Building Data Analytics Solutions Using Amazon Redshift

In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.                                                                  

Compare the features and benefits of data warehouses, data lakes, and modern data architectures

• Design and implement a data warehouse analytics solution
• Identify and apply appropriate techniques, including compression, to optimize data storage
• Select and deploy appropriate options to ingest, transform, and store data
• Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
• Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
• Secure data at rest and in transit
• Monitor analytics workloads to identify and remediate problems
• Apply cost management best practices
• In this course, you will learn to:

Students with a minimum one-year experience managing data warehouses will benefit from this course. We recommend that attendees of this course have:

Completed either AWS Technical Essentials or Architecting on AWS

• Completed Building Data Lakes on AWS
• This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.