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Big Data Hadoop : Advance concepts and Components.

Course Summary

Prepare yourself for Hadoop Certification


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    Course Syllabus

    • Introduction:
      • Introduction
      • Intro to Hadoop Eco System
    • Prerequisites
      • Intro To Core Java
      • Intro to Linux & Unix
      • Linux Practicals 1
      • Linux Practical 2
      • Linux Practical 3
      • Intro to Python
    • Installation
      • Hadoop Installation Part 1
      • Hadoop Installation Part 2
    • HDFS
      • Hadoop Distributed File System (HDFS) Intro
      • HDFS Commands part 1
      • HDFS Commands Part 2
    • MAP Reduce
      • Map Reduce Intro
      • Map Reduce - Word Count problem
      • Map Reduce - Word Count code Explianed
      • Map Reduce - Word Count code Explaination continued
      • Map Reduce- Customer Sentiment Analyzer
      • Map Reduce- Customer Sentiment Analyzer Explained
      • Map Reduce- Customer Sentiment Analyzer Explaination continued
      • Map Reduce - Word Count with Python
      • Map Reduce - Max temprature problem explained with python
    • Component - HIVE
      • Hive Introduction
      • Hive commands using command line interface
      • Hive commands (Loading Files into Hive)
      • Hive commands (adding Buckets)
    • Component - Pig
      • Intro to Pig
      • Pig Commands using command line interface
      • Pig useful Commands
      • User Defined Functions in PIG - Using JAVA
    • Component Sqoop
      • Sqoop Intro
      • Scoop commands Connecting to SQL and HDFS
      • Scoop commands ( RDBMS to HDFS command conversions)
      • Scoop commands (Export MSQL table into Hive Tables and back to MYSQL)
      • Scoop commands ( Transferring HDFS to MYSQL )
    • Component - Flume
      • Flume Intro
      • Flume Configuration
      • Flume Twitter Analysis
    • Component - HBase
      • Intro to HBase
      • HBase VS RDBMS
      • HBase Commands
    • Conclusion
      • Conclusion
      • Copy of the presentation I used


Course Fee:
USD 99

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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