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[NWV_103] Big Data Essentials

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Implementations of Big Data are still evolving as the industry grapples with this potentially significant technology transformation. Several definitions of the term “Big Data” exist but most of the industry agrees that the concept essentially is about ‘the ability to capture, manage, analyze and correlate large amounts of varied structured and un-structured data very quickly without too much delay. This is beyond the capability of traditional Relational Database Management Systems and needs a different approach from the perspective of Processes, Software, Hardware and Analytics. As with any new field, several approaches exist but the Hadoop ecosystem of software seems to be the de-facto industry standard for this. Key concepts will be discussed upon which Big Data is founded along with the industry landscape and motivation.

Learning Objectives

After completing this course, the student will be able to:
• Describe Big Data
• List the motivations for Big Data
• Discuss the key concepts of Big Data
• List the various Hadoop Components
• Describe Hadoop
• Discuss the Hadoop Ecosystem
• Describe MapReduce
• Explain the role of MapReduce
• Describe HDFS
• Explain the role of HDFS
• List the components of Hadoop Common
• List the key players in the Big Data industry

Intended Audience

This is a technical overview, intended for a technical audience that has knowledge of packet networking and an interest in understanding key concepts in Big Data.

Course Length

1 Day Instructor Led

Course Outlines / Knowledge Knuggets

1. Background and Motivation
1.1. Industry landscape and motivations
1.2. Analytics and Big Data
1.3. Industry standardization for Big Data
1.3.1. Hadoop
1.3.2. Other implementations
1.4. Benefits and challenges

2. Big Data
2.1. What is Big Data?
2.2. Big Data versus traditional approaches
2.2.1. RDMS
2.2.2. Grid
2.3. Enablers of Big Data
2.4. Big Data concepts
2.5. Big Data components

3. Hadoop
3.1. What is Hadoop?
3.2. History and apache
3.3. Apache Hadoop ecosystem
3.4. Hadoop implementations

4. Hadoop MapReduce
4.1. What is MapReduce?
4.2. History of MapReduce
4.3. Data analysis
4.3.1. Scheduling
4.3.2. Sorting
4.3.3. Task execution

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- Self-paced, animated, and interactive

- $150/each course for 6 month license

- Immediate online access upon purchase

- Flexible way to take training at any hour

- Online training transcript available

- Electronic certificates at completion


- Full list of courses at
  www.awardsolutions.com/elearning.pdf

CONTACT US

For any additional questions,
please contact Mr. Rod Marckese
at +1-972-664-0727 x246 or
public.training@awardsolutions.com

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