Implement Hbase and MapReduce Integration

This web-based training course on Implement Hbase and MapReduce Integration functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.

Initially, MapReduce was a process built to streamline and solve problems of processing excess terabytes of data using scalable methods. MapReduce strive to create a way of building systems whos performance increase linearly with the addition of physical machines. Fundamentally, MapReduce is a programming model for associated implementation of processing and creating big data sets. Following a divide and conquer approach of splitting data located on various distributed filesystems in order to provide the servers with the availability of accessing the chunks of data and the ability to process them extremely fast and efficiently. MapReduce also provides information for consolidating data in an effective manner for providing great and easy data access. A MapReduce program makes use of a Map() procedure which performs the operations of filtering and sorting. Another function known as Reduce() method is used for performing summary operations such frequency yielding in data sets and more. Together, the two functions help in parallel management of tasks along with all communications and data transfer between the various parts of the system while providing decrement in redundancy and increment in fault tolerance.


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Course Details

Through this Hbase integration with MapReduce online training course the fundamental aspect of the MapReduce programming model are taught in detail to create a strong knowledge base of basics in the trainees. Further the various different elements of working with the basic Map() and Reduce() functions of the tool are detailed upon. The course begins with the understanding of classes and how they are involved with Hbase and Hadoop big data and goes through inputFormat, outputFormat, provisioning methods and data sources and sinks. The course covers all the theoretical aspects of the subject and further deals with the various applicative and practical understanding of the subject to provide the trainees with the requisite knowledge of modeling effective MapReduce models as they are implemented in HBase. To successfully complete the course, it is advised that the trainees have a basic understanding of database management and big data to keep pace with the course.


Understanding Classes

  • Involvement of classes in the Hadoop for implementing MapReduce

InputFormat

  • Spllitting the input data
  • Returning a RecordReader instance
  • Defining the classes of the key and value objects
  • Providing a next() method
  • Iterating over each input record

Mapper

  • Processing the RecordReader using the map() method

Reducer

  • The Reducer stage and class hierarchy
  • Getting the output of a Mapper class
  • Processing after the data has been shuffled and sorted

OutputFormat

  • Working with the OutputFormat class
  • Persist the data in various locations
  • Implementations to allow output to files
  • Using a TableRecord Writer

Supporting Classes

  • Using TableMapReduceUtil
  • Setting up MapReduce jobs over HBase
  • Static methods to configure a job
  • Running jobs with HBase as the source and/or the target

MapReduce over HBase

Preparation

  • Running a MapReduce job
  • Making libraries available before the job is executed
  • Static preparation of all task nodes
  • Supplying everything needed with the job

Static Provisioning

  • Installing JAR file(s) locally on the task tracker machines
  • Copy the JAR files into a common location on all nodes
  • Adding the JAR files with full location into the hadoop-env.sh configuration file

Dynamic Provisioning

  • Providing different libraries to each job
  • Updating the library versions along with your job classes
  • Using the dynamic provisioning approach

Data Source and Sink

  • The source or target of a MapReduce job can
  • Use HBase as both input and output
  • MapReduce template using a table for the input and output types
  • Setting the TableInputFormat and TableOutputFormat classes
  • Fields of the job configuration
Live Instructor-led & Interactive Online Sessions


Regular Course

Duration : 40 Hours


Capsule Course

Duration : 4-8 Hours

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Training Options

OPTION 1

Weekdays- Cloud Based Training

Mon - Fri 07:00 AM - 09:00 AM(Mon, Wed, Fri)

Weekdays Online Lab

Mon - Fri 07:00 AM - 09:00 AM(Tue, Thur)


OPTION 2

Weekend- Cloud Based Training

Sat-Sun 09:00 AM - 11:00 AM (IST)

Weekend Online Lab

Sat-Sun 11:00 AM - 01:00 PM


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