We started out with introduction to Data management strategies on AWS in Part 1. Here are some additional strategies to look at.
Use policies to manage lifecycle of your data
Data has a lifecycle, just like everything else, and you need to have a plan to control it from the time it was first created to the time it is archived or deleted. As the data moves through this lifecycle, its storage requirements often change. If you are not managing the lifecycle of your data, chances are that your data is being stored in costly or inefficient storage. The recommended approach here is to first identify the lifecycle pattern for your data and then use automated lifecycle polices to manage the lifecycle of these datasets.
Doing so will ensure that data is stored in the most appropriate storage tier at each stage of its lifecycle. Note that the lifecycle management evaluation should include areas like understanding your data characteristics, data access patterns at each stage of data lifecycle, handling data that is old or rarely used, archival and finally data deletion.
Get rid of redundant or unneeded data.
Just as keeping underutilized or idle resources running on cloud costs money, so does data. Storing redundant or unneeded data not only consumes unnecessary storage resources but also increases costs. By removing such data, organizations can free up valuable storage resources and reduce their environmental impact. This problem often manifests in different forms such as data being unnecessarily backed up, duplicated or stored redundantly irrespective of its criticality(touched upon in item 2) or when the data itself is easy to recreate if the need arises.
Monitor data movement to reduce costs
Monitoring, optimizing and minimizing data movement across networks can help with reducing the overall resources need for supporting data movement and indirectly reduce your overall costs besides helping in other areas such as performance.
Ask yourself, have you considered proximity of data or users of your workload when selecting a region on where to store data ? Are you leveraging services such as Lambda@Edge or CloudFront Functions that help you run data closer to your users ? Is the serving of data itself optimized ? Is the data being served up in efficient file formats or compressed ? Is the data being moved in line with your business needs ? Have you evaulated that only relevant data and too in the level of granularity that is needed by your application is being passed around?
To conclude, data management plays a pivotal role in an organization's sustainability journey. By avoiding these anti-patterns and implementing the associated best practices, organizations can ensure that they are using their resources efficiently, and thereby reduce the costs and minimize environmental impact.
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