Compute Cost Best Practices: How to Optimize Data Costs Across All Architectures

Rising compute costs in the data world are a significant concern due to usage-based pricing models, but optimizing data architecture can help mitigate these expenses. The cloud computing ecosystem is primarily dominated by Amazon Web Services, Microsoft Azure, and Google Cloud Platform, and each has distinct pricing models that influence overall costs. One effective strategy for reducing compute costs is to separate storage and compute resources, allowing for more precise allocation of resources. Additionally, avoiding over-provisioning, shutting down unneeded services, and identifying redundant storage can further decrease expenses. Different data architectures, such as cloud data warehouses, data lakes, and data lakehouses, have varying impacts on compute costs, with data lakehouses offering more efficient resource use through advanced table formats like Apache Iceberg, Delta Lake, and Apache Hudi. Reviewing and adjusting pricing models for tools like ElasticSearch and AWS Athena can also help manage costs. Starburst offers solutions to manage and reduce compute costs effectively, with resources available for those interested in cloud data lakehouse architectures.