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Course Outline

1: HDFS (17%)

  • Explain the roles of HDFS Daemons.
  • Describe the standard operation of an Apache Hadoop cluster, covering both data storage and processing.
  • Recognize the current features of computing systems that drive the need for platforms like Apache Hadoop.
  • Outline the primary objectives of HDFS design.
  • In given scenarios, identify appropriate use cases for HDFS Federation.
  • Identify the components and daemons involved in an HDFS HA-Quorum cluster.
  • Analyze the role of HDFS security, specifically regarding Kerberos.
  • Determine the most suitable data serialization method for specific scenarios.
  • Describe the paths for file reading and writing.
  • Identify the commands used to manipulate files in the Hadoop File System Shell.

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

  • Understand the impact of upgrading a cluster from Hadoop 1 to Hadoop 2 on cluster configurations.
  • Learn how to deploy MapReduce v2 (MRv2 / YARN), including all associated YARN daemons.
  • Grasp the fundamental design strategy behind MapReduce v2 (MRv2).
  • Understand how YARN manages resource allocation.
  • Trace the workflow of a MapReduce job running on YARN.
  • Determine the necessary file changes and procedures to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) on YARN.

3: Hadoop Cluster Planning (16%)

  • Identify key considerations when selecting hardware and operating systems for hosting an Apache Hadoop cluster.
  • Analyze options for selecting an operating system.
  • Understand kernel tuning and disk swapping mechanisms.
  • In given scenarios and workload patterns, identify the appropriate hardware configuration.
  • In given scenarios, determine the ecosystem components required for the cluster to meet SLA requirements.
  • Cluster sizing: Given a scenario and execution frequency, identify workload specifics, including CPU, memory, storage, and disk I/O requirements.
  • Disk sizing and configuration, including JBOD versus RAID, SANs, virtualization, and cluster disk sizing requirements.
  • 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%)

  • In given scenarios, identify how the cluster handles disk and machine failures.
  • Analyze logging configurations and log file formats.
  • Understand the basics of Hadoop metrics and cluster health monitoring.
  • Identify the functions and purposes of available cluster monitoring tools.
  • Install all ecosystem components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Manager, Sqoop, Hive, and Pig.
  • Identify the functions and purposes of available tools for managing the Apache Hadoop file system.

5: Resource Management (10%)

  • Understand the overall design goals of each Hadoop scheduler.
  • In given scenarios, determine how the FIFO Scheduler allocates cluster resources.
  • In given scenarios, determine how the Fair Scheduler allocates cluster resources under YARN.
  • In given scenarios, determine how the Capacity Scheduler allocates cluster resources.

6: Monitoring and Logging (15%)

  • Understand the functions and features of Hadoop’s metric collection capabilities.
  • 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 space and memory allocation on all nodes.
  • Identify how to view and manage Hadoop’s log files.
  • Interpret log files.

Requirements

  • Foundational skills in Linux administration
  • Basic programming proficiency
 35 Hours

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