Get in Touch

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 aspects.
  • Identify the current computing system features that drive the need for systems like Apache Hadoop.
  • Categorize the primary objectives of HDFS Design.
  • 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, specifically Kerberos.
  • Select the most suitable data serialization method for a given scenario.
  • Describe the pathways for file read and write operations.
  • Identify the commands used to manipulate files in the Hadoop File System Shell.

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

  • Understand the impact on cluster settings when upgrading a cluster from Hadoop 1 to Hadoop 2.
  • Understand the deployment of MapReduce v2 (MRv2 / YARN), including all associated YARN daemons.
  • Grasp the basic design strategy for MapReduce v2 (MRv2).
  • Determine how YARN manages resource allocations.
  • Identify the workflow of a MapReduce job executing on YARN.
  • Determine which files need to be modified, and how, to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) on YARN.

3: Hadoop Cluster Planning (16%)

  • Key points to consider when selecting hardware and operating systems for hosting an Apache Hadoop cluster.
  • Analyze the options available when selecting an OS.
  • Understand kernel tuning and disk swapping processes.
  • Given a scenario and workload pattern, identify the hardware configuration that fits the scenario.
  • Given a scenario, determine the necessary ecosystem components for your cluster to meet SLAs.
  • Cluster sizing: Given a scenario and execution frequency, identify workload specifics, including CPU, memory, storage, and disk I/O.
  • 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 handles disk and machine failures.
  • Analyze logging configuration and the format of logging configuration files.
  • Understand the fundamentals 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 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

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

Number of participants


Price per participant

Testimonials (3)

Upcoming Courses

Related Categories