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Big Data Engineering Perspectives 

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Big Data Engineering Perspectives
 at 
Skillsoft 
Overview

A comprehensive overview of the technological landscape, best practices, and future trends in big data engineering

Duration

1 hour

Mode of learning

Online

Difficulty level

Beginner

Official Website

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Credential

Certificate

Future job roles

CRUD, .Net, CSR, Credit risk, Senior Software Developer

Big Data Engineering Perspectives
 at 
Skillsoft 
Highlights

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Big Data Engineering Perspectives
 at 
Skillsoft 
Course details

Who should do this course?

This course is intended for data engineers, IT professionals, system architects, and anyone interested in deepening their understanding of big data engineering

A basic understanding of data management concepts and experience with programming or scripting is recommended

What are the course deliverables?
Describe big data and the three v's
Recall what factors are important when considering a big data infrastructure
Recall some of the options when building a cloud based big data infrastructure
Compare the pros and cons of building an in house big data infrastructure
Recall the skill sets that individuals should have to make up the big data team
Describe the different software options that are available for big data analytics
Recall the types of analytics that can be done with the data
Compare the different types of data visualization that can be done with the analytics
Recall how large companies have used big data analytics effectively
Describe the risks involved when considering using big data as a solution
Read more
More about this course

"Big Data Engineering Perspectives" offers a deep dive into the engineering aspects of handling and processing massive datasets

Big data is a term for data sets so large that traditional data processing applications can't be used to perform meaningful analysis

 

Big Data Engineering Perspectives
 at 
Skillsoft 
Curriculum

Defining Big Data

Deciding on Big Data Needs

Building Cloud Based Infrastructures

Building On Site Infrastructures

Building Big Data Teams

Examining Software Options

Examining Analytics

Communicating Actionable Insights

Examining Real World Examples

Examining Big Data Risks

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Big Data Engineering Perspectives
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