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Course Outline
1: HDFS (17%)
- Outline the role of HDFS Daemons
- Explain the standard operation of an Apache Hadoop cluster, encompassing both data storage and processing functionalities.
- Identify contemporary computing system features that drive the need for systems like Apache Hadoop.
- Classify the primary objectives behind HDFS design
- Given a specific scenario, determine the suitable use case for HDFS Federation
- Identify the components and daemons within an HDFS HA-Quorum cluster
- Analyze the significance of HDFS security mechanisms (Kerberos)
- Select the optimal data serialization method for a given scenario
- Describe the pathways for file read and write operations
- Identify the necessary commands for manipulating files within the Hadoop File System Shell
2: YARN and MapReduce version 2 (MRv2) (17%)
- Comprehend the impact of upgrading a cluster from Hadoop 1 to Hadoop 2 on cluster configurations
- Understand the deployment of MapReduce v2 (MRv2 / YARN), including all associated YARN daemons
- Grasp the core design strategy for MapReduce v2 (MRv2)
- Determine how YARN manages resource allocations
- Identify the workflow of a MapReduce job executing on YARN
- Determine the file modifications required to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN
3: Hadoop Cluster Planning (16%)
- Highlight key considerations when selecting hardware and operating systems to host an Apache Hadoop cluster.
- Analyze options for selecting an appropriate OS
- Understand kernel tuning and disk swapping mechanisms
- Given a scenario and workload pattern, identify the hardware configuration best suited to the context
- Given a scenario, determine the ecosystem components required for the cluster to meet SLA requirements
- 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 basics of Hadoop metrics and cluster health monitoring
- Identify the function and purpose of available tools for cluster monitoring
- Be able to 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
- Fundamental skills in Linux administration
- 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.