Cloudera Administrator Training for Apache Hadoop Training in Australia

Cloudera University’s four-day administrator training course for Apache Hadoop provides participants with a comprehensive understanding of all the steps necessary to operate and maintain a Hadoop cluster using Cloudera Manager. From installation and configuration through load balancing and tuning, Cloudera’s training course is the best preparation for  the real-world challenges faced by Hadoop administrators.

There are no prerequisites for this course.

This course is best suited to systems administrators and IT managers who have basic Linux experience. Prior knowledge of Apache Hadoop is not required.

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

• Cloudera Manager features that make managing your clusters easier, such as aggregated logging, configuration management, resource management, reports,  alerts, and service management.
• The internals of YARN, MapReduce, Spark, and HDFS
• Determining the correct hardware and infrastructure for your cluster
• Proper cluster configuration and deployment to integrate with the data center
• How to load data into the cluster from dynamically-generated files using Flume  and from RDBMS using Sqoop
• Configuring the FairScheduler to provide service-level agreements for multiple users  of a cluster
• Best practices for preparing and maintaining Apache Hadoop in production
• Troubleshooting, diagnosing, tuning, and solving Hadoop issues

Introduction

The Case for Apache Hadoop

• Why Hadoop?
• Fundamental Concepts
• Core Hadoop Components

Hadoop Cluster Installation

• Rationale for a Cluster Management Solution
• Cloudera Manager Features
• Cloudera Manager Installation
• Hadoop (CDH) Installation

The Hadoop Distributed File System (HDFS)

• HDFS Features
• Writing and Reading Files
• NameNode Memory Considerations
• Overview of HDFS Security
• Web UIs for HDFS
• Using the Hadoop File Shell

MapReduce and Spark on YARN

• The Role of Computational Frameworks
• YARN: The Cluster Resource Manager
• MapReduce Concepts
• Apache Spark Concepts
• Running Computational Frameworks on YARN
• Exploring YARN Applications Through the Web UIs, and the Shell
• YARN Application Logs

Hadoop Configuration and Daemon Logs

• Cloudera Manager Constructs for Managing Configurations
• Locating Configurations and Applying Configuration Changes
• Managing Role Instances and Adding Services
• Configuring the HDFS Service
• Configuring Hadoop Daemon Logs
• Configuring the YARN Service

Getting Data Into HDFS

• Ingesting Data From External Sources With Flume
• Ingesting Data From Relational Databases

With Sqoop

• REST Interfaces
• Best Practices for Importing Data

Planning Your Hadoop Cluster

• General Planning Considerations
• Choosing the Right Hardware
• Virtualization Options*
• Network Considerations
• Configuring Nodes

Installing and Configuring Hive, Impala, and Pig

• Hive
• Impala
• Pig

Hadoop Clients Including Hue

• What Are Hadoop Clients?
• Installing and Configuring Hadoop Clients
• Installing and Configuring Hue
• Hue Authentication and Authorization

Advanced Cluster Configuration

• Advanced Configuration Parameters
• Configuring Hadoop Ports
• Configuring HDFS for Rack Awareness
• Configuring HDFS High Availability

Hadoop Security

• Why Hadoop Security Is Important
• Hadoop’s Security System Concepts
• What Kerberos Is and how it Works
• Securing a Hadoop Cluster With Kerberos
• Other Security Concepts

Managing Resources

• Configuring cgroups with Static Service Pools
• The Fair Scheduler
• Configuring Dynamic Resource Pools
• YARN Memory and CPU Settings
• Impala Query Scheduling

Cluster Maintenance

• Checking HDFS Status
• Copying Data Between Clusters
• Adding and Removing Cluster Nodes
• Rebalancing the Cluster
• Directory Snapshots
• Cluster Upgrading

Cluster Monitoring and Troubleshooting

• Cloudera Manager Monitoring Features
• Monitoring Hadoop Clusters
• Troubleshooting Hadoop Clusters
• Common Misconfigurations

Conclusion