Hadoop for Business Analysts培训

课程编码

hadoopba

课程时长

21 小时 通常来说是3天,包括中间休息。

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

课程概览

Apache Hadoop是处理大数据最流行的框架。 Hadoop提供丰富而深入的分析功能,并且正在进入传统BI分析世界。本课程将向分析师介绍Hadoop生态系统的核心组件及其分析听众业务分析师持续时间三天格式讲座和实验室之手。

Machine Translated

课程大纲

  • 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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