS lets you dump all your data and it will remain idle unless you leverage it for analysis. It can be performed either within Hadoop or after exporting it to another tool. A career in Hadoop is a lucrative option. Here's the proof -
This is the tool that actually gets the data processed. It falls under two tasks - Map and Reduce. The task of Map is to take input data and convert it into another set of data, where individual elements are broken into tuples (key/value pairs). The task of Reduce is to then take the output data it receives from Map and combine these data tuples into a smaller set of tuples.
In simple terms, MapReduce is responsible for placing all the data in a format (Map) and performing mathematical operations in a database (Reduce). For all the Hadoop Hopeful, here's some stats to tell you that you are on the right path.
3) Yet Another Resource Negotiator (YARN)
YARN has unlocked a new approach to analysis, enhancing a Hadoop computer cluster. It sets up the Global and application-specific resource management components and acts as a central platform to deliver consistent operations, security and data governance tools. Businesses use Hadoop YARN to track customer information, view supply chains and take care of other automated business processes.
4) Hadoop Common
It is the collection of commonly used Java libraries and utilities that support other Hadoop modules. It is considered as the core of Hadoop Framework since it involves the use of HDFS, MapReduce and YARN.
Today, many search engines (Google, Yahoo), social networking sites (Facebook, Twitter, Instagram, LinkedIn), retail sites (Amazon, eBay) and companies from various sectors are using Hadoop to make strategic business decisions with accurate Big Data analysis. If you’re keen on learning more or looking for a job in this game changing field, check out our Hadoop Training courses online, including Hadoop Certificate Training.