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HADOOP is 100% open source Java‐based programming framework that supports the processing of large data sets in a distributed computing environment. HADOOP framework divides the data into smaller chunks and stores each part of the data in the separate node within the cluster..Become Hadoop Administrator by attending these leading training courses that teach everything about Hadoop Cluster including planning & deployment, monitoring, performance tuning, security using Kerberos, HDFS high availability using Quorum Journal Manager (QJM) and Oozie, Hcatalog/Hive administration. Your knowledge and skills are upgraded with these training & certification classes to become a successful Hadoop Administrator. The fundamental concepts of Apache Hadoop and Hadoop Cluster are covered in details. Theory and hands-on practical exercises are intricately woven in the curriculum design to give a complete job-oriented Hadoop Administration training.

Module List

  • HADOOP Architecture
  • Map Reduce Architecture
  • BHADOOP Administrative Tasks
  • ACL (Access control list) Upgrading HADOOP
  • Hive Architecture
  • Pig Architecture
  • SQOP Architecture
  • Mini Project / POC (Proof of Concept)


    • How to become Hadoop Administrator? 
    • What is Hadoop? 
    • Significance of Hadoop 
    • Core Elements of Hadoop 
    • Basic concepts of Hadoop

    • What is HDFS (Hadoop Distributed File System)? 
    • Reading files in HDFS 
    • Writing Files in HDFS 
    • Need for NameNode 
    • Security features in HDFS 
    • How to use Namenode Web UI? 
    • How to use Hadoop File Shell?

    • Getting Data from External Sources 
    • Using Fume to load External data? 
    • Getting Data from Relational Databases 
    • Using Sqoop to load Relational Databases 
    • What is REST Interface? 
    • Techniques to Import Data

    • What is MapReduce? 
    • Basic Concepts of MapReduce

    • What is YARN Cluster? 
    • Architecture of YARN Cluster 
    • Allocation of Resources 
    • Recovery Techniques in case of Failure

    • What are the various deployment techniques for Hadoop? 
    • How to install Hadoop? 
    • Installation Tips for Hadoop 
    • Installing Hive 
    • Installing Impala 
    • Installing Pig

    • Basic Hadoop Configurations 
    • How to configure HDFS? 
    • How to configure YARN? 
    • How to configure MapReduce? 
    • Configuring Hadoop Log 
    • Configuring Hive 
    • Configuring Impala 
    • Configuring Pig

    • Overview of Hadoop Client 
    • Installation of Hadoop Clients 
    • Configurations of Hadoop Clients 
    • How to install and configure Hue? 
    • How to authenticate and authorize Hue?

    • Advanced Configurations 
    • How to configure Hadoop Ports? 
    • How to include hosts explicitly? 
    • How to exclude hosts explicitly? 
    • Rack Awareness HDFS Configuration

    • Significance of Security in Hadoop 
    • Main Security Concepts in Hadoop 
    • Kerberos 
    • How to work with Kerberos? 
    • Using Kerberos to secure Hadoop

    • What are Hadoop Jobs? 
    • How to manage a running Hadoop Job? 
    • How to schedule Hadoop Jobs? 
    • What is FairScheduler? 
    • How to configure FairScheduler to schedule Hadoop Jobs?

    • Basic Hadoop Monitoring Techniques 
    • Basic Troubleshooting techniques in Hadoop 
    • How to correct misconfigurations in Hadoop?

Training Advantages
35 contact hours
Industry Case Studies
Industry case studies
Real time training


    With the emergence of Hadoop, the demand for professionals skilled in Hadoop Administration has also increased. Being skilled in Hadoop Administration means brighter career, salary and more job opportunities.

    Amazon Web Services, IBM, Hortonworks, Cloudera, Intel, Microsoft, Pivotal, Twitter, Salesforce, AT&T Stumbleupon, Ebay, Yahoo, Facebook, Hulu etc.

    According to Google trends, there is an exponential growth of Hadoop Jobs. Various job portals list several Hadoop jobs such as 11000+ jobs on Indeed, 12000+ on Simplyhired, 4500+ on LinkedIn and 8000+ on Recruit.net etc.

    There are three modes in which Hadoop can be used:

    • Standalone/Local mode which works on single Java virtual machine and don’t use distributed file system. It is largely used to run MapReduce program only.
    • Pseudo-distributed mode in which all daemons runs on single machine.
    • Fully distributed mode which is used by enterprises for development and production.

    Java 1.6.x or higher, preferably from Sun. Linux and Windows are the supported operating systems but BSD, Mac OS/X and OpenSolaris are known to work.