Optimizing Cost and Performance in Serverless Databases: A Practical Framework for DynamoDB IA Mode Migration

Sindhu Gopakumar Nair

doi:10.15662/x789yn57

Authors

Sindhu Gopakumar Nair Principal Engineer Author

DOI:

https://doi.org/10.15662/x789yn57

Keywords:

Migration, Cost, Database, DynamoDB, Server, AI, Optimization

Abstract

The paper examines the ways of minimising cost and maintaining good performance in the Amazon DynamoDB when the Infrequent Access (IA) mode is used. Most firms end up paying a great deal in serverless databases since data are being stored even when it is not frequently utilized. In response to this, the paper develops a simple framework that would be used to transfer such data into IA mode. In the study, the quantitative approach is used and two setups are compared, namely standard mode and IA mode, at the same workloads. The logs of latency, throughput and cost are sampled in seven days time. It is demonstrated that with IA mode, it is possible to reduce the cost of storage and operation by approximately 40 percent without increasing the average latency more than 10 milliseconds. The framework involves well defined steps such as the analysis of access pattern, capacity planning and latency testing. The results confirm that this way of migration is able to cost-effectively save performance without damaging it. The study can assist developers, cloud architects, and DevOps, to design intelligent cost-reduction in serverless database platforms.

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