Hadoop Online Training | Hadoop Training from Experts

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. The Hadoop framework application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

MapReduce is a parallel programming model for writing distributed applications devised at Google for efficient processing of large amounts of data (multi-terabyte data-sets), on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. The MapReduce program runs on Hadoop which is an Apache open-source framework.

The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. It is highly fault-tolerant and is designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications having large datasets.

Hadoop Online Training Course Content

Module 01 - Hadoop Installation and Setup

Module 02 - Introduction to Big Data Hadoop and Understanding HDFS and MapReduce

Module 03 - Deep Dive in MapReduce

Module 04 - Introduction to Hive

Module 05 - Advanced Hive and Impala

Module 06 - Introduction to Pig

Module 07 - Flume, Sqoop and HBase

Module 08 - Writing Spark Applications Using Scala

Module 09 - Use Case Bobsrockets Package

Module 10 - Introduction to Spark

Module 11 - Spark Basics

Module 12 - Working with RDDs in Spark

Module 13 - Aggregating Data with Pair RDDs

Module 14 - Writing and Deploying Spark Applications

Module 15 - Project Solution Discussion and Cloudera Certification Tips and Tricks

Module 16 - Parallel Processing

Module 17 - Spark RDD Persistence

Module 18 - Spark MLlib

Module 19 - Integrating Apache Flume and Apache Kafka

Module 20 - Spark Streaming

Module 21 - Improving Spark Performance

Module 22 - Spark SQL and Data Frames

Module 23 - Scheduling/Partitioning

Module 24 - Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2

Module 25 - Hadoop Administration – Cluster Configuration

Module 26 - Hadoop Administration – Maintenance, Monitoring and Troubleshooting

Module 27 - ETL Connectivity with Hadoop Ecosystem (Self-Paced)

Module 28 - Hadoop Application Testing

Module 29 - Roles and Responsibilities of Hadoop Testing Professional

Module 30 - Framework Called MRUnit for Testing of MapReduce Programs

Module 31 - Unit Testing

Module 32 - Test Execution

Module 33 - Test Plan Strategy and Writing Test Cases for Testing Hadoop Application

Hadoop Course Introduction

Hadoop Online Training Course Content

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