Hadoop Applications Development With Admin

  • Price $400
  • Course Type Online
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Course Overview

In this Big Data Hadoop development course, you would be learning all the concepts and terminologies related to Big Data and Hadoop, such as the HDFS [Hadoop Distributed File System], MapReduce, PIG and Hive, Hbase. This course covers the importance of Big Data, how to setup Node Hadoop pseudo clusters, work with the architecture of clusters, run multi-node clusters on Amazons EMR, work with distributed file systems and operations including running Hadoop on Horton Works Sandbox and Cloudera.

By the end of this course, you would have a clear idea about Hadoop development, MapReduce concepts, using MapReduce with Hive and Pig, and know the Hadoop ecosystem, all the concepts related to the Hadoop, that should be sufficient to help you start off with Administering the Hadoop Cluster as well as Developing MapReduce Applications for Hadoop Cluster.

Pre-Requisites to Learn Hadoop:

  • A familiarity of programming in Java
  • Pretty basics of SQL & Linux Commands

Who Should Join this Course:

  • This course has been designed for people aspiring to learn and work in Big Data world using Hadoop Framework and become a Hadoop Developer. IT Freshers, Graduates/Post Graduates from other domains with knowledge on pre requisites, Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.

Trainer

Curriculum

1. Introduction to Big data and Hadoop

Topics - Understanding Big Data, Challenges in processing Big Data, 3V Characteristics (Volume, Variety and Velocity), Brief history of Hadoop, How Hadoop addresses Big Data?, Core Hadoop Daemons, Hadoop echo system, Hadoop Clusters.


2. HDFS (Hadoop Distributed File System)

Topics - HDFS Overview and Architecture, HDFS Keywords like Name Node, Data Node, Heart Beat etc, Configuring HDFS, Data Flows (Read and Write), HDFS Permissions and Security, HDFS commands, HDFS from Admin stand point, Rack Awareness


3. Map Reduce

Topics - Basics of Map Reduce, Map Reduce Data Flow, Word count Example solving, Developing a Map Reduce Application, Configuring Map Reduce, 2 ways executing Map Reduce program, Input and Output file formats, Driver, Mapper and Reducer Code walk thru, Hadoop Integration with Eclipse in Linux, Partitioners, Map Reduce Web UI, Joins, Distributed cache, Compression techniques in mapreduce


4. How Map Reduce works?

Topics - Classic Map Reduce (Map Reduce I), YARN (Map Reduce II), Shuffle and Sort, Job Chaining, Input formats – Input splits & custom file input formats, Output formats – text output, custom file output formats, Hands-on


5. Hadoop Echo System PIG

Topics - Overview of PIG, PIG Latin, Why PIG?, Loading and storing data, 21 Transformations of PIG, Local and HDFS modes of PIG, Grunt Shell, Script and Embedded modes of processing using PIG, Understanding Complex data types of PIG, Word Count using PIG, Hands-on


6. HIVE

Topics - Overview of HIVE, PIG vs HIVE, HiveQL, Managed and External Tables, LOAD vs INSERT, Views, CTAS, Partitioning, Bucketing, Dynamic partitioning vs Bucketing, OVERWRITE key word, Collection Data types in HIVE, Date type in HIVE, ORC File Format and other File Formats, Understanding SerDe, Types of Hive JOINS, Tuning Hive JOINS, Vectorization, Exploring HIVE User Defined Functions, HIVE Unions, Hands-on


7. HBASE

Topics - Overview of HBASE, NoSQL vs RDBMS, HBASE vs HDFS, HBASE Shell, CRUD with JAVA API, Hands-on


8. SQOOP

Topics - Overview, Data Ingestion mechanisms, Getting granted from MySQL, SQOOPING from MySQL, SQOOPING to MYSQL, Incremental append, working with Sqoop jobs


9. Course Highlights

Topics - Assignments, POC's, Mock Interview, Horton Works Certification covered


Trainer Details

Had around 6 years of IT Experience and more than 2 years of experience in hadoop development.

Completed hadoop online training for more than 10 batches and gave 2 corporate training's as well. am a specialist in dealing fast track batches with excellent quality in the subject delivery.

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Goals & Objectives

  • MapReduce and Hadoop fundamentals
  • Administering or Managing the Hadoop Cluster
  • Concepts associated with Big Data and Hadoop
  • Install and build up a Hadoop Cluster from scratch

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