Hadoop Job Roles & Benefits — Latest updated (2020)
Introduction:
Big Data and Hadoop skill could mean the difference between having your dream career and getting left behind. Dice has quoted, “Technology professionals should be volunteering for Big Data projects, which makes them more valuable to their current employer and more marketable to other employers.”
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
The role of the Hadoop is one such trending job role in the Big Data domain. The following should give you an idea of the kind of technical roles in Hadoop.
Hadoop Job Roles:
· Big data professionals can analyze all data & create useful information are highly sought after by companies across the world. There are different job titles and opportunities available for Hadoop Developers.
· Above Image is list of job titles, which will help you making the right decision by assisting you in choosing the desired job role as a Hadoop expert.
· It is not just the tech companies that are offering Hadoop jobs but all types of companies including financial firms, retail organizations, banks and healthcare organizations.
· Big Data/ Hadoop Development is one of the best career choices for every individual where you can get many job opportunities and healthy career growth.
Big Data Job roles given below for experienced based,
Benefits of Hadoop:
Advantages of Hadoop:
1. Scalable
Hadoop is a highly scalable storage platform, because it can stores and distribute very large data sets across hundreds of inexpensive servers that operate in parallel. Unlike traditional relational database systems (RDBMS) that can’t scale to process large amounts of data, Hadoop enables businesses to run applications on thousands of nodes involving many thousands of terabytes of data.
2. Cost effective
Hadoop also offers a cost effective storage solution for businesses’ exploding data sets. The problem with traditional relational database management systems is that it is extremely cost prohibitive to scale to such a degree in order to process such massive volumes of data. In an effort to reduce costs, many companies in the past would have had to down-sample data and classify it based on certain assumptions as to which data was the most valuable. The raw data would be deleted, as it would be too cost-prohibitive to keep. While this approach may have worked in the short term, this meant that when business priorities changed, the complete raw data set was not available, as it was too expensive to store.
3. Flexible
Hadoop enables businesses to easily access new data sources and tap into different types of data (both structured and unstructured) to generate value from that data. This means businesses can use Hadoop to derive valuable business insights from data sources such as social media, email conversations. Hadoop can be used for a wide variety of purposes, such as log processing, recommendation systems, data warehousing, market campaign analysis and fraud detection.
4. Fast
Hadoop’s unique storage method is based on a distributed file system that basically ‘maps’ data wherever it is located on a cluster. The tools for data processing are often on the same servers where the data is located, resulting in much faster data processing. If you’re dealing with large volumes of unstructured data, Hadoop is able to efficiently process terabytes of data in just minutes, and petabytes in hours.
5. Resilient to failure
A key advantage of using Hadoop is its fault tolerance. When data is sent to an individual node, that data is also replicated to other nodes in the cluster, which means that in the event of failure, there is another copy available for use.
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