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Data Warehousing on AWS

Length 3 days

Course overview

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Why study this course

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.

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

Aligns to certification

AWS Certified Data Engineer - Associate

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What you’ll learn

This course is designed to teach participants how 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
Analyse 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 organisation
Orchestrate workflows in the data warehouse using AWS Step Functions state machines •
Create an ML model and configure predictors using Amazon Redshift ML

AWS at Lumify Work

Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.

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Who is the course for?

This course is intended for:

Data engineers
Data architects
Database architects
Database administrators
Database developers

Course subjects

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

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 materialised 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

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

Please note: This is an emerging technology course. Course outline is subject to change as needed.

Prerequisites

We recommend that attendees of this course have completed the following courses:

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.