High-performance Data Warehousing with Amazon Redshift
Amazon Redshift has an excellent performance reputation, but not by accident. This course focuses on performance and related topics such as architecture, design, configuration best practices, query plans, performance monitoring, and troubleshooting.
What you'll learn
One of the greatest selling points of Amazon Redshift is its high-performance potential. While some of these performance aspects are a natural part of the architecture, there are many others that require proper understanding and application of knowledge. In this course, High-performance Data Warehousing with Amazon Redshift, you’ll learn the keys to getting the highest performance possible out of Amazon Redshift. First, you’ll learn how to implement distribution styles across your nodes to support massively parallel processing. Next, you’ll discover many best practices produced by the AWS labs team to troubleshoot your cluster and deliver lightning-fast aggregates. Finally, you'll develop performance monitoring dashboards using Redshift features such as workload management (WLM) and AWS services such as cloud formation and cloud watch. When you're finished with this course, you'll have a foundational understanding of architecture, design, and configuration best practices in Amazon Redshift.
Table of contents
- Course and Module Introduction 5m
- Relational Database Access Patterns 3m
- Benefits of Column-oriented Storage 4m
- Compression Encodings 4m
- Redshift Clusters and Nodes 4m
- Distribution Styles and Even Distribution 4m
- Distribution Styles and Key Distribution 5m
- Distribution Styles and All Distribution 6m
- Network Architecture 3m
About the author
Currently an IT leader in Denver Colorado's FinTech sector Russ has focused on database engineering, modelling, administration, and BI since 1997. Russ is a passionate trainer and community volunteer presenting regularly at tech events and local user groups around the US. In 2019 much of Russ' focus turned to cloud based data lakes specifically on Google BigQuery, and the emerging world of large language models.