Hadoop for Systems Administrators

GL660 - Hadoop for Systems Administrators

Upcoming GL660 Classes

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This course covers the essentials of deploying and managing an Apache™ Hadoop® cluster. The course is lab intensive with each participant creating their own Hadoop cluster using either the CDH (Cloudera's Distribution, including Apache Hadoop) or Hortonworks Data Platform stacks. Core Hadoop services are explored in depth with emphasis on troubleshooting and recovering from common cluster failures. The fundamentals of related services such as Ambari, Zookeeper, Pig, Hive, HBase, Sqoop, Flume, and Oozie are also covered. The course is approximately 60% lecture and 40% labs.


Qualified participants should be comfortable with the Linux commands and have some systems administration experience, but do not need previous Hadoop experience

Supported Distributions:

Red Hat Enterprise Linux 6

Course Outline:

  1. Hadoop: The Big Picture
    1. Data Analysis
    2. Big Data
    3. Hadoop Core Architecture
    4. Hadoop Ecosystem
    5. Hadoop Ecosystem continued
    6. Running Commands on Multiple Systems
    Lab Tasks
    1. Running Commands on Multiple Hosts
    2. Preparing to Install Hadoop
  2. HDFS
    1. Design Goals
    2. Design
    3. Blocks
    4. Block Replication
    5. Namenode Daemon
    6. Secondary Namenode Daemon
    7. Datanode Daemon
    8. Accessing HDFS
    9. Permissions and Users
    10. Adding and Removing Datanodes
    11. Balancing
    Lab Tasks
    1. Single Node HDFS
    2. Multi-node HDFS
    3. Files and HDFS
    4. Managing and Maintaining HDFS
  3. MapReduce
    1. MapReduce
    2. Terminology and Data Flow
    3. MapReduce Daemons
    4. YARN
    5. MapReduce Essential Configuration
    6. Failure and Recovery
    Lab Tasks
    1. MapReduce
  4. MapReduce Schedulers
    1. Working with Jobs
    2. Scheduling Concepts
    3. FIFO Scheduler
    4. Fair Scheduler
    5. Fair Scheduler - Configuration
    Lab Tasks
    1. MapReduce Schedulers
  1. Installing Hadoop with Ambari Lab Tasks
    1. Install Ambari