Hadoop training and placement
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Hadoop training and placement

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ou can attend 1st 2 classes or 3 hours for free. once you like the classes then you can go for registration. ...

ou can attend 1st 2 classes or 3 hours for free. once you like the classes then you can go for registration.

or full course details please visit our website www.hadooponlinetraining.net

Duration for course is 30 days or 45 hours and special care will be taken. It is a one to one training with hands on experience.

* Resume preparation and Interview assistance will be provided.
For any further details please contact +91-9052666559 or
visit www.magnifictraining.com

please mail us all queries to info@magnifictraining.com

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Hadoop training and placement Hadoop training and placement Presentation Transcript

  • hadoop training and placement Course Objective Summary *During this course, you will learn *Introduction to Big Data and Hadoop. *Hadoop ecosystem – Concepts. * Hadoop Map-features reduce concepts and
  • *hadoop training and placement • CONTACT US: • Call :+91-9052666559 • Mail :info@magnifictraining.com • Visit : www.hadooponlinetraining.net
  • hadoop training and placement • ADpevpelilcoaptiinogn st he map-reduce • Pig concepts • Hive concepts • Oozie workflow concepts • HBASE Concepts • Real Life Use Cases
  • hadoop training and placement • Introduction to Big Data and • Hadoop • What is Big Data? • bWigh adta atare? • the challenges for processing • What technologies support big data? • What is Hadoop? • Why Hadoop?
  • hadoop training and placement • Use Cases of Hadoop • Histiry of Hadoop • Hadoop eco systems • Hdfs • Map reduce • Statistics
  • hadoop training and placement • Understanding the Cluster • Typical workflow • Writing files to HDFS • Reading files from HDFS • Rack Awareness
  • hadoop training and placement • Let's talk Map Reduce • Before Map reduce • Map Reduce Overview • Word Count Problem • Word Count Flow and Solution • Map Reduce Flow • pArlgoobrleitmhms s
  • hadoop training and placement • Developing the Map Reduce • Application • Data Types • File Formats