Hadoop for Business Analysts 교육 과정

Course Code

hadoopba

Duration

21 hours (usually 3 days including breaks)

Requirements

  • 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

Overview

Apache Hadoop은 Big Data를 처리하기위한 가장 보편적 인 프레임 워크입니다 Hadoop은 풍부하고 심층적 인 분석 기능을 제공하며, 전통적 BI 분석 세계로 진출하고 있습니다 이 과정에서는 하둡 에코 시스템의 핵심 구성 요소와 그 분석에 대한 분석가를 소개합니다 청중 비즈니스 분석가 지속 삼 일 체재 강의와 실험실에 손 .

Machine Translated

Course Outline

  • 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

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