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What is Apache Hadoop?

Hadoop is an open source project from Apache that has evolved rapidly into a major technology movement. It has emerged as the best way to handle massive amounts of data, including not only structured data but also complex, unstructured data as well. Its popularity is due in part to its ability to store, analyze and access large amounts of data, quickly and cost effectively across clusters of commodity hardware.

Apache Hadoop is not actually a single product but instead a collection of several components including the following:

MapReduce – A framework for writing applications that processes large amounts of structured and unstructured data in parallel across large clusters of machines in a very reliable and fault-tolerant manner.

Hadoop Distributed File System (HDFS) – A reliable and distributed Java-based file system that allows large volumes of data to be stored and rapidly accessed across large clusters of commodity servers.

Hive – Built on the MapReduce framework, Hive is a data warehouse that enables easy data summarization and ad-hoc queries via an SQL-like interface for large datasets stored in HDFS.

Pig – A platform for processing and analyzing large data sets. Pig consists on a high-level language (Pig Latin) for expressing data analysis programs paired with the MapReduce framework for processing these programs.

HBase – A column-oriented NoSQL data storage system that provides random real-time read/write access to big data for user applications.

ZooKeeper – A highly available system for coordinating distributed processes. Distributed applications use ZooKeeper to store and mediate updates to important configuration information.

Ambari – An open source installation lifecycle management, administration and monitoring system for Apache Hadoop clusters.

HCatalog – A table and metadata management service that provides a centralized way for data processing systems to understand the structure and location of the data stored within Apache Hadoop.

Apache Hadoop is generally not a direct replacement for enterprise data warehouses, data marts and other data stores that are commonly used to manage structured or transactional data. Instead, it is used to augment enterprise data architectures by providing an efficient and cost-effective means for storing, processing, managing and analyzing the ever-increasing volumes of semi-structured or un-structured data being produced daily.

Apache Hadoop can be useful across a range of use cases spanning virtually every vertical industry. It is becoming popular anywhere that you need to store, process, and analyze large volumes of data. Examples include digital marketing automation, fraud detection and prevention, social network and relationship analysis, predictive modeling for new drugs, retail in-store behavior analysis, and mobile device location-based marketing.

Apache Hadoop is widely deployed at organizations around the globe, including many of the world’s leading Internet and social networking businesses. At Yahoo!, Apache Hadoop is literally behind every click, processing and analyzing petabytes of data to better detect spam, predicting user interests, target ads and determine ad effectiveness. Many of the key architects and Apache Hadoop committers from Yahoo! founded Hortonworks to further accelerate development and adoption and assist organizations achieve similar business value.




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The Motivation For Hadoop

·         Problems with traditional large-scale systems

·         Requirements for a new approach

·         HADOOP History and Evolution

 Hadoop Basic Concepts

·         An Overview of Hadoop

·         The Hadoop Distributed File System

·         HADOOP EcoSystem

·         IT Architecture with HADOOP

·         Case Studies and Use Cases discussion for HADOOP

 Writing your first MapReduce Program

·         MapReduce Introduction

·         The MapReduce Program Flow

·         How MapReduce Program works

·         Examining a Sample MapReduce Program

·         Hands-On Exercise using example program

Dissecting your first MapReduce program to understand:

·         Basic MapReduce API Concepts

·         The Driver Code

·         The Mapper

·         The Reducer

·         Hadoop’s Streaming API

·         Hadoop Pipes

·         Hadoop Scaling Out

MapReduce Features:

·         Counters

·         Sorting

·         Joins

·         MapReduce Library Classes

·         Side Data Distribution

·         Input Formats

·         Output Formats

·         Hands-on exercise to understand the features

Advanced features : Hadoop API

·         Using Combiners

·         Using LocalJobRunner Mode for Faster Development

·         Reducing Intermediate Data with Combiners

·         The configure and close methods for MapReduce

·         Setup and Teardown

·         Writing Partitioners for Better Load Balancing

·         Directly Accessing HDFS

·         Using The Distributed Cache

·         Hands-On Exercise (Optional)

Practical Development Tips and Techniques

·         Testing with MRUnit

·         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

·         Implementing Multiple Mappers using ChainMapper

·         Hands-On Exercise

Introduction to HIVE, SQOOP, PIG

·         Key features of HIVE, SQOOP, PIG

·         Example Programs study and practice for

·         HIVE

·         PIG

·         SQOOP

·         MapReduce Program Integration with HIVE, PIG, SQOOP tools

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Visit Us:

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