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Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a
cloud-based data warehousing solution using Amazon Redshift. This course demonstrates how to ingest,
store, and transform data in the data warehouse. Topics covered include: the purpose of Amazon
Redshift, how Amazon Redshift addresses business and technical challenges, features and capabilities of
Amazon Redshift, designing a Data Warehousing Solution on AWS by applying best practices based on
the Well-Architected Framework, integration with AWS and non-AWS products and services, performance
tuning, orchestration, and securing and monitoring Amazon Redshift.
• Course level: Advanced
• Duration: 3 days
Overview
Supercharge Your Analytics with Amazon Redshift — Build, Optimize & Predict
Take your data warehouse skills to the next level with Amazon Redshift — the powerhouse behind modern, cloud-based analytics. In this hands-on course, you’ll learn how to design, implement, and optimize Redshift data warehouses that are fast, secure, and built for scale.
Go beyond the basics with real-world scenarios covering multi-source data ingestion, advanced SQL analysis, disaster recovery, performance tuning, and even machine learning inside Redshift. You’ll also master data sharing across clusters and orchestration with AWS Step Functions — skills in high demand by today’s data-driven organizations.
👉 From architecture to AI-powered insights — unlock the full potential of Amazon Redshift. Enroll now.
Activities
This course includes presentations, hands-on labs, and demonstrations.
Course objectives
In this course, you will learn to:
• Describe Amazon Redshift architecture and its roles in a modern data architecture
• Design and implement a data warehouse in the cloud using Amazon Redshift
• Identify and load data into an Amazon Redshift data warehouse from a variety of sources
• Analyze data using SQL QEV2 notebooks
• Design and implement a disaster recovery strategy for an Amazon Redshift data warehouse
• Perform maintenance and performance tuning on an Amazon Redshift data warehouse
• Secure and manage access to an Amazon Redshift data warehouse
• Share data between multiple Redshift clusters in an organization
• Orchestrate workflows in the data warehouse using AWS Step Functions state machines
• Create an ML model and configure predictors using Amazon Redshift ML Intended audience
This course is intended for:
• Data engineers
• Data architects
• Database architects
• Database administrators
• Database developers
Prerequisites
We recommend that attendees of this course have completed the following courses:
• Fundamentals of Analytics on AWS – Part 1 (Digital course)
• Fundamentals of Analytics on AWS – Part 2 (Digital course)
• Building Data Lakes on AWS (Instructor led Training)
• Building Data Analytics Solutions Using Amazon Redshift (Instructor led Training)
Course outline
Day 1
Module 1: Data Warehouse Concepts
• Modern data architecture
• Introduction to the course story
• Data warehousing with Amazon Redshift
• Amazon Redshift Serverless architecture
• Hands-On Lab: Launch and Configure an Amazon Redshift Serverless Data Warehouse
Module 2: Setting up Amazon Redshift
• Data models for Amazon Redshift
• Data management in Amazon Redshift
• Managing permissions in Amazon Redshift
• Hands-On Lab: Setting up a Data Warehouse using Amazon Redshift Serverless
Module 3: Loading Data
• Overview of data sources
• Loading data from Amazon Simple Storage Service (Amazon S3)
• Extract, transform, and load (ETL) and extract, load, and transform (ELT)
• Loading streaming data
• Loading data from relational databases
• Hands-On Lab: Populating the data warehouse
Day 2
Module 4: Deep Dive into SQL Query Editor v2 and Notebooks
• Features of Amazon Redshift Query Editor v2
• Demonstration: Using Amazon Redshift Query Editor v2
• Advanced queries
• Hands-On Lab: Data Wrangling on AWS
Module 5: Backup and Recovery
• Disaster recovery
• Backing up and restoring Amazon Redshift provisioned
• Backing up and restoring Amazon Redshift Serverless
Module 6: Amazon Redshift Performance Tuning
• Factors that impact query performance
• Table maintenance and materialized views
• Query analysis
• Workload management
• Tuning guidance
• Amazon Redshift monitoring
• Hands-On Lab: Performance Tuning the Data Warehouse
Module 7: Securing Amazon Redshift
• Introduction to Amazon Redshift security and compliance
• Authentication with Amazon Redshift
• Access control with Amazon Redshift
• Data encryption with Amazon Redshift
Auditing and compliance with Amazon Redshift
• Hands-On Lab: Securing Amazon Redshift
Day 3
Module 8: Orchestration
• Overview of data orchestration
• Orchestration with AWS Step Functions
• Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)
• Hands-On Lab: Orchestrating the Data Warehouse Pipeline
Module 9: Amazon Redshift ML
• Machine Learning Overview
• Getting started with Amazon Redshift ML
• Amazon Redshift ML workflow scenarios
• Amazon Redshift ML Usage
• Hands-On Lab: Predicting customer churn with Amazon Redshift ML
Module 10: Amazon Redshift Data Sharing
• Overview of data sharing in Amazon Redshift
• Amazon DataZone for Data as a service
Module 11: Wrap-Up
• Hands-On Lab: End of course challenge lab
Highlights
- End-to-End Data Warehouse Design and Implementation Gain hands-on experience in designing, building, and optimizing a cloud-based data warehouse using Amazon Redshift, including loading data from diverse sources and orchestrating workflows with AWS Step Functions.
- Advanced Performance, Security, and Data Sharing Capabilities Learn how to fine-tune performance, implement disaster recovery, manage secure access, and share data seamlessly across Redshift clusters within your organization.
- Integrated Machine Learning with Redshift ML Develop and deploy machine learning models directly within Amazon Redshift, enabling predictive analytics without needing to move data outside the platform.
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Pricing
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