Administrator Training for Apache Hadoop Training Course


Detailed information

Duration:35 Hour
Total hours of lesson:7
Requirements:basic Linux administration skills basic programming skills
Students per class:6

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Course program

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 ecoystme 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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