Hadoop is an open-source software framework for storing and processing big data.

HADOOP Online Training

Hadoop parallelizes data processing across many nodes (computers) in a compute cluster, speeding up large computations and hiding I/O latency through increased concurrency. Hadoop is especially well-suited to large data processing tasks (like searching and indexing) because it can leverage its distributed file system to cheaply and reliably replicate chunks of data to nodes in the cluster, making data available locally on the machine that is processing it.

Hadoop is written in Java. Hadoop programs can be written using a small API in Java or Python. Hadoop is a rapidly evolving ecosystem of components for implementing the Google MapReduce algorithms in a scalable fashion on commodity hardware. Hadoop enables users to store and process large volumes of data and analyze it in ways not previously possible with less scalable solutions or standard SQL-based approaches.

What you will learn

As an evolving technology solution, Hadoop design considerations are new to most users and not common knowledge. As part of the Dell | Hadoop solution, Dell has developed a series of best practices and architectural considerations to use when designing and implementing Hadoop solutions.

Hadoop can also run binaries and shell scripts on nodes in the cluster provided that they conform to a particular convention for string input/output. As with many other types of information technology (IT) solutions, change management and systems monitoring are a primary consideration within Hadoop. The IT operations team needs to ensure tools are in place to properly track and implement changes, and notify staff when unexpected events occur within the Hadoop environment.

  • Introduction to Big Data and Hadoop

HADOOP Online Training Course Content


  • Topics Covered in Hadoop Developer
  • Introduction to Big Data and Hadoop
  • Hadoop ecosystem concepts
  • Hadoop MapReduce concepts and features
  • Developing MapReduce applications
  • Pig concepts
  • Hive concepts
  • Real-time queries with Impala
  • Real life use cases
  • Introduction to Big Data and Hadoop
  • What is Big Data?
  • What is Hadoop?
  • Why Hadoop?
  • History of Hadoop
  • Hadoop ecosystem
  • HDFS
  • MapReduce
  • Install Hadoop
  • Single Node Hadoop Setup
  • Test run Hadoop commands
  • Hands on Understanding the Cluster
  • Writing files to HDFS
  • Reading files from HDFS
  • Rack awareness 5 daemons
  • Deep Dive into  MapReduce
  • Architecture  MapReduce
  • Developing the MapReduce Application
  • Data Types
  • File Formats
  • Explain the Driver, Mapper and Reducer code


  • Before MapReduce
  • MapReduce overview
  • Word count problem
  • Word count flow and solution
  • MapReduce flow
  • Configuring development environment – Eclipse
  • Writing unit test
  • Running locally
  • Running on cluster
  • Hands on Monitoring MapReduce Job Status
  • Job submission
  • Job initialization
  • Task assignment
  • Job completion
  • Job scheduling
  • Job failures
  • Shuffle and sort
  • Hands on MapReduce Types and Formats
  • Hands on MapReduce Features Sorting
  • Joins – Map side and reduce side
  • MapReduce combiner
  • MapReduce partitioner
  • MapReduce distributed cache
  • Hands-on Sqoop Fundamentals Concepts
  • Hands-on Flume Fundamentals Concepts
  • Hands-on Case Studies
  • Real time use case explanation

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