Hadoop for Business Analysts培訓

課程代碼

hadoopba

課程時長

21 時間: 同常來說包括休息是 3天

最低要求

  • 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

概觀

Apache Hadoop是處理大數據最流行的框架。 Hadoop提供豐富而深入的分析功能,並且正在進入傳統BI分析世界。本課程將向分析師介紹Hadoop生態系統的核心組件及其分析聽衆業務分析師持續時間三天格式講座和實驗室之手。

Machine Translated

課程簡介

  • 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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