Overview:

Take your knowledge to the next level with Cloudera’s Administrator Training and Certification. Cloudera University’s four-day administrator training course 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, this training course is the best preparation for the real-world challenges faced by Cloudera administrators.

Delivery Method and Course Duration:

OnDemand: 365 days

Classroom: 4 days

Objectives:

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

• The internals of YARN, MapReduce, 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

Intended Audience & Prerequisites:

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

Course outlines:

1.      Introduction

2.      The Case for Apache Hadoop

• Why Hadoop?
• Fundamental Concepts
• Core Hadoop Components

3.      Hadoop Cluster Installation

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

4.      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

5.      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

6.      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

7.      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

8.      Planning Your Hadoop Cluster

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

9.      Installing and Configuring Hive, Impala, and Pig

• Hive
• Impala
• Pig

10.  Hadoop Clients Including Hue

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

11.  Advanced Cluster Configuration

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

12.  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

13.  Managing Resources

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

14.  Cluster Maintenance

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

15.  Cluster Monitoring and Troubleshooting

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

16.  Conclusion