Hadoop for Developers (4 days) 교육 과정

  1. 빅 데이터 Training
  2. Hadoop Training
  3. Hadoop for Developers (4 days) 교육 과정

Course Code

hadoopdev

Duration

28 hours (usually 4 days including breaks)

Requirements

  • comfortable with Java programming language (most programming exercises are in java)
  • comfortable in Linux environment (be able to navigate Linux command line, edit files using 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

  • an SSH client (Linux and Mac already have ssh clients, for Windows Putty is recommended)
  • a browser to access the cluster. We recommend Firefox browser

Overview

Apache Hadoop 은 서버 클러스터에서 Big Data 를 처리하는 데 가장 널리 사용되는 프레임 워크입니다. 이 과정에서는 Hadoop 생태계의 다양한 구성 요소 (HDFS, MapReduce, Pig, Hive 및 HBase)에 대한 개발자를 소개합니다.

    Machine Translated

    Course Outline

    Section 1: Introduction to Hadoop

    • hadoop history, concepts
    • eco system
    • distributions
    • high level architecture
    • hadoop myths
    • hadoop challenges
    • hardware / software
    • lab : first look at Hadoop

    Section 2: HDFS

    • Design and architecture
    • concepts (horizontal scaling, replication, data locality, rack awareness)
    • Daemons : Namenode, Secondary namenode, Data node
    • communications / heart-beats
    • data integrity
    • read / write path
    • Namenode High Availability (HA), Federation
    • labs : Interacting with HDFS

    Section 3 : Map Reduce

    • concepts and architecture
    • daemons (MRV1) : jobtracker / tasktracker
    • phases : driver, mapper, shuffle/sort, reducer
    • Map Reduce Version 1 and Version 2 (YARN)
    • Internals of Map Reduce
    • Introduction to Java Map Reduce program
    • labs : Running a sample MapReduce program

    Section 4 : Pig

    • pig vs java map reduce
    • pig job flow
    • pig latin language
    • ETL with Pig
    • Transformations & Joins
    • User defined functions (UDF)
    • labs : writing Pig scripts to analyze data

    Section 5: Hive

    • architecture and design
    • data types
    • SQL support in Hive
    • Creating Hive tables and querying
    • partitions
    • joins
    • text processing
    • labs : various labs on processing data with Hive

    Section 6: HBase

    • concepts and architecture
    • hbase vs RDBMS vs cassandra
    • HBase Java API
    • Time series data on HBase
    • schema design
    • labs : Interacting with HBase using shell;   programming in HBase Java API ; Schema design exercise

    회원 평가

    ★★★★★
    ★★★★★

    Related Categories

    Related Courses

    Alluxio: Unifying Disparate Storage SystemsAdministrator Training for Apache HadoopApache Hadoop: Manipulation and Transformation of Data PerformanceDatameer for Data AnalystsHadoop AdministrationHadoop For AdministratorsHadoop for Business AnalystsAdvanced Hadoop for DevelopersHadoop for Developers and AdministratorsHadoop for Project ManagersHadoop Administration on MapRHBase for DevelopersApache Avro: Data Serialization for Distributed ApplicationsSamza for Stream ProcessingTigon: Real-time Streaming for the Real World

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