Hadoop for Developers and Administrators Treningskurs

Module 1. Introduction to Hadoop

• The Hadoop Distributed File System (HDFS)
• The Read Path and The Write Path
• Managing Filesystem Metadata
• The Namenode and the Datanode
• The Namenode High Availability
• Namenode Federation
• The Command-Line Tools
• Understanding REST Support

Module 2. Introduction to MapReduce

• Analyzing the Data with Hadoop
• Map and Reduce Pattern
• Java MapReduce
• Scaling Out
• Data Flow
• Developing Combiner Functions
• Running a Distributed MapReduce Job

Module 3. Planning a Hadoop Cluster

• Picking a Distribution and Version of Hadoop
• Versions and Features
• Hardware Selection
• Master and Worker Hardware Selection
• Cluster Sizing
• Operating System Selection and Preparation
• Deployment Layout
• Setting up Users, Groups, and Privileges
• Disk Configuration
• Network Design

Module 4. Installation and Configuration

• Installing Hadoop
• Configuration: An Overview
• The Hadoop XML Configuration Files
• Environment Variables and Shell Scripts
• Logging Configuration
• Managing HDFS
• Optimization and Tuning
• Formatting the Namenode
• Creating a /tmp Directory
• Thinking Namenode High Availability
• The Fencing Options
• Automatic Failover Configuration
• Format and Bootstrap the Namenodes
• Namenode Federation

Module 5. Understanding Hadoop I/O

• Data Integrity in HDFS  
• Understanding Codecs
• Compression and Input Splits
• Using Compression in MapReduce
• The Serialization mechanism
• File-Based Data Structures
• The SequenceFile format
• Other File Formats and Column-Oriented Formats

Module 6. Developing a MapReduce Application

• The Configuration API 
• Setting Up the Development Environment
• Managing Configuration
• GenericOptionsParser, Tool, and ToolRunner
• Writing a Unit Test with MRUnit
• The Mapper and Reducer
• Running Locally on Test Data 
• Testing the Driver
• Running on a Cluster
• Packaging and Launching a Job
• The MapReduce Web UI
• Tuning a Job

Module 7. Identity, Authentication, and Authorization

• Managing Identity
• Kerberos and Hadoop
• Understanding Authorization

Module 8. Resource Management

• What Is Resource Management?
• HDFS Quotas
• MapReduce Schedulers
• Anatomy of a YARN Application Run
• Resource Requests
• Application Lifespan
• YARN Compared to MapReduce 1
• Scheduling in YARN
• Scheduler Options
• Capacity Scheduler Configuration
• Fair Scheduler Configuration
• Delay Scheduling
• Dominant Resource Fairness

Module 9. MapReduce Types and Formats

• MapReduce Types
• The Default MapReduce Job
• Defining the Input Formats
• Managing Input Splits and Records
• Text Input and Binary Input
• Managing Multiple Inputs
• Database Input (and Output)
• Output Formats
• Text Output and Binary Output
• Managing Multiple Outputs
• The Database Output

Module 10. Using MapReduce Features

• Using Counters
• Reading Built-in Counters
• User-Defined Java Counters
• Understanding Sorting
• Using the Distributed Cache

Module 11. Cluster Maintenance and Troubleshooting

• Managing Hadoop Processes
• Starting and Stopping Processes with Init Scripts
• Starting and Stopping Processes Manually
• HDFS Maintenance Tasks
• Adding a Datanode
• Decommissioning a Datanode
• Checking Filesystem Integrity with fsck
• Balancing HDFS Block Data
• Dealing with a Failed Disk
• MapReduce Maintenance Tasks 
• Killing a MapReduce Job
• Killing a MapReduce Task
• Managing Resource Exhaustion

Module 12. Monitoring

• The available Hadoop Metrics
• The role of SNMP
• Health Monitoring
• Host-Level Checks
• HDFS Checks
• MapReduce Checks

Module 13. Backup and Recovery

• Data Backup
• Distributed Copy (distcp)
• Parallel Data Ingestion
• Namenode Metadata