Getting Started

Create an AWS account

In order to complete the hands-on content on this site, you'll need an AWS Account. We strongly recommend that you use a personal account or create a new AWS account to ensure you have the necessary access and that you do not accidentally modify corporate resources. Do not use an AWS account from the company you work for unless they provide sandbox accounts just for this purpose.

If you are setting up an AWS account for the first time, follow the instructions below to create an administrative IAM user account, we recommend not using your AWS account root credentials for day to day usage. If you have received credits to complete these labs follow the instructions below on adding the credits to your AWS account.

Overview of labs

The following labs are currently available, part of this instructional website.

Prerequisites

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

# Lab Module Recommendation Overview
1 Deploy Environment Required, start here Set up the lab environment and provision the prerequisite resources.
2 Connect to the Session Manager Workstation Required Connect to the EC2 based workstation using Session Manager, so you can interact with the database.
Labs for Aurora Provisioned DB clusters
# Lab Module Recommendation Overview
1 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.
2 Connect, Load Data and Auto Scale Recommended Connect to the DB cluster, load an initial data set from S3 and test read replica auto scaling. The initial data set may be used in subsequent labs.
3 Clone a DB Cluster Recommended Clone an Aurora DB cluster and observing the divergence of the data set.
4 Backtrack a DB Cluster Recommended Backtrack an Aurora DB cluster to fix an accidental DDL operation.
5 Use Performance Insights Recommended Examine the performance of your DB instances using RDS Performance Insights.
6 Test Fault Tolerance Recommended Examine the failover process in Amazon Aurora MySQL and how it can be optimized.
Labs for Aurora Serverless DB clusters
# Lab Module Recommendation Overview
1 Create an Aurora Serverless DB cluster Required Create a new Amazon Aurora Serverless MySQL DB cluster manually.
2 Use Aurora Serverless with AWS Lambda functions Recommended Connect to your DB cluster using the RDS Data API and Lambda functions.
Aurora Global Database Workshop
# Lab Module Recommendation Overview
1 Create Infrastructure Required Create a multi-region environment to use with Aurora Global Database.
2 Create Global Database Recommended Create a Global Database which will span across multiple regions.
3 Connect Application Recommended Connect a Business Intelligence application to the global database.
4 Monitor Latency Recommended Create an Amazon CloudWatch Dashboard to monitor the latency, replicated IO and the cross region replication data transfer of the global database.
5 Failover Recommended Simulate a regional failure and DR scenario.
6 Failback Optional Fail back to the original primary region.
Machine Learning with Amazon Aurora
# Lab Module Recommendation Overview
1 Overview and Prerequisites Required Setup a sample schema and data for machine learning integration.
2 Use Comprehend with Aurora Recommended Integrate Aurora with the Comprehend Sentiment Analysis API and make sentiment analysis inferences via SQL commands.
3 Use SageMaker with Aurora Recommended Integrate Aurora with SageMaker Endpoints to infer customer churn in a data set using SQL commands.
4 Cleanup Lab Resources Recommended Clean up after the labs and remove unneeded AWS resources.

You can also discover 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:

Create an IAM user (with admin permissions)

If you don't already have an AWS IAM user with admin permissions, please use the following instructions to create one:

  1. Browse to the AWS IAM console.
  2. Click Users on the left navigation and then click Add User.
  3. Enter a User Name, check the checkbox for AWS Management Console access, enter a Custom Password, and click Next:Permissions.
  4. Click Attach existing policies directly, click the checkbox next to the AdministratorAccess policy, and click Next:Review.
  5. Click Create User
  6. Click Dashboard on the left navigation and use the IAM users sign-in link to login as the admin user you just created.

Add credits (optional)

If you are doing these workshop as part of an AWS sponsored event that doesn't provide AWS accounts, you will receive credits to cover the costs. Below are the instructions for entering the credits:

  1. Browse to the AWS Account Settings console.
  2. Enter the Promo Code you received (these will be handed out at the beginning of the workshop).
  3. Enter the Security Check and click Redeem.

Additional software needed for labs

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.
  • Percona's sysbench-tpcc available using the Apache License 2.0.