Computers and Technology

8 Advantages of Big Data Hadoop


Big data is a cluster of large datasets that cannot be fixed by using standard computing techniques. It is free to use a framework that introduced a new way for the allocated processing of big business data collections. It is a complete subject that contains various tools, techniques, and structures. Organizations are noticing that organizing and interpreting Big Data can assist in making crucial business predictions. Apart from this if you are willing to know more about this course then join Big Data Hadoop Online Training from different websites for better understanding.

Advantages of Big Data 

Open Source-: It is free to learn Java-based programming structure. Open-source means it is freely obtainable precisely you can modify its source code according to your requirements.

Fault Tolerance-: Hadoop controls responsibilities by dummy creation of the operation. The Hadoop framework splits the file into blocks when the customer keeps a file in HDFS. HDFS creates 3 duplicates of blocks on another machine to present the new group. If any machine in the group fails due to some unfavorable environment. Although we can efficiently access that information from different machines.

Allocated Processing-: Hadoop stores a huge quantity of data in an allocated method. Process the data similarly on a set of nodes.

Scalability-: Hadoop is easy to use which makes it a completely scalable medium. Hadoop supplies flat scalability so an unknown node is added on the fly model to the technique. In Apache Hadoop, applications operate on more than thousands of nodes.

Reliability-: Data is stored on the set of machines despite machine collapse due to the synchronization of data. So, if any of the nodes collapse, still we can store data reliably.

High Availability-:  Due to the number of copies of data, data is admiringly functional and affordable despite hardware collapse. Any machine that goes down data can recover from a different path.

Economic-: Hadoop is not very costly as it runs on a group of entity hardware. As we are operating low-cost item hardware, we don’t require you to consume a large amount of money scaling out your Hadoop collection.

Data Locality-: It guides to the capability to carry the analysis immediately to where real data resides on the node. Rather than moving data to computation. This lowers the network congestion and improves the excess output of the technique.

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Job Roles for Hadoop Developers

Getting knowledge of Hadoop is not specified to a single job role but you can make a profession with different job designations in the Hadoop specialization.

  • Developer
  • Administrator
  • Architect
  • Engineer
  • Lead Developer
  • Data Scientist
  • Hadoop Tester

Skills Needed for Hadoop Developers

Hadoop developers generally consist of few developing skills. If candidates have all or some of these skills and fulfill the Hadoop developer job characterization, then they will consider as Hadoop developers. Here are the skills demanded the Hadoop designer position –

  • Expertise understanding of Hadoop, Hive, HBase, and Pig
  • Working knowledge in HQL
  • Knowledge in writing Pig Latin Scripts and MapReduce assignments
  • Hands-on experience in backend programming, especially Java, JavaScript, and OOAD.
  • Adequate knowledge of the ideas of multi-threading and concurrency
  • Analytical and problem-solving aptitudes; the execution of these aptitudes in the Big Data specialization
  • Understanding of data loading devices such as Flume, Sqoop, etc.
  • Good knowledge of database codes, rules, facilities, and approaches
  • Familiarity with the schedulers


In this article, we have discussed the advantages of big data Hadoop and the skills needed for Hadoop developers. Hadoop is playing a significant role in modern-day life. If you’re looking to enhance your career in this field then join Big Data Hadoop Training in Delhi to enhance your skills and knowledge.

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