Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloudbased data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data.
Activities
This course includes presentations, group exercises, and hands-on labs.
Course objectives
In this course, you will:
Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions
Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud
Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution
Architect the data warehouse
Identify performance issues, optimize queries, and tune the database for better performance
Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket
Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse
Intended audience
This course is intended for:
Database Architects
Database Administrators
Database Developers
Data Analysts
Data Scientists
Prerequisites
We recommend that attendees of this course have:
Taken AWS Technical Essentials (or equivalent experience with AWS)
Familiarity with relational databases and database design concepts
Day 1
Module 1: Introduction to Data Warehousing
Relational databases
Data warehousing concepts
The intersection of data warehousing and big data
Overview of data management in AWS
Hands-on lab 1: Introduction to Amazon Redshift
Module 2: Introduction to Amazon Redshift
Conceptual overview
Real-world use cases
Hands-on lab 2: Launching an Amazon Redshift cluster
Module 3: Launching clusters
Building the cluster
Connecting to the cluster
Controlling access
Database security
Load data
Hands-on lab 3: Optimizing database schemas
Day 2
Module 4: Designing the database schema
Schemas and data types
Columnar compression
Data distribution styles
Data sorting methods
Module 5: Identifying data sources
Data sources overview
Amazon S3
Amazon DynamoDB
Amazon EMR
Amazon Kinesis Data Firehose
AWS Lambda Database Loader for Amazon Redshift
Hands-on lab 4: Loading real-time data into an Amazon Redshift database
Module 6: Loading data
Preparing Data
Loading data using COPY
Maintaining tables
Concurrent write operations
Troubleshooting load issues
Hands-on lab 5: Loading data with the COPY command
Day 3
Module 7: Writing queries and tuning for performance
Amazon Redshift SQL
User-Defined Functions (UDFs)
Factors that affect query performance
The EXPLAIN command and query plans
Workload Management (WLM)
Hands-on lab 6: Configuring workload management
Module 8: Amazon Redshift Spectrum
Amazon Redshift Spectrum
Configuring data for Amazon Redshift Spectrum
Amazon Redshift Spectrum Queries
Hands-on lab 7: Using Amazon Redshift Spectrum
Module 9: Maintaining clusters
Audit logging
Performance monitoring
Events and notifications
Lab 8: Auditing and monitoring clusters
Resizing clusters
Backing up and restoring clusters
Resource tagging and limits and constraints
Hands-on lab 9: Backing up, restoring and resizing clusters
