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Hadoop Admin Training

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BTM Layout 1st Stage, Bangalore

Course ID: 29602

BTM Layout 1st Stage, Bangalore

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About the Course

PrwaTech invites you to enroll in our Hadoop Admin Training and become a first rate Hadoop Administrator. This training Course is designed by professionals to provide practical skills and knowledge to become a successful Hadoop Administrator. Learn from experts, get all your queries answered and enjoy 24/7 support and get unlimited access to tutorials.


About The Training:


The Hadoop Admin training course is designed to offer knowledge and skills which will make you a successful Hadoop Architect. Our training program starts with elementary concepts of Hadoop Cluster and Apache Hadoop. The training program covers topics to configure, manage, deploy, secure and monitor a Hadoop Cluster. Training program includes various challenging and practical exercises for trainees.


Training Highlights:



  • Role Specific Training

  • Hands-on Exercises

  • Introduction to BIG DATA and HADOOP

  • Resume preparation, Interview question answers are also provided.

  • Covers all significant Hadoop system products


Benefits of Hadoop Admin training:



  • Unlimited access to Training material

  • Assignments for every training session

  • Excellent Course Material and E-books

  • Excellent Lab Material

  • 24/7 contact with trainers for doubt clarification

  • Certificate of Completion at completion


After the completion of Hadoop Admin Training course at PrwaTech, you will have the knowledge of:



  • Apache Hadoop

  • Hadoop Cluster

  • Hadoop Administration

  • Insight on Hadoop 2.0

  • HDFS Federation

  • Name Node High Availability

  • MapReduce v2

  • YARN

  • Hadoop Cluster Installation

  • Load Data and Run applications

  • Troubleshoot Hadoop Cluster

  • Backup and Recovery

  • Hcatalog/Hive Oozie

  • HBase Administration


Who should go for this training?


Systems administrators, linux administrators, windows administrators, Infrastructure engineers, Big Data Architects, DB Administrators, IT managers and Mainframe Professionals.


Pre-requisites for this training.


This course requires no prior knowledge of Java, Hadoop Cluster Administration or Apache Hadoop. Fundamental knowledge of Linux basics is necessary as Hadoop runs on Linux.

Date and Time

Not decided yet.

About the Trainer

4.2 Avg Rating

5 Reviews

8 Students

9 Courses

Work Experience:
Software professional, worked on enterprise, web, JAVA, J2EE,mobile applications.. Now focusing on Hadoop, BigData.5+ Year Experience in Corporate Training.
Freelance Consulting on BIGDATA & HADOOP DEVELOPER / ADMIN, Spark.

Given talks at various meetup.Very proactive in learning and leveraging emerging technologies. Mentored engineers; believe in leading by doing.
Hadoop / BigData / Spark & Scala:
Consulting on Hadoop ecosystem : Hadoop, MapReduce, Pig,Hive, Sqoop, Flume, Spark & Scala
Managed Hadoop clusters : setup, install, monitor, maintain; Distributions : Cloudera CDH
Hadoop tutorials &howtos :
Teach Hadoop courses for individuals and corporate groups .

Responsibilities :
Written MapReduce code to process all the sequence files to extract the useful
Information from log files (like Host name, platform, operating system etc.).
Writing serdes and used complex data type in Hive.
Extensively involved in query optimization in Hive query language.
Extensively used pig script, complex type, grouping, flattening etc.
Maintained Hadoop clusters for dev/production.

Created the customized Map Reduce Input/output formatted and used extensively.
Highly involved in designed the next generation data architecture for the unstructured data.
Performance tuning of Hadoop clusters routines against very large data sets.
Assisted with the addition of Hadoop processing to the IT infrastructure.
Depending on this information the HBase rules will be applied on the log files, to get the required information in a structural manner (MapReduce output) used hive for storing the MapReduce output into tables.
Used Sqoop and Flume for importing the data into the HDFS.
Reviewed the HDFS usage and system design for future scalability.
Developed applications on MapReduce programming Model of Hadoop in java.

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