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Course Outline
1: HDFS (17%)
- Explain the role of HDFS Daemons
- Describe the standard operation of an Apache Hadoop cluster, covering both data storage and processing aspects.
- Identify current computing system characteristics that drive the need for a system like Apache Hadoop.
- Classify the primary objectives of HDFS design.
- Given a specific 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 paths for file read and write operations.
- Identify the commands required to manipulate files using the Hadoop File System Shell.
2: YARN and MapReduce version 2 (MRv2) (17%)
- Comprehend how upgrading a cluster from Hadoop 1 to Hadoop 2 impacts cluster configurations.
- Understand the deployment of MapReduce v2 (MRv2) / YARN, including all YARN daemons.
- Understand the fundamental design strategy of MapReduce v2 (MRv2).
- Determine how YARN manages resource allocation.
- Identify the workflow of a MapReduce job running on YARN.
- Determine which files need modification and how to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) on YARN.
3: Hadoop Cluster Planning (16%)
- Key considerations when selecting hardware and operating systems to host an Apache Hadoop cluster.
- Analyze options for selecting an operating system.
- Understand kernel tuning and disk swapping mechanisms.
- Given a scenario and workload pattern, identify hardware configurations suitable for that scenario.
- Given a scenario, determine the ecosystem components your cluster requires to meet SLA obligations.
- 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 log file formats.
- 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 Hadoop scheduler.
- 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 Linux administration skills
- Basic programming proficiency
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.
