Rewinding a MySQL Amazon Aurora Database with Backtrack

Lab Steps

Logging in to the Amazon Web Services Console
Connecting to the Virtual Machine using EC2 Instance Connect
Populating the Amazon Aurora Mysql Database
Simulating a Data Loss Event
Backtracking the Amazon Aurora Mysql Database
Examining Backtrack Metrics in Amazon Cloudwatch

Ready for the real environment experience?

Time Limit1h 30m


Backtrack is a feature of Amazon Aurora MySQL databases that enables you to quickly and seamlessly "rewind" your database to an earlier point in time. The primary use-case for Backtrack is to easily and quickly restore data after a data loss event in a production environment. Common causes of data loss include:

  • User error when manually running SQL queries
  • Invalid or interrupted database schema migrations
  • Misconfigured applications
  • Malicious activity

Compared to other backup and restore options, Backtrack is fast. With a traditional Point in Time Restore (PITR), where your database is restored to a specific point in time, the process can be expected to take hours on large datasets. Backtrack can complete rewinding your database in minutes.

In this lab, you will populate an Amazon Aurora MySQL database with some sample data, delete some data to simulate a data loss event, use Backtrack to restore the database, and finally, you will use Amazon CloudWatch to examine Backtrack specific metrics that are available to you.

Learning Objectives

This is a beginner level lab. Upon completion of this lab you will be able to:

  • Connect to an Amazon Aurora MySQL database using the Linux command-line
  • Execute SQL queries to populate the database and simulate a data loss event
  • Use the Backtrack feature to restore your database
  • Use Amazon CloudWatch to examine Backtrack specific metrics

Intended Audience

This lab is intended for:

  • Data Engineers
  • Database Administrators (DBAs)
  • Cloud Engineers
  • Developers


You should be familiar with:

  • Amazon Relational Database Service (RDS)
  • Structured Query Language (SQL)

The following course can be used to fulfill the prerequisites:

Familiarity with the Linux command-line will be helpful but is not required.

The following lab can be used to learn more about the Linux command-line:

Environment before
Environment after
About the Author
Learning paths2

Andrew is a Labs Developer with previous experience in the Internet Service Provider, Audio Streaming, and CryptoCurrency industries. He has also been a DevOps Engineer and enjoys working with CI/CD and Kubernetes.

He holds the Developer - Associate, Sysops Administrator - Associate, and Solutions Architect – Associate AWS certifications.