Optimize Queries Using Query History Metadata with Acceldata

Cloud data platforms like Snowflake and Databricks are increasingly popular for handling large-scale data workloads, including Customer Segmentation and Personalization, Fraud Detection, Predictive Maintenance, Supply Chain Optimization, Financial Planning and Analysis, Human Resources Analytics, Risk Management, Marketing Analysis, and others. These workloads can be categorized into Batch ETL (Extract, Transform, Load), Exploratory, and Interactive types. Badly written queries can significantly increase costs on cloud data platforms by consuming more resources, increasing the amount of data transferred, and increasing the number of queries sent to the platform. To minimize these costs, it is important to optimize queries, minimize unnecessary queries, and use monitoring tools to troubleshoot poorly performing queries. Acceldata's Query Studio for Snowflake provides benefits for data teams to understand, debug, and optimize queries and warehouses by following the measure-understand-optimize (MUO) cycle. This includes measuring query performance and usage, understanding the data collected through analysis, and optimizing based on insights gained in the previous steps.