Java Parallel Computation on Hadoop

Learn to write real, working data-driven Java programs that can run in parallel on multiple machines by using Hadoop.

Build your essential knowledge with this hands-on, introductory course on the Java parallel computation using the popular Hadoop framework:

What you’ll learn

  • Know the essential concepts about Hadoop.
  • Know how to setup a Hadoop cluster in pseudo-distributed mode.
  • Know how to setup a Hadoop cluster in distributed mode (3 physical nodes).
  • Know how to develop Java programs to parallelize computations on Hadoop.

Course Content

  • Overview.
  • Background knowledge about Hadoop.
  • The Hadoop Ecosystem.
  • Get Ready in pseudo-distributed mode.
  • Get Ready in distributed mode.
  • Large-scale Word Counting.
  • Large-scale Data Sorting.
  • Large-scale Pattern Searching.
  • Large-scale Item Co-occurrence.
  • Large-scale Inverted Index.
  • Large-scale Data Aggregation.
  • Data Preparation.

Java Parallel Computation on Hadoop

Requirements

  • An understanding of the Java programming language.
Description

Build your essential knowledge with this hands-on, introductory course on the Java parallel computation using the popular Hadoop framework:

– Getting Started with Hadoop

– HDFS working mechanism

– MapReduce working mecahnism

– An anatomy of the Hadoop cluster

– Hadoop VM in pseudo-distributed mode

– Hadoop VM in distributed mode

– Elaborated examples in using MapReduce

Learn the Widely-Used Hadoop Framework

Apache Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware. Hadoop is an Apache top-level project being built and used by a global community of contributors and users. It is licensed under the Apache License 2.0.

All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines, or racks of machines) are common and thus should be automatically handled in software by the framework. Apache Hadoop’s MapReduce and HDFS components originally derived respectively from Google’s MapReduce and Google File System (GFS) papers.

Who are using Hadoop for data-driven applications?

You will be surprised to know that many companies have adopted to use Hadoop already. Companies like Alibaba, Ebay, Facebook, LinkedIn, Yahoo! is using this proven technology to harvest its data, discover insights and empower their different applications!

Contents and Overview

As a software developer, you might have encountered the situation that your program takes too much time to run against large amount of data. If you are looking for a way to scale out your data processing, this is the course designed for you. This course is designed to build your knowledge and use of Hadoop framework through modules covering the following:

– Background about parallel computation

– Limitations of parallel computation before Hadoop

– Problems solved by Hadoop

– Core projects under Hadoop – HDFS and MapReduce

– How HDFS works

– How MapReduce works

– How a cluster works

– How to leverage the VM for Hadoop learning and testing

– How the starter program works

– How the data sorting works

– How the pattern searching

– How the word co-occurrence

– How the inverted index works

– How the data aggregation works

– All the examples are blended with full source code and elaborations

Come and join us! With this structured course, you can learn this prevalent technology in handling Big Data.

Who this course is for:
  • IT Practitioners
  • Software Developers
  • Software Architects
  • Programmers
  • Data Analysts
  • Data Scientists
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