How to Automate and Optimize Snowflake Usage With Data Observability

A startup accidentally processed test code more than a hundred billion times, resulting in a five-figure bill instead of the expected $7. Such mistakes are not the only reason for unexpected cloud infrastructure costs; suboptimal resource allocation and lack of effective management can also lead to significant expenses. Modern cloud computing services like Snowflake make it easy to develop and scale data-intensive applications, but proper management is crucial to avoid unwanted costs. A multidimensional data observability solution can help by wiring up the entire data environment for better examination, understanding operations that cause high spending, and optimizing data operations. Acceldata's cost intelligence dashboard automatically aggregates usage across all Snowflake services and assigns a dollar-cost value, offering granular breakups of cost trends across each service. It also allows users to filter their spend by each service and dig deeper into more granular costs. Automatic cost anomaly detection minimizes unwanted expenses by flagging workload spikes that fall beyond average usage boundaries. Capacity planning helps predict cloud resource consumption, identify under- or over-utilization trends, and optimize annual cloud contracts.