Data Warehousing on AWS (ANDWOA)

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

Activities

This course includes presentations, hands-on labs, and demonstrations

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)

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

This course is intended for:

• Data engineers
• Data architects
• Database architects
• Database administrators
• Database developers

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