Administrator Training for Apache Hadoop Training

Administrator Training for Apache Hadoop

Master the key techniques and concepts of Hadoop administration in this comprehensive training. Delve into HDFS, YARN, cluster planning and installation.

Learn the art of effective resource management and master the intricacies of monitoring and logging. Prepare to manage powerful, scalable Hadoop clusters and harness their full potential.

What will you learn?

This Apache Hadoop Administrator course is your entry point into the world’s leading platform for distributed data processing. By the end of the course, participants will:

Understand HDFS: Dive into the core daemons and operational capabilities of the Hadoop File System.
Learn YARN and MRv2: Upgrade and transition smoothly from Hadoop 1 to Hadoop 2.
Plan your Hadoop cluster: Select the best hardware, optimal operating system, and suitable network topology.
Install and manage your cluster: Master tools and techniques to ensure optimal operation.
Optimise resource management: Develop understanding of FIFO, Fair, and Capacity schedulers.
Master monitoring and logging: Utilise Hadoop metrics, web interfaces, and log files to ensure cluster health.

Requirements:

Basic IT knowledge: familiarity with operating systems, hardware configurations, and basic networking functions.

Prior experience with distributed systems is beneficial but not mandatory.

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

HDFS – The Heart of Hadoop

Introduction to HDFS: understanding its central role in Hadoop
HDFS daemons: detailed analysis of NameNode, DataNode, and SecondaryNameNode and their responsibilities
Operating a Hadoop cluster: how data is efficiently stored and processed
The evolution of Hadoop: why modern computing systems require platforms like Hadoop
Design principles of HDFS: focus on reliability, scalability, and fault tolerance
Exploring HDFS Federation: improving namespace isolation and scalability
High availability with HDFS HA Quorum: ensuring data durability and cluster availability
Security in HDFS: introduction to Kerberos-based authentication
Serialisation in Hadoop: optimal serialisation options for various scenarios
Practical exercise: working with the Hadoop File System Shell – commands for managing and editing files

YARN and MapReduce Version 2 (MRv2) – Enabling Processing

Transition between versions: key differences between Hadoop 1 and Hadoop 2
Deploying YARN: setting up the next-generation Hadoop computation framework
Designing with MRv2: strategies for optimising data processing tasks
Resource management in YARN: dynamic resource allocation and management
MapReduce on YARN: detailed analysis of a job’s lifecycle
Migration guidelines: ensuring a smooth transition from MRv1 to MRv2

Strategic Planning of a Hadoop Cluster

Optimal hardware selection: understanding server specifications for various Hadoop workloads
Choosing the right operating system: recommendations for stability and performance
Performance tuning: kernel adjustments for optimised operations
Workload analysis: determining hardware and software requirements based on usage patterns
Diverse ecosystem: overview of complementary components to enhance Hadoop
Storage considerations: JBOD vs RAID, disk sizing, and other factors
Network planning for Hadoop: ensuring bandwidth and fault tolerance

Practical Cluster Installation and Administration

Ensuring fault tolerance: techniques for maintaining uptime during failures
Logging mechanisms in Hadoop: setup, analysis, and interpretation of logs
Hadoop health checks: tools and strategies for monitoring cluster health
Cluster management tools: introduction to platforms like Ambari
Hadoop ecosystem on CDH 5: setting up components like Impala, Flume, and Hive

Resource Management – Achieving Maximum Efficiency

Overview of Hadoop schedulers: understanding their role in resource management
FIFO scheduler in detail: how resources are allocated sequentially within the cluster
Fair and Capacity schedulers: ensuring efficient and priority-based resource allocation

Monitoring, Logging, and Troubleshooting

Metrics in Hadoop: using built-in tools for performance analysis
Web interfaces for monitoring: navigating and interpreting NameNode and JobTracker interfaces
Daemon monitoring: tools and techniques to ensure daemon functionality
Monitoring CPU and memory: optimisation techniques to enhance system performance
Log analysis: reading, managing, and deriving insights from Hadoop logs

Hands-on learning with expert instructors at your location for organizations.