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Course Outline
1: HDFS (17%)
- Explain the role of HDFS Daemons
- Describe the standard operation of an Apache Hadoop cluster, encompassing both data storage and processing functions.
- Identify current computing system features that drive the need for systems like Apache Hadoop.
- Classify the primary objectives of HDFS Design.
- Determine the appropriate use case for HDFS Federation within a given scenario.
- Identify the components and daemons of an HDFS HA-Quorum cluster.
- Analyze the role of HDFS security mechanisms, including Kerberos.
- Select the optimal data serialization choice for a specific scenario.
- Describe the pathways 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%)
- Understand the impact on cluster settings when upgrading from Hadoop 1 to Hadoop 2.
- Comprehend the deployment of MapReduce v2 (MRv2 / YARN), including all associated YARN daemons.
- Understand the core design strategy for MapReduce v2 (MRv2).
- Determine how YARN manages resource allocations.
- Identify the workflow of a MapReduce job executing on YARN.
- Identify the specific files that must be modified and how to do so when migrating a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) on YARN.
3: Hadoop Cluster Planning (16%)
- Highlight key considerations for selecting hardware and operating systems to host an Apache Hadoop cluster.
- Analyze the options available when selecting an operating system.
- Understand kernel tuning and disk swapping processes.
- Identify an appropriate hardware configuration for a given scenario and workload pattern.
- Determine the necessary ecosystem components for a cluster to meet SLA requirements in a specific scenario.
- Cluster sizing: Identify workload specifics, including CPU, memory, storage, and disk I/O, based on a scenario and execution frequency.
- Disk Sizing and Configuration: Understand 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%)
- Identify how the cluster handles disk and machine failures in a given scenario.
- 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.
- Determine how the FIFO Scheduler allocates cluster resources in a given scenario.
- Determine how the Fair Scheduler allocates cluster resources under YARN in a given scenario.
- Determine how the Capacity Scheduler allocates cluster resources in a given scenario.
6: Monitoring and Logging (15%)
- Understand the functions and features of Hadoop’s metric collection capabilities.
- Analyze the NameNode and JobTracker Web UIs.
- Understand methods for monitoring cluster Daemons.
- Identify and monitor CPU usage on master nodes.
- Describe methods for monitoring swap and memory allocation across all nodes.
- Identify methods for viewing and managing 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.
