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Course Overview
By 2025, the global data volume is expected to exceed 180 zettabytes—much of it requiring real-time insights. Amazon Redshift is a cornerstone of modern data analytics solutions, and this course teaches you how to harness its full power.
In this hands-on, instructor-led AWS course, you’ll learn to design, optimize, and scale data analytics solutions using Amazon Redshift. You’ll explore Redshift’s architecture, load and model data, run complex queries, and extend your reach with Redshift Spectrum and federated queries. Each module includes practical labs to reinforce skills you can apply in real cloud environments.
Who Should Attend
This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
Course Objectives
This course prepares you to build scalable, cost-effective, and performance-optimized data analytics solutions on Amazon Redshift. You’ll gain hands-on experience loading, querying, and securing large datasets, and learn to integrate Redshift into broader AWS analytics workflows.
By the end of the course, you will be able to:
- Understand Amazon Redshift architecture, key components and node types
- Load, model, and query data with Redshift SQL
- Use Redshift Spectrum and federated queries for external access
- Apply cost management to enhance Redshift performance and efficiency
Course Outline
Module A: Overview of Data Analytics and the Data Pipeline
- Explore common use cases for data analytics
- Understand how the data pipeline supports analytics at scale
Module 1: Using Amazon Redshift in the Data Analytics Pipeline
- Learn why Amazon Redshift is used for cloud data warehousing
- Get an overview of Amazon Redshift’s core capabilities
Module 2: Introduction to Amazon Redshift
- Understand the architecture and components of Amazon Redshift
- Interactive Demo 1: Navigate the Amazon Redshift console
- Explore key Redshift features and functionalities
- Practice Lab 1: Load and query data in a Redshift cluster
Module 3: Ingestion and Storage
- Discover methods for ingesting data into Amazon Redshift
- Interactive Demo 2: Connect to a Redshift cluster using a Jupyter notebook with the Data API
- Learn how Redshift handles data distribution and storage
- Interactive Demo 3: Work with semi-structured data using the SUPER data type
- Explore how Redshift supports efficient querying across storage types
- Practice Lab 2: Analyze data using Amazon Redshift Spectrum
Module 4: Processing and Optimizing Data
- Perform data transformations using SQL in Amazon Redshift
- Apply advanced querying techniques for complex analytics workloads
- Practice Lab 3: Transform and analyze data in Redshift
- Manage resources with workload management (WLM) features
- Interactive Demo 4: Apply mixed workload management settings
- Automate and optimize performance in Redshift environments
- Interactive Demo 5: Resize clusters from dc2.large to ra3.xlplus
Module 5: Security and Monitoring of Amazon Redshift Clusters
- Implement best practices for securing your Redshift cluster
- Monitor cluster performance and troubleshoot issues effectively
Module 6: Designing Data Warehouse Analytics Solutions
- Review real-world data warehouse scenarios
- Activity: Design a modern analytics workflow using Redshift
Module B: Developing Modern Data Architectures on AWS
- Examine patterns for building modern data architectures on AWS
- Understand how Redshift fits into broader analytics and data lake ecosystems
- Explore how Redshift integrates with machine learning tools like Amazon SageMaker for predictive insights