Tips for Cost Efficient Data Management
Created in the early 2000s, Amazon Web Services quickly established itself as a major player in the cloud computing market. With upfront costs for in-house implementation being prohibitive for small and medium businesses, AWS offers off-premise cloud computing solutions that are scalable and affordable.
However, moving to big data can drive AWS costs up. It’s time to take control of your AWS spending for a successful transition to big data.
Understanding the unique challenges of big data
Big data refers to data assets that are challenging to compute, store and analyze with traditional tools. Due to their size, complexity and velocity, big data assets require specialized tools that can sift through terabytes or even petabytes of data and still deliver real-time insights.
How to manage AWS costs linked to big data
You can optimize AWS costs by implementing best practices for scaling or terminating instances. You should also keep in mind that storage tends to be cheaper while computing and memory assets will increase your AWS bill. Here are a few tips to help you learn how to manage AWS and big data:
Choose the right storage solution
Amazon Elastic Block Store (EBS) is an affordable file storage option for Amazon Elastic Compute Cloud (EC2) instances, but big data can drive costs up. You can manage costs by deleting EBS snapshots you no longer need. You should also run EBS volume checks to identify the snapshots you haven’t used in a while.
If you don’t mind the learning curve, Amazon S3 is a good option for affordable object storage. With the S3 Analytics tool, you can see the object data you don’t access often and move it to a different storage tier like S3 Infrequently Accessed storage. AWS has a tool for automating this process called S3 Intelligent Tiering. And for long-term object storage, you can archive your data with S3 Glacier.
Don’t run instances you don’t need
With AWS’s on-demand model, you’re billed by the hour for the EC2 instances you run. You can reduce costs by adopting Spot instances and running several smaller EC2 instances in parallel for non-production assets.
You can use AWS Instance Scheduler to terminate or pause the instances you don’t need. Additionally, the AWS Cost Explorer Resource Optimization tool can improve visibility over EC2 instances with low use. Amazon Trusted Advisor is a similar tool that helps you find Relational Database Service and Redshift instances that are underutilized.
Terminate associated assets
When you launch a new EC2 instance, AWS will create associated assets. These can keep running after you terminate the instance and increase your bill.
For new EC2 instances, don’t forget to check the box to automatically delete the associated EBS volume when you terminate the instance. You should also make sure that you delete the associated elastic IP address and elastic load balancers.
Save on licensing costs
Don’t overlook licensing costs. You can save by adopting Linux servers instead of Windows and leveraging open-source software.
Remember that AWS bills you for the resources you provision and not for what you use. It’s important to assign the right amount of resources to each instance.
Upgrading to the latest version of the AWS tools can also help you save since you can sometimes run old instances with fewer resources.
Once you know what your baseline usage looks like, consider using EC2 Reserved Instances to get a discount compared to on-demand pricing. You can also opt for a Savings Plan if you’re ready to commit to a minimal level of usage.
Use AWS tools to monitor usage
AWS has some great tools to give you visibility over your usage. You can use the billing section of the AWS console, AWS Cost Explorer and Trusted Advisors AWS to review usage and costs.
With a wide range of services, plans designed to help you get started with few upfront costs, and a complete infrastructure that covers storage, networking, memory and more, AWS is currently the biggest player on the cloud computing market and a viable option for big data. However, the size, complexity and velocity of large datasets can drive costs up unless you implement the right strategies to monitor usage and cut expenses.
by: Steve Hall, Special Projects Manager
About Steve: Steve has been up to his elbows in computers since the 1980s. After high school in St. Petersburg, he moved to Winston Sales, NC. He spent several years in the US Marine Corps as a technology specialist. He is an expert at computer graphics, video productions and all things data. He and Byron have been working on computers and various business projects together since they were teenagers, and the saga continues.