Amazon Redshift is a fast, fully-managed, petabyte-scale data warehouse, and designed for high performance and analysis of information capable of storing and processing petabytes of data. Access to this data can be provided using your existing business intelligence tools, using standard SQL. It operates as a relational database management system and therefore is compatible with other RDBMS applications.

In this learning path, you will be introduced to Amazon Redshift and a number of its features to allow you to make use of this service within your production environment.

Intended Audience

This Learning Path is ideal for anyone looking to learn more about the Amazon Redshift, its capabilities, and its features.

Learning Objectives

Following this learning path you will have a better understanding of:

• The fundamentals of Amazon Redshift 
• The architecture of Amazon Redshift
• How to create an Amazon Redshift cluster
• The essentials of the COPY command in Redshift and its features
• How to load single or multiple large files into Redshift
• What upserting is and why it's important
• The key concepts of data distribution
• The three types of distribution styles
• The difference between distribution keys and sort keys
• The fundamentals of Amazon Redshift concurrency scaling
• How to set up and activate concurrency scaling
• Scaling Redshift Clusters
• How to scale Redshift both horizontally and vertically 
• The four ways a Redshift Cluster can be scaled
• Classic resize and elastic resize
• How to manage cold data in Redshift using Amazon S3
• What Amazon Redshift Spectrum is and does
• How Spectrum Queries work
• Supported data formats of Spectrum
• File optimization using Spectrum
• Amazon Redshift Spectrum Considerations

Prerequisites

To get the most from this learning path it would be advantageous to have a basic understanding of data analytic concepts, but it is not essential.

Feedback

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