INSTRUCTOR LED ONLINE LIVE SESSIONS

Hadoop MapReduce

Course Brief

How big is BIG? Become a big data expert through an intensive training program customised across various levels designed specifically for you. It will make participants solve real-time problems with huge datasets.Through this intensive program we aim to train the participants in a way that they are prepared to appear for International Certifications. The main aim of this course is to introduce hadoop and map-reduce with hands-on exercises. It is to make the student familiar with hadoop and map-reduce environment. Successful completion of this course will provide a platform to clear HDP Certified Developer: Java (HDPCD: Java), an internationally acclaimed certification program.

    Introduction Data, Storage, Bigdata, Distributed environment, Hadoop introduction History, Environment, Benefits - Subprojects HDFS, Map-Reduce, PIG, Hbase, Hive, Zoo-Keeper, SQOOP, Mahout, MongoDB, Hadoop DB.

    Learning Outcomes:

    • Understand big data, challenges, distributed environment
    • Know hadoop and sub projects

    Hadoop Architecture : Overall Architecture-NameNode - Datanode Fault Tolerance - Read&Write operations - Interfaces(Command line interface, JSP, API) - HDFS Shell - FS Shell Commands - Java API Programs.

    Learning Outcomes:

    • Acquire knowledge of HDFS components , Namenode, Datanode
    • Acquire knowledge of storing and maintaining data in cluster, reading and writing data to/from cluster
    • Be able to maintain files in HDFS
    • Be able to access data from HDFS through java program

    Map-Reduce Introduction - Map-Reduce Architecture - Yarn Architecture - Basic M-R Programs - Detailed description of M-R Methods and exercises.

    Learning Outcomes:

    • Understand Map-Reduce paradigm and Yarn Architecture
    • Analyze a given problem in map-reduce pattern
    • Be able to write Basic Map-Reduce Programs

    Rkey/value pairs - Different types of values from a mapper - GenericWritable - Custom values from mapper - Writable - Custom keys from Mapper - WritableComparable - Exercises.

    Learning Outcomes:

    • Understand the key-value pairs from map to reduce
    • Be able to design applications with custom value types
    • Be able to design applications with custom key types
    • Applications with Generic writable

    Input format - FileInputFormat - Steps for Input - RecordReader - Custom FileInputFormat - Custom RecordReader - Exercise Output format - FileOutputFormat - RecordWriter - Custom FileOutputFormat -Custom RecordWriter.

    Learning Outcomes:

    • Understand the input and output formats of map-reduce application
    • Be able to read different formats of files into map-reduce application
    • Be able to produce different formats of files from map-reduce application

    Joins- various types - Reduce Side joins - Distributed Cache - Map-Side Join - Exercises.

    Learning Outcomes:

    • Be able to take data from multiple data sets and join them
    • Be able to implement various joins in Map-Reduce
    • Be able to design applications with map-side joins
    • Be able to design application with reduce side join
    • Be able to use distributed cache

Mr. P.V.N.Balarama Murthy
Hadoop Map Reduce and Hadoop Ecosystem

Mr. P.V.N.Balarama Murthy, is an M.Tech(CSE) having over 10 years of teaching and technical training experience. He is specialist in Data Science and Bigdata. He has experience in deploying hadoop clusters. As technical trainer, he has trained a number of people in C,C++, Java, Oracle, Hadoop (Administration, Development with MR, PIG, Hive, Flume, Sqoop) and Data Science with R. He has guided to his credit 15+ students to get Hortonworks certifications for Hadoop.

A dedicated, resourceful and result oriented instructor that he is, it is helping shape up careers of students.

Ms. Jyothi SanjeevaMani
Hadoop Map Reduce

Ms. Jyothi SanjeevaMani has over 15 years of satisfying teaching and technical training experience. She is a Research Scholar of Big Data Analytics from a reputed university. As a technical trainer she trained many students in industry oriented subjects like C, C++, Java, MySQL, Oracle (SQL, PL/SQL), Python, Linux, Openstack, BigData - Hadoop(MapReduce, Pig, Hive, Sqoop, Flume), Data Science with both Python and R.

She is an Asst.Professor with the Department of IT at The Keshav Memorial Institute of Technology (KMIT).

She is a dedicated, resourceful and a result oriented instructor, who strives to help students change marginal grades into good grades.

  • Are there any pre requisites for this course?

    Basic knowledge of Java and Mysql will help.

  • Can I just enroll in a single course? I'm not interested in the entire Specialization?

    No you cannot enroll for individual skill sets within a defined course on teleuniv.

  • How long does it take to complete this Specialization?

    Most learners are able to complete the Specialization in about 2 months.

  • Do I need to take the courses in a specific order?

    We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.

  • What will I be able to do upon completing this Specialization?

    Upon completion as a Big Data and Analytics you will be able to help a company with the following:

    • Cost reduction

      Big data technologies like Hadoop and cloud-based analytics provide substantial cost advantages.

    • Faster, better decision making
    • Analytics has always involved attempts to improve decision making, and big data doesn't change that. Large organizations are seeking both faster and better decisions with big data, and they're finding them. Driven by the speed of Hadoop and in-memory analytics, several companies are now focused on speeding up existing decisions.

    • New products and services

      Perhaps the most interesting use of big data analytics is to create new products and services for customers. Online companies have done this for a decade or so, but now predominantly offline firms are doing it too.

  • Can I attend a demo session?

    We have limited number of participants in a live session to maintain the Quality Standards, hence, participation in a live class without enrollment is not possible. However, we can create a demo login for one demo session.

  • What are the payment options?

    You can pay by Credit Card, Debit Card or Net Banking from all the leading banks. We use a Payment Gateway.

  • Do you provide placement assistance?

    Teleuniv is associated with Keshav Memorial Institute of Technology, one among the top performing colleges in Hyderabad and hence lot of recruitment firms contacts us for our students profiles from time to time. Since there is a big demand for this skill, we help our certified students get connected to prospective employers. Having said that, please understand that we don't guarantee any placements however if you go through the course diligently and complete the assignments and exercises you will have a very good chance of getting a job.

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