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Why Hadoop?

With today’s powerful hardware, distribution capabilities, visualization tools, containerization concepts, cloud storage and computing capabilities, huge amounts of raw data can be stored, processed, analyzed, and converted into information, used for decision making, historical analysis and for future trend prediction.

Understanding Big data and converting into knowledge is the most powerful thing any entity can possess today. To achieve this, Hadoop is currently the most used data management platform. The main benefits of Hadoop are:

  1. Highly scalable
  2. Cost-effective
  3. Fault-tolerant
  4. Easy to process
  5. Open Source
  1. What is Hadoop?

Hadoop is a Highly distributed file system (HDFS), maintained by Apache Software Foundation. It is a software to store raw data, process it by leveraging the distributed computing capability and to manipulate and filter it for further analysis.

Several frameworks and machine learning libraries like python and Operate on the processed data to analyze and make predictions out of it. It is a horizontally scalable, largely distributed, clustered, highly available, and reliable framework to store and process unstructured data.

Hadoop consists of the file storage system (HDFS), a parallel batch processing engine Map Reduce and a resource management layer, YARN as standalone components. Open source software like Pig, Flume, Drill, Storm,Spark, Tez, Hive, Kafka, HBase, Mahoot, Zepplin etc. can be integrated on top of the Hadoop ecosystem to achieve the intended purpose.

How to Learn Hadoop?

With interest in Big Data growing day by day, learning it can help propel your career in development. There are several Big data Hadoop training courses and resources available online which can be used to master Hadoop theoretically. However,mastery requires years of experience, practice, availability of large hardware resources and exposure to differently dimension ed software projects. Below area few ways to speed up learning Big Data.

  1. Join a course: There are several Big Data and Hadoop training courses available from a developer, architect, and administrator perspective. Hadoop customization like MapR, Horton Works, Cloud era etc. offer their own certifications.
  2. Learning marketplaces: Virtual classrooms and courses are available in Course Era, Udemy, Audacity etc. They are created by the best minds in the Big Data profession and are available at a nominal price.
  3. Start your own POC: Start practice with a single node cluster on a downloaded VM. Example: Cloud Era.com quick start.
  4. Books and Tutorials on the Hadoop ecosystem: Hadoop.apache.org, Data Science for Business, edurekha,digital vidya, are a few examples apart from the gazillion online tutorials and videos.
  5. Join the community: Joining the big data community, taking part in discussions and contributing back is a surefire way to increase your expertise in big data.

Points to remember why Learning Hadoop:

Below are the things to keep in mind while working on large open source Big Data projects like Hadoop:

  1. It can be overwhelming and frustrating: There will always be someone wiser and more adept than you are.Compete only with yourself.
  2. Software changes: The ecosystem keeps shifting to keep up with new technology and market needs. Keeping abreast is a continuous process.
  3. Always Optimize: Keep finding ways to increase the performance, maturity, reliability, scalability, and usability of your product. Try making it domain agnostic.
  4. Have Fun: Enjoy what you are doing, and the rest will come automatically!

All the Best on your foray into the digital jungle!

For Online Course Enquiries
About Imarticus
Imarticus Learning is India’s leading professional education institute that offers training in Financial Services, Data Analytics & Technology. We’ve successfully transformed careers of over 35,000+ individuals globally through our Certification, Prodegree, and Post Graduate programs offered in association with leading and renowned global organisations in the Financial Services, Data Analytics & Technology domain.
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