Curso: Data Warehousing on AWS

Objectives

• 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.

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