Set Up Snowflake Monitoring Quickly with Sigma Templates
If you're using Snowflake as your cloud data warehouse (CDW), you know how important it is to monitor usage and how much work is required to build a reliable set of reporting dashboards. Instead of starting from scratch, Sigma's Snowflake Usage templates give you prebuilt dashboards that reduce your time-to-answers. These templates have curated the important metrics and visualizations for you, but still let you drill down to the most granular underlying data. Templates are used to create Sigma workbooks, which means it's easy to make edits, share insights and explore your data in depth without changing the whole workbook to answer a specific question. And because the templates are built directly on the Snowflake-provided usage views, there is no data engineering required to deploy them.
Using Sigma templates is easy. First, log into your Sigma organization (or start a Free Trial). Then, go to the Templates section of Sigma where you’ll simply click on a Snowflake template to launch it, pre-loaded with sample data. You’ll then be prompted to swap your own data into the template. Sigma will automatically search your connected cloud data warehouses, looking for the correct tables to swap with. You can also navigate to the correct table yourself. Because the Snowflake Usage templates are built on top of the Snowflake-provided views, the swapping feature finds the proper Snowflake tables easily.
We'll start with the Snowflake Cost Monitoring template, which answers questions like "How much money is being spent on compute? Which accounts and warehouses are the most expensive? How much data is being stored and what does that cost? Have these costs gone up or down in the last few months?".
Snowflake Performance Monitoring Template
Next up is the Snowflake Performance Monitoring template. This template will help answer questions about how queries are performing in terms of run time, cache usage and pruning, and help monitor the health of warehouses.
On the first tab, the workbook shows key performance indicators (KPIs) for query performance, a breakdown of the types of queries run, and a histogram of query time. There is also a table that reports the status of each warehouse, with metrics like cache utilization, average queued time, total GB spilled to storage and more. In this example, SALES_PROD_WH is the busiest warehouse, running 72% of all queries in this account, but has very low cache utilization. To get a better understanding of this issue, we'll look at the Caching tab.
To monitor users, we can use the User Activity template, which provides metrics on the top Snowflake users, which data they interact with most and their behavior over time.
After filtering to include only the SALES_PROD_WH warehouse, the workbook gives some quick information on the numbers of users and queries in the specified time period. It also lists the most active, most recent, and most regular users with a couple other breakdowns of activity. On the Periodic tab, another anomaly appears: there were two weeks in February with an extreme increase in query volume for this warehouse. Interestingly, this time period was not particularly high in credit consumption based on the Cost Monitoring template, warranting a deeper investigation into the data.
