SkillSoft Explore Course – Lambers, Inc.

IT Professional Curricula Enterprise Database Systems Solution Area Big Data Big Data Analytics

Big data analytics provides a way to turn the vast amounts of data available in today's digital world into valuable insights. For this reason, big data analytics techniques have taken a central place in many businesses' IT infrastructure. These comprise complex processes and multiple stack layers that allow you to transform raw data into visualizations that demonstrate trends or other phenomena.
Use this course to explore the basic principles and techniques of big data analytics in a business context. Go through each step of data processing to fully comprehend the big data analytics pipeline. Furthermore, explore various use cases of big data analytics through real-world examples.
When you're done with this course, you'll have a foundational comprehension of some of the technologies behind big data and how these can drive business decisions for the better.

Asset ID	it_dlbdadj_01_enus
Course Type	Video Course
Course Category	Enterprise Database Systems Solution Area

Objectives
Big Data Analytics: Techniques for Big Data Analytics discover the key concepts covered in this course describe the challenges in the current data analytics models and system designs, such as scalability, consistency, reliability, efficiency, and maintainability name and describe the role of the main layers of big data analytics, from the bottom all the way to the top specify why unstructured data comes from variable sources and describe how it moves from its origin to storage and gets further analyzed and visualized define the role of the data processing layer and specify how information captured in the previous layer is processed define the role of the data storage layer using HDFS as an example of commonly used primary data storage outline the main pillars and components of big data architecture describe batch processing, its use cases, and common reasons for using it outline how stream processing enables quick decision-making by creating actionable real-time insights define the concept of Lambda architecture and outline its use cases define the concept of Kappa architecture and outline its use cases summarize the key concepts covered in this course

Objectives

Big Data Analytics: Techniques for Big Data Analytics

discover the key concepts covered in this course
describe the challenges in the current data analytics models and system designs, such as scalability, consistency, reliability, efficiency, and maintainability
name and describe the role of the main layers of big data analytics, from the bottom all the way to the top
specify why unstructured data comes from variable sources and describe how it moves from its origin to storage and gets further analyzed and visualized
define the role of the data processing layer and specify how information captured in the previous layer is processed
define the role of the data storage layer using HDFS as an example of commonly used primary data storage
outline the main pillars and components of big data architecture
describe batch processing, its use cases, and common reasons for using it
outline how stream processing enables quick decision-making by creating actionable real-time insights
define the concept of Lambda architecture and outline its use cases
define the concept of Kappa architecture and outline its use cases
summarize the key concepts covered in this course