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Course Outline
1: HDFS (17%)
- Explain the function 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 a system like Apache Hadoop.
- Classify the primary objectives of HDFS design.
- Given a scenario, determine 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.
- Determine the optimal data serialization choice for a given scenario.
- Describe the file read and write paths.
- 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 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 files need to be modified 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%)
- Highlight the key points to consider when choosing hardware and operating systems for hosting an Apache Hadoop cluster.
- Analyze the choices involved in selecting an operating system.
- Understand kernel tuning and disk swapping.
- Given a scenario and workload pattern, identify a hardware configuration suitable 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 frequency of execution, 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 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 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
- Basic Linux administration skills
- Basic programming skills
35 Hours
Testimonials (3)
I genuinely enjoyed the many hands-on sessions.
Jacek Pieczatka
Course - Administrator Training for Apache Hadoop
I genuinely enjoyed the big competences of Trainer.
Grzegorz Gorski
Course - Administrator Training for Apache Hadoop
I mostly liked the trainer giving real live Examples.