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.
Prerequisites
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 NOT ELIGIBLE FOR TRAINING BUNDLES.
Purpose
| Learn to build a data analytics solution using Amazon Redshift |
Audience
| Anyone who builds and manages data analytics pipelines |
Role
| Data Warehouse Engineers | Data Platform Engineers | Architects and
Operators
|
Skill level
| Intermediate |
Style
| Lectures |Demos | Labs | Discussions | Class Exercises |
Duration
| 1 day |
Related technologies
| Data Lakes | Jupyter |
Course objectives
- 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 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
