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Introduction to Administering Hadoop Clusters

Course Summary

The Introduction to Administering Hadoop Clusters training course is designed to demonstrate the key aspects of installing and maintaining a Hadoop cluster in various forms.

The course begins by examining how to operate the Hadoop Distributed File System (HDFS) file system and MapReduce I/O framework as complementary technologies. Next, it explores how to configure and monitor processes to manage storage and job tasks. The course concludes with an analysis of supplementing clusters with enhanced storage features and client tools.

Purpose
 Learn how to set, configure, and administer Hadoop.
Audience
 System administrators, developers, and DevOps engineers creating Big Data solutions using Hadoop.
Role
Software Developer - System Administrator
Skill Level
Intermediate
Style
Hack-a-thon - Learning Spikes - Workshops
Duration
4 Days
Related Technologies
Java | Big Data Training | Hadoop | Apache

 

Productivity Objectives
  • Describe the HDFS file system and MapReduce I/O frameworks
  • Configure and monitor storage management processes and tasks
  • Add network topology awareness to a cluster
  • Configure a highly-available storage system
  • Supplement clusters with enhanced storage features and client tools

What You'll Learn:

In the Introduction to Administering Hadoop Clusters training course, you'll learn:
  • Hadoop Concepts
    • Operate on large data sets
    • Parallelize to improve performance
    • Use large block sizes
    • Distribute and replicate data
    • Assign code to data
    • Compensate for Node failures and recoveries
    • Add Nodes for better performance
    • Utilize virtualization for rapid deployment
  • Installing a Hadoop Cluster
    • Understand the NameNode
    • Understand the secondary NameNode
    • Understand the Data Node
    • Understand the JobTracker
    • Understand the TaskTracker
  • Understanding the MapReduce Flow
    • Map data
    • Shuffle and sorting
    • Reduce data
    • Utilize the "Write Once, Read Many" approach
  • Reviewing Job Performance
    • Interpret console output
    • Navigate the JobTracker UI
    • Use TaskTracker logs
  • Configuring Nodes
    • Understand Hadoop property management
    • Manage core properties
    • Manage HDFS properties
    • Manage MapReduce properties
    • Manage worker properties
    • Restrict job property changes
  • Supporting Federated & HA File Systems
    • Restore NameNode services
    • Protect NameNode metadata
    • Use Federated NameNodes
    • Understand the NameNode HA model
      • Configure a Federated HDFS system
      • Create defensive copies of NameNode metadata
  • Controlling Jobs and Resources
    • Schedule jobs
    • Understand the FairScheduler
    • Orchestrate Workflows
  • Importing Legacy and Continuous Data
    • Utilize Sqoop
    • Use Flume
    • Understand Hive, Impala, and HBase
  • Maintaining HDFS
    • Check block integrity
    • Balance data across nodes
    • Use HDFS Safe Mode
    • Address other HDFS systems
    • Restrict Node additions
  • Installing Ecosystem Packages
    • Install Pig
    • Install Hive
    • Review HBase requirements
  • Improving Hadoop Security
    • Review authentication & authorization
    • Analyze Hadoop's authorization model
    • Understand kerberos architecture
    • Review Kerberos implementation options
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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