Data Warehousing on AWS

Data Warehousing on AWS Course Overview

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.

Skills Gained

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

•

Who will the Course Benefit?

This course is intended for:

• - Data engineers

• - Data architects

• - Database architects

• - Database administrators

• - Database developers

Requirements

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)

•

NOTE: Course technical content is subject to change without notice.

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Course Contents

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