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Learn Hadoop, MapReduce and BigData from Scratch

Course Summary

A Complete Guide to Learn and Master the Popular Big Data Technologies


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    Course Syllabus

    • Introduction to Big Data
      • Introduction to the Course
      • Why Hadoop, Big Data and Map Reduce Part - A
      • Why Hadoop, Big Data and Map Reduce Part - B
      • Why Hadoop, Big Data and Map Reduce Part - C
      • Architecture of Clusters
      • Virtual Machine (VM), Provisioning a VM with vagrant and puppet
    • Hadoop Architecture
      • Set up a single Node Hadoop pseudo cluster Part - A
      • Set up a single Node Hadoop pseudo cluster Part - B
      • Set up a single Node Hadoop pseudo cluster Part - c
      • Clusters and Nodes, Hadoop Cluster Part - A
      • Clusters and Nodes, Hadoop Cluster Part - B
      • NameNode, Secondary Name Node, Data Nodes Part - A
      • NameNode, Secondary Name Node, Data Nodes Part - B
      • Running Multi node clusters on Amazons EMR Part - A
      • Running Multi node clusters on Amazons EMR Part - B
      • Running Multi node clusters on Amazons EMR Part - C
      • Running Multi node clusters on Amazons EMR Part - D
      • Running Multi node clusters on Amazons EMR Part - E
    • Distributed file systems
      • Hdfs vs Gfs a comparison - Part A
      • Hdfs vs Gfs a comparison - Part B
      • Run hadoop on Cloudera, Web Administration
      • Run hadoop on Hortonworks Sandbox
      • File system operations with the HDFS shell Part - A
      • File system operations with the HDFS shell Part - B
      • Advanced hadoop development with Apache Bigtop Part - A
      • Advanced hadoop development with Apache Bigtop Part - B
    • Mapreduce Version 1
      • MapReduce Concepts in detail Part - A
      • MapReduce Concepts in detail Part - B
      • Jobs definition, Job configuration, submission, execution and monitoring Part -A
      • Jobs definition, Job configuration, submission, execution and monitoring Part -B
      • Jobs definition, Job configuration, submission, execution and monitoring Part -C
      • Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part A
      • Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part B
      • Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part C
      • The ETL class, Definition, Extract, Transform, and Load Part - A
      • The ETL class, Definition, Extract, Transform, and Load Part - B
      • The UDF class, Definition, User Defined Functions Part - A
      • The UDF class, Definition, User Defined Functions Part - B
    • Mapreduce with Hive ( Data warehousing )
      • Schema design for a Data warehouse Part - A
      • Schema design for a Data warehouse Part - B
      • Hive Configuration
      • Hive Query Patterns Part - A
      • Hive Query Patterns Part - B
      • Hive Query Patterns Part - C
      • Example Hive ETL class Part - A
      • Example Hive ETL class Part - B
    • Mapreduce with Pig (Parallel processing)
      • Introduction to Apache Pig Part - A
      • Introduction to Apache Pig Part - B
      • Introduction to Apache Pig Part - C
      • Introduction to Apache Pig Part - D
      • Pig LoadFunc and EvalFunc classes
      • Example Pig ETL class Part - A
      • Example Pig ETL class Part - B
    • The Hadoop Ecosystem
      • Introduction to Crunch Part - A
      • Introduction to Crunch Part - B
      • Introduction to Arvo
      • Introduction to Mahout Part - A
      • Introduction to Mahout Part - B
      • Introduction to Mahout Part - C
    • Mapreduce Version 2
      • Apache Hadoop 2 and YARN Part - A
      • Apache Hadoop 2 and YARN Part - B
      • Yarn Examples
    • Putting it all together
      • Amazon EMR example Part - A
      • Amazon EMR example Part - B
      • Amazon EMR example Part - C
      • Amazon EMR example Part - D
      • Apache Bigtop example Part - A
      • Apache Bigtop example Part - B
      • Apache Bigtop example Part - C
      • Apache Bigtop example Part - D
      • Apache Bigtop example Part - E
      • Apache Bigtop example Part - F
      • Course Summary
      • References


Course Fee:
USD 199

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

Related Posts:

Hadoop

 

Big Data

 

MapReduce

 

Apache

 

Pig

 

Extract, Transform, Load (ETL)

 

Hive

 

Shell

 

Data Warehouse (DW)

 

Workflow

 

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Awards & Accolades for MyTechLogy
Winner of
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Top 100 Asia
Finalist at SiTF Awards 2014 under the category Best Social & Community Product
Finalist at HR Vendor of the Year 2015 Awards under the category Best Learning Management System
Finalist at HR Vendor of the Year 2015 Awards under the category Best Talent Management Software
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