The global big data technology market is estimated to touch USD 116.07 billion by 2027. This is a huge jump from USD 41.33 billion in 2019, indicating a CAGR of 14% in the forecast period. Big data technology is growing at a rapid pace, and so is its adoption by businesses. As a result, the big data job market is brimming with opportunities, especially for talents having a niche. One such niche that a majority of job seekers are picking is to learn Hadoop and master it.
The framework is a bit complex to learn and challenging to work upon. However, with the right kind of guidance, you can master it.
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This article answers trending questions like what are the prerequisites for Hadoop, who should learn Hadoop, what are the essential Hadoop tools, career prospects after learning Hadoop, and enlightens the use cases.
Why Learn Hadoop?
Hadoop is a revolutionary technology that has brought transformations in the procedures of compiling and analyzing vast data stores collected by businesses over time. It stands for ‘High Availability Distributed Object Oriented Platform’.
An open-source software framework, Hadoop, stores data and processes extremely large datasets in a distributed computing environment. It offers its developers higher availability of data by providing distribution of object-oriented tasks, this is done by allowing parallel processing over multiple clustered computers.
The Hadoop ecosystem is designed to scale linearly from a single computer to numerous machines in the cluster, with each system granting local storage and computation.
- Implicitly Hadoop is used for data mining, log analysis, image processing, ETL (extract-transform-load), and network monitoring for data in gigabytes, terabytes, or petabytes from any location.
- Hadoop lets businesses seek answers to intricate questions providing comprehension for business operations, and bringing new product intuitions and recommendations.
The all-inclusive Hadoop library is capable of detecting and handling failures at the local level minimizing the risks of failure. It is an integrated ecosystem of Big Data tools and technologies which is increasingly being deployed for storing and parsing Big Data.
- Hadoop permits scalability in parallel jobs to execute from single to numerous servers without delay.
- It provides a distributed file system increasing data accessibility and file transfer over different nodes in reduced time.
- Node failure does not impact the computation.
Also read: Top 3 reasons to start with a big data hadoop certification course
Hadoop: History
Hadoop was named after a yellow elephant toy by Doug Cutting, who was working with Yahoo at that time. Doug’s then-toddler son called his stuffed elephant toy as ‘Hadoop’ when Doug was building Hadoop.
Cutting and Mike Cafarella aimed at developing faster search results by using distributed data and calculations over different computers to allow multiple tasking.
Hadoop was initially a part of the Nutch search engine project which later separated as a distributed computing and processing unit from the large web crawler project.
Hadoop was released by Yahoo as an open-source project in 2008. It is maintained by a non-profit, global community for software developers and contributors Apache Software Foundation.