3 Days
AWS Landing Page
Data Warehousing on AWS (AWS-DBDWOA)

Unlock the power of cloud-based data warehousing with the Data Warehousing on AWS course. This course equips you with the skills to design, implement, and optimize a robust data warehousing solution using Amazon Redshift. Explore Redshift’s architecture, best practices, and integration with AWS services. Learn about data ingestion, transformation, SQL analysis, disaster recovery, performance tuning, security, and access management. Dive into the potential of data sharing, workflow orchestration with Step Functions, and machine learning with Redshift ML.


$2,025
Duration: 3 Days
About the course

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 Objectives

This course teaches you how 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

Prerequisites

We recommend that attendees of this course have the following prerequisites:

  • 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)
Upcoming dates
May 5th-7th 2025
$2,025
or 21 NTCs
Duration: 3 Days (Live Online)
6:30AM - 2:30PM Pacific Time (US & Canada)
July 8th-10th 2025
$2,025
or 21 NTCs
Duration: 3 Days (Live Online)
6:30AM - 2:30PM Pacific Time (US & Canada)
September 9th-11th 2025
$2,025
or 21 NTCs
Duration: 3 Days (Live Online)
6:30AM - 2:30PM Pacific Time (US & Canada)
Course content


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

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

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
Who Should Attend
  • Data engineers
  • Data architects
  • Database architects
  • Database administrators
  • Database developers