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Course Outline

1: HDFS (17%)

  • Explain the functions of HDFS Daemons
  • Describe the standard operation of an Apache Hadoop cluster, covering both data storage and data processing.
  • Identify current computing system features that drive the need for systems like Apache Hadoop.
  • Classify the primary design goals of HDFS.
  • Given a specific scenario, identify the appropriate use case for HDFS Federation.
  • Identify the components and daemons of an HDFS HA-Quorum cluster.
  • Analyze the role of HDFS security (Kerberos).
  • Determine the optimal data serialization choice for a given scenario.
  • 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 how upgrading a cluster from Hadoop 1 to Hadoop 2 impacts cluster settings.
  • Understand how to deploy MapReduce v2 (MRv2 / YARN), including all associated YARN daemons.
  • Comprehend the core design strategy for MapReduce v2 (MRv2).
  • Determine how YARN manages resource allocations.
  • Identify the workflow of a MapReduce job running on YARN.
  • Determine which configuration files need to be modified, and how, to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN.

3: Hadoop Cluster Planning (16%)

  • Key considerations when selecting hardware and operating systems to host an Apache Hadoop cluster.
  • Analyze options available when selecting an OS.
  • Understand kernel tuning and disk swapping mechanisms.
  • Given a scenario and workload pattern, identify the hardware configuration suitable for that scenario.
  • Given a scenario, 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 disk sizing requirements within 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 logging configuration and the format of logging configuration files.
  • Understand the basics of Hadoop metrics and cluster health monitoring.
  • Identify the function and purpose of available tools for cluster monitoring.
  • Install all 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 capabilities.
  • Analyze the NameNode and JobTracker Web UIs.
  • Understand how to monitor cluster Daemons.
  • Identify and monitor CPU usage on master nodes.
  • Describe methods to monitor swap and memory allocation on all nodes.
  • Identify how to view and manage Hadoop’s log files.
  • Interpret a log file.

Requirements

  • Foundational Linux administration skills
  • Basic programming proficiency
 35 Hours

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