Administrator Training for Apache Hadoop Training Course


35 hours (usually 5 days including breaks)


  • Basic Linux administration skills
  • Basic programming skills



The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment


Deep knowledge on Hadoop cluster administration.

Course Outline

1: HDFS (17%)

  • Describe the function of HDFS Daemons
  • Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing.
  • Identify current features of computing systems that motivate a system like Apache Hadoop.
  • Classify major goals of HDFS Design
  • Given a scenario, identify appropriate use case for HDFS Federation
  • Identify components and daemon of an HDFS HA-Quorum cluster
  • Analyze the role of HDFS security (Kerberos)
  • Determine the best data serialization choice for a given scenario
  • Describe file read and write paths
  • Identify the commands to manipulate files in the Hadoop File System Shell

2: YARN and MapReduce version 2 (MRv2) (17%)

  • Understand how upgrading a cluster from Hadoop 1 to Hadoop 2 affects cluster settings
  • Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons
  • Understand basic design strategy for MapReduce v2 (MRv2)
  • Determine how YARN handles resource allocations
  • Identify the workflow of MapReduce job running on YARN
  • Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN.

3: Hadoop Cluster Planning (16%)

  • Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster.
  • Analyze the choices in selecting an OS
  • Understand kernel tuning and disk swapping
  • Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
  • Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA
  • Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
  • Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
  • Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario

4: Hadoop Cluster Installation and Administration (25%)

  • Given a scenario, identify how the cluster will handle disk and machine failures
  • Analyze a logging configuration and logging configuration file format
  • Understand the basics of Hadoop metrics and cluster health monitoring
  • Identify the function and purpose of available tools for cluster monitoring
  • Be able to install all the ecosystem components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Manager, Sqoop, Hive, and Pig
  • Identify the function and purpose of available tools for managing the Apache Hadoop file system

5: Resource Management (10%)

  • Understand the overall design goals of each of Hadoop schedulers
  • Given a scenario, determine how the FIFO Scheduler allocates cluster resources
  • Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN
  • Given a scenario, determine how the Capacity Scheduler allocates cluster resources

6: Monitoring and Logging (15%)

  • Understand the functions and features of Hadoop’s metric collection abilities
  • Analyze the NameNode and JobTracker Web UIs
  • Understand how to monitor cluster Daemons
  • Identify and monitor CPU usage on master nodes
  • Describe how to monitor swap and memory allocation on all nodes
  • Identify how to view and manage Hadoop’s log files
  • Interpret a log file

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