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Course Outline
1: HDFS (17%)
- Describe the functions of HDFS Daemons.
- Explain the normal operation of an Apache Hadoop cluster, covering both data storage and data processing.
- Identify current limitations in computing systems that necessitate a solution like Apache Hadoop.
- Classify the primary objectives of HDFS design.
- Evaluate scenarios to determine the appropriate use case for HDFS Federation.
- Identify the components and daemons within an HDFS HA-Quorum cluster.
- Analyze the role of HDFS security mechanisms, 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 within 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.
- Comprehend the deployment process for MapReduce v2 (MRv2 / YARN), including all YARN daemons.
- Grasp the fundamental design strategy behind MapReduce v2 (MRv2).
- Determine how YARN manages resource allocations.
- Identify the workflow of a MapReduce job executing on YARN.
- Determine which files require modification and the necessary steps to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN.
3: Hadoop Cluster Planning (16%)
- Examine the key considerations when selecting hardware and operating systems to host an Apache Hadoop cluster.
- Analyze the factors involved in selecting an operating system.
- Understand the principles of kernel tuning and disk swapping.
- Given a specific scenario and workload pattern, identify a hardware configuration that aligns with the requirements.
- Given a scenario, determine the necessary ecosystem components to run on the cluster to meet the Service Level Agreement (SLA).
- Perform cluster sizing: given a scenario and execution frequency, identify specific workload requirements, including CPU, memory, storage, and disk I/O.
- Address disk sizing and configuration, including comparisons between JBOD and RAID, SANs, virtualization, and cluster-specific disk sizing requirements.
- Network Topologies: understand network usage within 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 configurations and logging configuration file formats.
- 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 overarching 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 across all nodes.
- Identify methods to view and manage Hadoop's log files.
- Interpret log file contents.
Requirements
- Fundamental Linux administration skills
- Basic programming competencies
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.
