Java Parallel Computation on Hadoop
Java Parallel Computation on Hadoop, available at $39.99, has an average rating of 4.35, with 43 lectures, based on 115 reviews, and has 14933 subscribers.
You will learn about 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 This course is ideal for individuals who are IT Practitioners or Software Developers or Software Architects or Programmers or Data Analysts or Data Scientists It is particularly useful for IT Practitioners or Software Developers or Software Architects or Programmers or Data Analysts or Data Scientists.
Enroll now: Java Parallel Computation on Hadoop
Summary
Title: Java Parallel Computation on Hadoop
Price: $39.99
Average Rating: 4.35
Number of Lectures: 43
Number of Published Lectures: 43
Number of Curriculum Items: 43
Number of Published Curriculum Objects: 43
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- IT Practitioners
- Software Developers
- Software Architects
- Programmers
- Data Analysts
- Data Scientists
Target Audiences
- IT Practitioners
- Software Developers
- Software Architects
- Programmers
- Data Analysts
- Data Scientists
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.
Course Curriculum
Chapter 1: Overview
Lecture 1: Welcome!
Chapter 2: Background knowledge about Hadoop
Lecture 1: Existing Technical Limitations
Lecture 2: Requirements for the new approach
Lecture 3: Hadoop solving the limitations
Chapter 3: The Hadoop Ecosystem
Lecture 1: Overview of HDFS
Lecture 2: Overview of MapReduce
Lecture 3: Overview of Hadoop clusters
Chapter 4: Get Ready in pseudo-distributed mode
Lecture 1: Cloudera VM
Lecture 2: Demonstration: Using the VM
Lecture 3: Shared Folders between your host OS and VM
Lecture 4: Tips about Shared Folders
Lecture 5: Accessing HDFS
Lecture 6: Running MapReduce
Lecture 7: Demonstration: Accessing HDFS
Lecture 8: Demonstration: Running MapReduce
Lecture 9: Demonstration: Web Console for HDFS
Lecture 10: Demonstration: Web Console for MapReduce
Chapter 5: Get Ready in distributed mode
Lecture 1: About the Environment
Lecture 2: Setup the Master node – Exercise Manual
Lecture 3: Setup the Slave node – Exercise Manual
Lecture 4: Start the Master node – Exercise Manual
Lecture 5: Start the Slave node – Exercise Manual
Chapter 6: Large-scale Word Counting
Lecture 1: The Problem and Design
Lecture 2: Demonstration: Develop and Run the program
Lecture 3: Word Counting – Source Code
Chapter 7: Large-scale Data Sorting
Lecture 1: The Problem and Design
Lecture 2: Demonstration: Develop and Run the program
Lecture 3: Data Sorting – Source Code
Chapter 8: Large-scale Pattern Searching
Lecture 1: The Problem and Design
Lecture 2: Demonstration: Develop and Run the program
Lecture 3: Pattern Searching – Source Code
Chapter 9: Large-scale Item Co-occurrence
Lecture 1: The Problem and Design
Lecture 2: Demonstration: Develop and Run the program
Lecture 3: Item Co-occurrence – Source Code
Chapter 10: Large-scale Inverted Index
Lecture 1: The Problem and Design
Lecture 2: Demonstration: Develop and Run the program
Lecture 3: Inverted Index – Source Code
Chapter 11: Large-scale Data Aggregation
Lecture 1: The Problem and Design
Lecture 2: Demonstration: Develop and Run the program
Lecture 3: Data Aggregation – Source Code
Chapter 12: Data Preparation
Lecture 1: Dataset 0
Lecture 2: Dataset 1
Lecture 3: Dataset 2
Instructors
-
Ivan Ng
Instructor on Emerging Technologies -
Frahaan Hussain
CEO and Lead Developer at Sonar Systems
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 9 votes
- 3 stars: 22 votes
- 4 stars: 41 votes
- 5 stars: 42 votes
Frequently Asked Questions
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