Data Warehousing on AWS

Course Information

Course Name

AWS-DW: Data Warehousing on AWS

Duration

3 Days

Certification

AWS Certified Data Engineer – Associate

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

Audience Profile

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)

At Course Completion

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

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 warehous

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 Redshif

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

Training Delivery Notice

All AWS certification courses are conducted by certified trainers from Iverson.

Digital Methods acts as the official training partner and assists with program consultation, registration, coordination, scheduling, and administrative arrangements to ensure a smooth and professional learning experience.