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.


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)
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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
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