Hadoop for Business Analysts Schulung

Kurs Code

hadoopba

Dauer

21 hours (üblicherweise 3 Tage inklusive Pausen)

Voraussetzungen

  • programming background with databases / SQL
  • basic knowledge of Linux (be able to navigate Linux command line, editing files with vi / nano)

Lab environment

Zero Install : There is no need to install hadoop software on students’ machines! A working Hadoop cluster will be provided for students.

Students will need the following

Überblick

Apache Hadoop ist das beliebteste Framework für die Verarbeitung von Big Data Hadoop bietet umfassende und umfassende Analysemöglichkeiten und bahnt sich einen Weg in die tradionelle BI-Analytics-Welt In diesem Kurs wird ein Analyst zu den Kernkomponenten von Hadoop eco system und seinen Analysen vorgestellt Publikum Business Analysten Dauer 3 Tage Format Vorlesungen und praktische Übungen .

Machine Translated

Schulungsübersicht

  • Section 1: Introduction to Hadoop
    • hadoop history, concepts
    • eco system
    • distributions
    • high level architecture
    • hadoop myths
    • hadoop challenges
    • hardware / software
    • Labs : first look at Hadoop
  • Section 2: HDFS Overview
    • concepts (horizontal scaling, replication, data locality, rack awareness)
    • architecture (Namenode, Secondary namenode, Data node)
    • data integrity
    • future of HDFS : Namenode HA, Federation
    • labs : Interacting with HDFS
  • Section 3 : Map Reduce Overview
    • mapreduce concepts
    • daemons : jobtracker / tasktracker
    • phases : driver, mapper, shuffle/sort, reducer
    • Thinking in map reduce
    • Future of mapreduce (yarn)
    • labs : Running a Map Reduce program
  • Section 4 : Pig
    • pig vs java map reduce
    • pig latin language
    • user defined functions
    • understanding pig job flow
    • basic data analysis with Pig
    • complex data analysis with Pig
    • multi datasets with Pig
    • advanced concepts
    • lab : writing pig scripts to analyze / transform data
  • Section 5: Hive
    • hive concepts
    • architecture
    • SQL support in Hive
    • data types
    • table creation and queries
    • Hive data management
    • partitions & joins
    • text analytics
    • labs (multiple) : creating Hive tables and running queries, joins , using partitions, using text analytics functions
  • Section 6: BI Tools for Hadoop
    • BI tools and Hadoop
    • Overview of current BI tools landscape
    • Choosing the best tool for the job

Erfahrungsberichte

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