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Hadoop Administration training course

Install, configure, and manage the Apache Hadoop platform and its associated ecosystem, and build a Hadoop solution for Big Data

4.8 out of 5 average

(see feedback)
  • Train a team
  • Customised content
  • Free tasters
  • Next on 28 Feb
  • £1,600.00+vat
  • Live / Virtual

JBI training course London UK

  • Gain an introduction to data storage and processing 
  • Architect a Hadoop solution to meet your business requirements
  • Install and build a Hadoop cluster capable of processing large data
  • Configure and tune the Hadoop environment to ensure high throughput and availability
  • Allocate, distribute and manage resources
  • Monitor the file system, job progress and overall cluster performance


In this Hadoop architecture and administration training course, you will gain the skills to install, configure and manage the Apache Hadoop platform and its associated ecosystem.

You will learn how to build a Hadoop solution that satisfies your business requirements.

JBI training course London UK
JBI training course London UK

IT professionals looking to learn about how to architect and administer Apache Hadoop and clusters for Big Data


4.8 out of 5 average

JBI training course London UK

Introduction to Data Storage and Processing

  • Installing the Hadoop Distributed File System (HDFS)
  •     Defining key design assumptions and architecture
  •     Configuring and setting up the file system
  •     Issuing commands from the console
  •     Reading and writing files
  • Setting the stage for MapReduce
  •     Reviewing the MapReduce approach
  •     Introducing the computing daemons
  •     Dissecting a MapReduce job

Defining Hadoop Cluster Requirements

  • Planning the architecture
  •     Selecting appropriate hardware
  •     Designing a scalable cluster
  • Building the cluster
  •     Installing Hadoop daemons
  •     Optimising the network architecture

Configuring a Cluster

  • Preparing HDFS
  •     Setting basic configuration parameters
  •     Configuring block allocation, redundancy and replication
  • Deploying MapReduce
  •     Installing and setting up the MapReduce environment
  •     Delivering redundant load balancing via Rack Awareness

Maximising HDFS Robustness

  • Creating a fault–tolerant file system
  •     Isolating single points of failure
  •     Maintaining High Availability
  •     Triggering manual failover
  •     Automating failover with Zookeeper
  • Leveraging NameNode Federation
  •     Extending HDFS resources
  •     Managing the namespace volumes
  • Introducing YARN
  •     Critiquing the YARN architecture
  •     Identifying the new daemons

Managing Resources and Cluster Health

  • Allocating resources
  •     Setting quotas to constrain HDFS utilisation
  •     Prioritising access to MapReduce using schedulers
  • Maintaining HDFS
  •     Starting and stopping Hadoop daemons
  •     Monitoring HDFS status
  •     Adding and removing data nodes
  • Administering MapReduce
  •     Managing MapReduce jobs
  •     Tracking progress with monitoring tools
  •     Commissioning and decommissioning compute nodes

Maintaining a Cluster

  • Employing the standard built–in tools
  •     Managing and debugging processes using JVM metrics
  •     Performing Hadoop status checks
  • Tuning with supplementary tools
  •     Assessing performance with Ganglia
  •     Benchmarking to ensure continued performance

Extending Hadoop

  • Simplifying information access
  •     Enabling SQL–like querying with Hive
  •     Installing Pig to create MapReduce jobs
  • Integrating additional elements of the ecosystem
  •     Imposing a tabular view on HDFS with HBase
  •     Configuring Oozie to schedule workflows

Implementing Data Ingress and Egress

  • Facilitating generic input/output
  •     Moving bulk data into and out of Hadoop
  •     Transmitting HDFS data over HTTP with WebHDFS
  • Acquiring application–specific data
  •     Collecting multi–sourced log files with Flume
  •     Importing and exporting relational information with Sqoop
  • Planning for Backup, Recovery and Security
  •     Coping with inevitable hardware failures
  •     Securing your Hadoop cluster
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