Big data hadoop admin

Big data describes a holistic information management strategy that includes and integrates
many new types of data and data management alongside traditional data.


Courses Overview

Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Hadoop Cluster- Single and Multi node, Hadoop 2.x, Flume, Sqoop, Map-Reduce, PIG, Hive, Hbase, Zookeeper, Oozie etc. will be covered in the course. This course is designed for professionals aspiring to make a career in Big Data Analytics using Hadoop Framework. Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. Other professionals who are looking forward to acquire a solid foundation of Hadoop Architecture can also opt for this course.

  • Understanding Big data
  • Collecting and cleaning data
  • Traditional approach for processing and its challenges
  • Big data vs Hadoop
  • Hadoop overview
  • Hadoop components
  • Hadoop distributions
  • Getting started
  • What is HDFS
  • What is Map Reduce
  • Hadoop stack
  • Hands On Hadoop setup and basic operations
  • HDFS explained
  • High availability
  • Federation
  • Architecture
  • File system Shell
  • Hands On
  • Hands On
  • Map Reduce flow
  • Hello World
  • Map Reduce API concepts
  • Mapper
  • Reducer
  • Other components combiner, partitioner, shuffle/sort
  • Hadoop 1.x vs 2.x
  • Hadoop streaming API
  • Hands on with Eclipse
  • Architecture
  • Scheduler
  • Resource Manager (RM)
  • RM HA
  • YARN commands
  • Hands On with YARN applications
  • RDBMS interaction using Sqoop
  • Workflow management using Oozie
  • Back office jobs with Zookeeper
  • Hands On with actual data sets
  • Unstructured data using PIG
  • Structured data mining using hive
  • Hands On with actual data sets
  • Problem with SQL Database
  • Introduction to NOSQL
  • Hands On Exercises
  • Introduction to HBASE
  • Column Families
  • Delving deeper into HBASE
  • HBASE Architecture
  • HBASE Hands-On Exercises
  • More about ToolRunner
  • Testing with MRUnit
  • Reducing Intermediate Data With Combiners
  • The configure and close methods for Map/Reduce Setup and Teardown
  • Writing Partitioners for Better Load Balancing
  • Hands-On Exercise
  • Directly Accessing HDFS
  • Using the Distributed Cache
  • Debugging MapReduce Code
  • Using LocalJobRunner Mode for Easier Debugging
  • Retrieving Job Information with Counters
  • Logging
  • Splittable File Formats
  • Determining the Optimal Number of Reducers
  • Map-Only MapReduce Jobs
  • Hands-On Exercise
  • Map-Side Joins
  • The Secondary Sort
  • Reduce-Side Joins
  • Hands-On Exercise

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