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Course Outline

1: HDFS (17%)

  • Explain the function of HDFS Daemons
  • Describe the typical operation of an Apache Hadoop cluster, covering both data storage and processing.
  • Identify current computing system features that drive the need for systems like Apache Hadoop.
  • Classify the primary objectives of HDFS Design.
  • Given a 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 YARN daemons.
  • Understand the basic design strategy for MapReduce v2 (MRv2).
  • Determine how YARN handles resource allocations.
  • Identify the workflow of a MapReduce job running on YARN.
  • Determine which files need to be changed 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%)

  • Key points to consider when choosing 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 the hardware configuration appropriate for that 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 execution frequency, identify the specifics for the workload, including CPU, memory, storage, and 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 handles 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.

Requirements

  • Fundamental Linux administration skills
  • Basic programming proficiency
 35 Hours

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