Video: .MP4, AVC, 1152×720, 30 fps | Audio: English, AAC, 44.1 KHz, 2 Ch | Duration: 1h 47m | 593 MB
Instructor: Shane Gibson
Course two of the Big Data Specialty learning path focuses on storage. In this course, we outline the key storage options for big data solutions. We determine data access and retrieval patterns, and some of the use cases that suit particular data patterns such as evaluating mechanisms for capture, update, and retrieval of catalog entries. We learn how to determine appropriate data structure and storage formats, and how to determine and optimize the operational characteristics of a Big Data storage solution.
Learning objectives
Recognize and explain big data access and retrieval patterns.
Recognize and explain appropriate data structure and storage formats.
Recognize and explain the operational characteristics of a Big Data storage solution.
Intended audience
This course is intended for students looking to increase their knowledge of the AWS storage options available for Big Data solutions.
Prerequisites
While there are no formal prerequisites for this course, students will benefit from having a basic understanding of cloud storage solutions. Our courses on AWS storage fundamentals and AWS database fundamentals will give you a solid foundation for taking this present course.
What You’ll Learn
Course Intro: What to expect from this course.
Amazon DynamoDB: How you can use Amazon DynamoDB in Big Data scenarios.
Amazon DynamoDB Reference Architecture: A real-life model using DynamoDB
Amazon Relational Database Service: A look at how Amazon RDS works and how you can use it in Big Data scenarios.
Amazon Relational Database Service Reference Architecture: A real-life model using RDS.
Amazon Redshift: An overview of Amazon Redshift works and how you can use it in Big Data scenarios.
Amazon Redshift Reference Architecture: A real-life model using Redshift.
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