Course Overview
This intermediate-level course focuses on building data analytics solutions using Amazon Redshift, a cloud data warehouse service. It covers the data analytics pipeline, including data collection, ingestion, cataloging, storage, processing, querying, and optimization. Learners explore Amazon Redshift architecture, data distribution, semi-structured data, Amazon Redshift Spectrum, resource management, and workload optimization.
The course also covers security and monitoring of Amazon Redshift clusters and the design of data warehouse analytics workflows. Learners examine how Amazon Redshift integrates with data lakes and modern data architectures to support analytics and machine learning workloads, while applying security, performance, and cost-management practices.
Course Objective
- 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 techniques, including compression, to optimize data storage
- Select and deploy appropriate options to ingest, transform, and store data
- Choose appropriate instance and node types, clusters, auto scaling, and network topology for a business use case
- Understand how data storage and processing affect analysis and visualization mechanisms
- Secure Amazon Redshift data at rest and in transit
- Monitor analytics workloads and identify and remediate problems
- Apply cost-management best practices to Amazon Redshift operations
Pre-requisites
- Students with a minimum of one year of experience managing data warehouses will benefit from this course.
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS
Course Curriculum
