Welcome to BigData School that can get you hired on Small startups or big Product based companies.Are you tired of 30-40 hours of theoritical bigdata courses in the market. Welcome to 240+ hours of Bigdata training. Just subscribe to our popup to get access to our 80+hours of course absolutely free.Try them out and you can join our further course once you are happy with our Demos.
Map Reduce
(244 Review)
  • Suraj Ghimire
    Suraj Ghimire
    Java,J2EE,BigData
  • BIGDATA
    Map Reduce
The course contains around 45 sessions on Map-reduce.
The course is completely practically with a lot of handson exercise.
Introduction:

1.  Map-Reduce(Using New API)(20 Hours)

  • Understanding Map Reduce Framework
  • Inspiration to Word-Count Example
  • Developing Map-Reduce Program using Eclipse Luna
  • HDFS Read-Write Process
  • Map-Reduce Life Cycle Method
  • Serialization(Java)
  • Data-types
  • Comparator and Comparable(Java)
  • Custom Output File
  • Analysing Temperature dataset using Map-Reduce
  • Custom Partitioner & Combiner
  • Running Map-Reduce in Local and Pseudo Distributed Mode.

2.  Advanced Map-Reduce(25 Hours)

  • Enum(Java)
  • Custom and Dynamic Counters
  • Running Map-Reduce in Multi-node Hadoop Cluster
  • Custom Writable
  • Site Data Distribution
    • Using Configuration
    • Using DistributedCache
    • Using stringifier
  • Input Formatters
    • NLine Input Format
    • XML Input Format
    • DB Input Format
    • Sequence File Format
    • Avro File Format
  • Sorting
    • Primary Reverse Sorting
    • Secondary Sorting
  • Joins
    • Map-side Joins
    • Reduce side Joins
  • Compression Technique
    • Gzip
    • snappy
    • bzip2
    • deflate
  • Processing Multiple Line using Map-Reduce
  • Processing XML File using Map-Reduce
  • TokenMapper
  • Testing MapReduce with MR Unit
  • Working with NYSE DataSets
  • Running Map-Reduce in Cloudera Box

OUR CURRICULUM

1.  Map-Reduce(Using New API)(20 Hours)

  • Understanding Map Reduce Framework
  • Inspiration to Word-Count Example
  • Developing Map-Reduce Program using Eclipse Luna
  • HDFS Read-Write Process
  • Map-Reduce Life Cycle Method
  • Serialization(Java)
  • Data-types
  • Comparator and Comparable(Java)
  • Custom Output File
  • Analysing Temperature dataset using Map-Reduce
  • Custom Partitioner & Combiner
  • Running Map-Reduce in Local and Pseudo Distributed Mode.

2.  Advanced Map-Reduce(25 Hours)

  • Enum(Java)
  • Custom and Dynamic Counters
  • Running Map-Reduce in Multi-node Hadoop Cluster
  • Custom Writable
  • Site Data Distribution
    • Using Configuration
    • Using DistributedCache
    • Using stringifier
  • Input Formatters
    • NLine Input Format
    • XML Input Format
    • DB Input Format
    • Sequence File Format
    • Avro File Format
  • Sorting
    • Primary Reverse Sorting
    • Secondary Sorting
  • Joins
    • Map-side Joins
    • Reduce side Joins
  • Compression Technique
    • Gzip
    • snappy
    • bzip2
    • deflate
  • Processing Multiple Line using Map-Reduce
  • Processing XML File using Map-Reduce
  • TokenMapper
  • Testing MapReduce with MR Unit
  • Working with NYSE DataSets
  • Running Map-Reduce in Cloudera Box

We will keep adding
Kavita Mehra

kavitha Mehra

Map Reduce
Map-reduce

COURSE INSTRUCTOR

asdf

Suraj Ghimire

Suraj Ghimire

Java,J2EE,BigData

asdf