Data Warehousing on AWS – Intensive Training («AWSA05»)

• Describing Amazon Redshift architecture and its roles in a modern data architecture
• Designing and implementing a data warehouse in the cloud using Amazon Redshift
• Identifying and loading data into an Amazon Redshift data warehouse from a variety of sources
• Analyzing data using SQL QEV2 notebooks
• Designing and implementing a disaster recovery strategy for an Amazon Redshift data warehouse
• Performing maintenance and performance tuning on an Amazon Redshift data warehouse
• Securing and managing access to an Amazon Redshift data warehouse
• Sharing data between multiple Redshift clusters in an organization
• Orchestrating workflows in the data warehouse using AWS Step Functions state machines
• Creating an ML model and configure predictors using Amazon Redshift ML

Note: This course is additionally enriched with content from the course Building Data Analytics Solutions 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.

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

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

This course is intended for the following job roles:

• Data Analytics

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

• Familiarity with relational databases and database design concepts

and have attended the following courses (or have equivalent knowledge):