Hadoop Developer


Course Id: 1012

[wptab name=’About’]

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop is composed of four core components—Hadoop Common, Hadoop Distributed File System (HDFS), MapReduce and YARN.

Hadoop Common

A module containing the utilities that support the other Hadoop components.


A framework for writing applications that process large amounts of structured and unstructured data in parallel across a cluster of thousands of machines, in a reliable, fault-tolerant manner.


A file system that provides reliable data storage and access across all the nodes in a Hadoop cluster. It links together the file systems on many local nodes to create a single file system.

Yet Another Resource Negotiator (YARN)

The next-generation MapReduce, which assigns CPU, memory and storage to applications running on a Hadoop cluster. It enables application frameworks other than MapReduce to run on Hadoop, opening up a wealth of possibilities.

Hadoop is supplemented by an ecosystem of Apache open-source projects that extend the value of Hadoop and improve its usability.



[wptab name=’Syllabus’]

Hadoop Developer Course Syllabus

  • Introduction
    • Hadoop history and concepts
    • Ecosystem
    • Distributions
    • High level architecture
    • Hadoop myths
    • Hadoop challenges (hardware / software)
  • HDFS
    • Concepts (horizontal scaling, replication, data locality, rack awareness)
    • Architecture
    • Namenode (function, storage, file system meta-data, and block reports)
    • Secondary namenode
    • HA Standby namenode
    • Data node
    • Communications / heart-beats
    • Block manager / balancer
    • Health check / safemode
    • read / write path
    • Navigating HDFS UI
    • Command-line interaction with HDFS
    • File systems abstractions
    • WebHDFS
    • Reading / writing files using Java API
    • Getting Data into / out of HDFS (Flume, Sqoop)
    • Getting HDFS stats
    • Latest in HDFS
    • Namenode HA and Federation
    • HDFS roadmap
  • MapReduce
    • Parallel computing before MapReduce
    • MapReduce concepts
    • Daemons: jobtracker / tasktracker
    • Phases: driver, mapper, shuffle/sort, and reducer
    • First MapReduce job
    • MapReduce UI walk through
    • Counters
    • Distributed cache
    • Combiners
    • Partitioners
    • MapReduce configuration
    • Job config
    • MR types and formats
    • Sorting
    • Job schedulers
    • MapReduce best practices
    • MRUnit
    • Optimizing MapReduce
    • Fool proofing MR
    • Thinking in MapReduce
    • YARN: architecture and use
  • Pig
    • Intro: principles and uses cases
    • Pig versus MapReduce
  • Hive
    • Intro: principles and uses cases
    • Environment and configuration
    • Hive tables and metadata
    • Hive keywords
  • HBase
    • History and concepts
    • Architecture
    • HBase versus RDBMS
    • HBase shell
    • HBase Java API
    • Splits and compaction
    • Read path / write path
    • Schema design
  • Real world Big Data skills and a hackathon
    • NoSQL design patterns: going from SQL to NoSQL
    • Smart Meter data collection with Flume
    • Sinks into HDFS and HBase
    • Analyzing smart meter data with Pig and Hive
    • Smart meter analytics with Mahout
    • Scheduling complete workflow with Oozie
  • Conclusion


[wptab name=’Duration’]

  • Regular classes – 4 weeks
  • Weekend Classes – 6 weeks
  • Customized Fast Track option is available as well. Call 9731012185 now to customize according to your requirement


[wptab name=’Trainer’]

  • Experienced IT professionals
  • Having hands on practical knowledge
  • With experience of training large batches in both offline and online mode


[wptab name=’Placement’]

The following services are available on demand as add-on to this course

  • Resume Preparation
  • Mock interviews
  • Job opportunity leads and suggestions


[wptab name=’Mode’]

  • Online Self Paced Training (SPT) with Videos and Documents
  • Online Instructor Led Training (ILT)

About the course:

Study9 provides a robust job market focused Hadoop training. Our Hadoop course is designed with the right mix of basic and advanced topics to get one started in the domain and enable a person to get a good job in this competitive market. Our Hadoop trainers are experienced professionals with hands on knowledge of Hadoop projects. The Hadoop course content is designed with keeping the current job market’s demands in mind.Our Hadoop training course is value for money and tailor made for our students.

About Study9 Training Method

The Study9 Hadoop training courses are completely online training courses. The online Hadoop training is given using advanced training softwares to make the students comfortable with the online training. The student and teacher can talk over VOIP software, they can share each others screens, share Hadoop course contents and concerns during the class through chat window and even can see each other using Webcams. The time tested proven online Hadoop training methodologies adopted by study9 are of the most advanced ones in India. The student will feel at ease with the Hadoop training mode. And we are so confident on that, we offer a moneyback if the student is not satisfied with first Hadoop Training class.

The cloud based Hadoop training course contents are accessible from anywhere in the world. Study9 provides access for each student to an online Learning Management System that holds all the slides and videos that are part of the Hadoop training courses. The students can access them from their Laptop, Mobile, Tablets etc. The students will also give Hadoop training exams on this Learning Management System and our expert Hadoop trainers will rate their papers and provide certifications on successful completion of these Hadoop training exams.

The best part of this online Hadoop training approach is that it does not require one to waste time by travelling to a particular Hadoop training center. And the timings are flexible so that if someday the student has problems in taking the morning Hadoop training class he/she can fix an alternate time in the evening in discussion with Hadoop trainer. On need basis our Hadoop trainers can take a class in late night as well. On request basis missed Hadoop training class sessions can even be given as video lectures to the student for them to go through to be prepared for the next class.


[wptab name=’Cost’]

[wptab name=’Register’]

Schedule: Weekdays (1 hr /day), Weekends (2.5 hrs /day)  and Fast Track options available




    Your Cart
    Your cart is emptyBack to site