Software architecture, design, and development in a fraction of the time
The Snowflake Data Cloud offers a host of benefits like virtually unlimited scale and concurrency. But an unoptimized Data Platform could quickly run up costs and exhaust your Snowflake credits before you know it.
We have been implementing Data Warehouses for over ten years now, and we have got your back. With our simple-to-use and extensive Snowflake Usage Dashboard, you can address all of these top-priority items and maximize your technology investment.
The overview tab begins with providing you top queries that can be optimized.
running in QUERY_DEV_XSMALL_WH (X-Small). can be moved to higher warehouse.
Shift right approach to move long executing queries to a higher warehouse to optimize performance.
are queries with highest difference between compilation and execution times.
Shift left approach to move queries to a lower warehouse to optimize cost.
queuing time. Consider enabling auto-scaling to improve performance.
This is the number of queries that have a high queuing time. And a recommendation to enable auto-scaling to improve performance.
With the help of these charts, you could monitor and optimize your Snowflake cost by Date, User, or Warehouse. By viewing a record of credits consumed by various users or warehouses, you are in the perfect position to optimize your spending. You could also prevent the unnecessary exhaustion of credits.
These charts are designed to give you insights into your Snowflake Datawarehouse’s execution time. While the Cloud offers several advantages, it is important for you to keep a tab on Query Execution Time. Apart from knowing the average execution time from day to day, you could also keep a close track of variations, so that your Data Cloud is efficient.
Under Query Trends, you will visualize the volumetrics information of the queries executed over a specified time range. This helps you monitor unnecessary spikes observed during a time period or for a specific user and will make sure queries/users are prioritized according to your plan.
The Query Explorer table displays the comparison of queries under a similar bucket having execution time greater or lower than other warehouse sizes. You could also visualize the grouping of similar query sizes and their execution time to optimize long-running queries.
This tab offers insights into the several Warehouses, Warehouse size, User, and Date. It gives you a clear picture of how the Snowflake Datacloud is being used across the organization. Thereby helping you maximize RoI with actionable insights to optimize queries, reduce cost and improve performance.
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