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Amazon Aurora Labs for MySQL

Welcome to the AWS workshop and lab content portal for Amazon Aurora MySQL compatible databases! Here you will find a collection of workshops and other hands-on content aimed at helping you gain an understanding of the Amazon Aurora features and capabilities.

The resources on this site include a collection of easy to follow instructions with examples, templates to help you get started and scripts automating tasks supporting the hands-on labs. These resources are focused on helping you discover how advanced features of the Amazon Aurora MySQL database operate. Prior expertise with AWS and MySQL-based databases is beneficial, but not required to complete the labs.

Overview of labs

The following labs are currently available, click on the relevant tab to see the labs for that topic:

You will need to complete the following prerequisites before running any other labs. Do this first!

# Lab Module Recommendation Overview
P1 Get started using the lab environment Required, start here Set up the lab environment and provision the prerequisite resources.
P2 Connect to the Cloud9 Desktop Required Connect to the AWS Cloud9 cloud-based integrated development environment (IDE) so you can interact with the database.
# Lab Module Recommendation Overview
R1 Create a New DB Cluster Optional Create a new Amazon Aurora MySQL DB cluster manually. This is optional, as you can also deploy the environment with a cluster provisioned automatically for you.
R2 Connect to the DB Cluster and Load Data Recommended Connect to the DB cluster and load an initial data set from S3. The initial data set may also be used in subsequent labs.
R3 Auto Scale Read Replicas Recommended This lab will demonstrate how auto scaling read replicas work.
R4 Clone a DB Cluster Recommended Clone an Aurora DB cluster and observing the divergence of the data set.
R5 Backtrack a DB Cluster Recommended Backtrack an Aurora DB cluster to fix an accidental DDL operation.
R6 Use Performance Insights Recommended Examine the performance of your DB instances using RDS Performance Insights.
R7 Test Fault Tolerance Recommended Examine the failover process in Amazon Aurora MySQL and how it can be optimized.
R8 Set up Database Activity Streams Recommended Monitor your database activity by using Database Activity Streams.
R9 Observe and Identify SQL Performance Issues Recommended Leverage Amazon Aurora MySQL ecosystem tools to detect performance issues.
R10 Analyze SQL Query Performance Recommended Troubleshoot SQL query performance using Amazon Aurora MySQL ecosystem tools.
# Lab Module Recommendation Overview
G1 Deploy a Global Database Required Create a Global Database which will span across multiple regions.
G3 Connect an Application Recommended Connect a Business Intelligence application to the Global Database.
G4 Monitor a Global Database Recommended Create an Amazon CloudWatch Dashboard to monitor the latency, replicated I/O, and the cross region replication data transfer of the Global Database.
G4 Use Write Forwarding Recommended Use a simple application to understand and experience the different consistency modes available with the Global Database Write Forwarding feature.
G5 Recover from an Unplanned Primary DB Cluster Failure Recommended Simulate a regional failure and DR scenario, and promote the secondary region in a Global Database.
G6 Fail Back a Global Database Optional Restore full operations in the original primary region for the Global Database after a failover.
# Lab Module Recommendation Overview
M1 Overview and Prerequisites Required Setup a sample schema and data for machine learning integration.
M2 Use Comprehend with Aurora Recommended Integrate Aurora with the Comprehend Sentiment Analysis API and make sentiment analysis inferences via SQL commands.
M3 Use SageMaker with Aurora Recommended Integrate Aurora with SageMaker Endpoints to infer customer churn in a data set using SQL commands.
M4 Clean up Lab Resources Recommended Clean up after the labs and remove unneeded AWS resources.
# Lab Module Recommendation Overview
S1 Create an Aurora Serverless DB Cluster Required Create a new Amazon Aurora Serverless MySQL DB cluster manually.
S2 Use Aurora Serverless with AWS Lambda Functions Recommended Connect to your DB cluster using the RDS Data API and Lambda functions.

You can also discover other exercises, labs and workshops related to Amazon Aurora on the Related Labs and Workshops page.

Lab environment at a glance

To simplify the getting started experience with the labs, we have created foundational templates for AWS CloudFormation that provision the resources needed for the lab environment. These templates are designed to deploy a consistent networking infrastructure, and client-side experience of software packages and components used in the lab.

The environment deployed using CloudFormation includes several components:

Additional software needed for labs

You do not need any special software on the computer you are using for these labs, except an up to date web browser. The templates and scripts setting up the lab environment install the following software in the lab environment for the purposes of deploying and running the labs:

  • mysql-client package. MySQL open source software is provided under the GPL License.
  • sysbench available using the GPL License.
  • test_db available using the Creative Commons Attribution-Share Alike 3.0 Unported License.
  • weather_data available using the Creative Commons Attribution-ShareAlike 4.0 International.
  • Percona's sysbench-tpcc available using the Apache License 2.0.
  • Percona Toolkit available using the Apache License 2.0.
  • Apache Superset available using the Apache License 2.0.