The course is part of these learning paths
See 2 moreThis course explores the cost metrics associated with the Amazon Relational Database Service, known as RDS. Minimizing cloud spend is always a priority when architecting and designing your cloud solutions, and care should be taken to understand where your costs come from and the steps you can take to reduce them.
This course looks at each of the components associated with RDS that incur a cost and how those costs are broken down. It looks at on-demand instances, reserved instances, database storage & I/Os, backup storage, backtrack storage, snapshot export, and data transfer.
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Learning Objectives
- Understand the different database instance purchasing options and payment plans
- Learn about primary storage and I/O pricing options
- Explore the costs associated with backup storage and backtrack storage
- Learn about the pricing for snapshot exports and data transfers
Intended Audience
This course is intended for anyone responsible for designing, operating, and optimizing AWS Database solutions. It would also be advantageous for individuals planning to take the AWS Certified Database - Specialty exam.
Prerequisites
To get the most from this course, you should have a basic understanding of the AWS global infrastructure. It would be beneficial, but not essential, to have a basic awareness of the database engines covered in this course, i.e. Amazon Aurora, MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server.
Let me now move on to backtrack storage costs with Amazon Aurora. Backtrack is a feature that is only currently available for a MySQL-compatible Aurora database, using and is configured at the time of the database creation. Essentially, backtrack allows you to go back in time on the database to recover from an error or incident without having to perform a restore or create another DB cluster. For a deeper dive on on Backtrack storage, take a look at this AWS blog post found here: https://aws.amazon.com/blogs/aws/amazon-aurora-backtrack-turn-back-time/
As you can see from the configuration page during the Aurora database creation process, it is enabled via a checkbox and allows you to enter a number in hours of how far you would like to ‘backtrack’ to, with a maximum of 72 hours. In this example, I have entered 12 hours, and so Aurora will retain log data of all changes 12 hours as specified. The number of changes made directly relates to how much the Backtrack feature is going to cost you.
The pricing shown here is based upon a set cost per 1 million change records per hour for the London region.
So let’s look at an example. If you had built an Aurora database with a 12 hour backtrack setting like I had in my previous example that was generating 50,000 change records per hour the calculation would be as follows:
Obtain the total number of change records for your backtrack time period:
50,000 (change records/hour) x 12 hours = 600,000 change records
Calculating total costs based upon the London region:
(600,000 / 1,000,000) x $0.014 = $0.0084/hour
To help you keep an accurate record of the number of change records, you can use Amazon CloudWatch to help you monitor the number of change records that are being generated each hour.
Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data center and network infrastructure design, to cloud architecture and implementation.
To date, Stuart has created 150+ courses relating to Cloud reaching over 180,000 students, mostly within the AWS category and with a heavy focus on security and compliance.
Stuart is a member of the AWS Community Builders Program for his contributions towards AWS.
He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.
In January 2016 Stuart was awarded ‘Expert of the Year Award 2015’ from Experts Exchange for his knowledge share within cloud services to the community.
Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.