What is Hadoop and its Connectivity with Big Data?

Hadoop has grown a common term and has found its influence in today’s digital world. When anyone can make massive quantities of data with just one click, the Hadoop framework is necessary. Have you ever questioned what Hadoop is and what all the excitement is about? 

What is Hadoop?

Hadoop is an open-source framework from Apache and is applied to store processes and interpret huge volumes of data. Hadoop is composed in Java and is not online analytical processing. 

It is practiced for batch/offline processing. It is being used by Facebook, Yahoo, Google, Twitter, LinkedIn, and several more. Further, we can scale it up just by computing nodes in the cluster.

Hadoop was co-founded by Doug Cutting and Mike Cafarella at the beginning 2000s as a sub-project and was based on Google’s Google File System Whitepaper.  

It was formed at Yahoo before becoming an Apache open source project. By 2009 moved on to grow the fastest system to order terabyte data in 209 seconds, practicing a 910-node cluster. 

The main problem was managing the heterogeneity of data, i.e., structured, semi-structured, and unstructured. 

The RDBMS concentrates principally on structured data like business transactions, operational data, etc., and Hadoop concentrates on semi-structured, unstructured data, same text, videos, audios, Facebook posts, logs, etc. 

RDBMS technology is a proven, extremely logical, matured system recommended by numerous companies.

Growth of Big Data

Back in the day, there was insufficient data generation. Hence, collecting and processing data was done with a separate storage unit and a processor each. 

In the blink of an eye, data production progressed by leaps and bounds. Not only did it grow in volume but also it’s quality. Therefore, a single processor was inadequate for processing high volumes of many varieties of data. Speaking of types of data, you can have structured, semi-structured and unstructured data. 

Modules of Hadoop

1. HDFS
2. Yarn
3. Map Reduce
4. Map Reduce

Hadoop Architecture

The Hadoop architecture combines the file system, MapReduce engine, and the Hadoop Distributed File System. The MapReduce engine can be MapReduce/MR1 or YARN/MR2.

A Hadoop cluster consists of a private master and various slave nodes. The control node comprises Job Tracker, Task Tracker, NameNode, and DataNode, whereas the slave node comprises DataNode and TaskTracker.

Hadoop Challenges

The various important challenge to achieving a Hadoop cluster is that it needs a vital learning curve associated with building, operating, and managing the cluster. 

A bulk of enterprise data is structured so that storage and retrieval would be more comfortable in an RDBMS system, whereas preparing the same thing in Hadoop is a difficulty. 

Hadoop is extremely configurable, which makes optimization for more reliable performance a key difficulty. Hadoop needs a highly specific skill placed to manage.  Additionally, because it is an open-source project, there is no standard support channel.