Optimizing Databricks SQL: Achieving Blazing-Fast Query Speeds at Scale

In this data age, delivering a seamless user experience is paramount. While there are numerous ways to measure this experience, one metric stands tall when evaluating the responsiveness of applications and databases: the P99 latency. Especially vital for SQL queries, this seemingly esoteric number is, in reality, a powerful gauge of the experience we provide to our customers. Why is it so crucial? And how can we optimize it to ensure our databases aren’t just fast, but consistently reliable for 99% of our users? Join us as we demystify P99 latency and delve into strategies to fine-tune it in Databricks SQL. <img alt="" src="https://miro.medium.com/v2/resize:fit:700/0*57KLQ0FORwBigKqQ" style="height:492px; width:700px" /> Photo by <a href="https://unsplash.com/@haakon?utm_source=medium&utm_medium=referral" rel="noopener ugc nofollow" target="_blank">Håkon Sataøen</a> on <a href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="noopener ugc nofollow" target="_blank">Unsplash</a> <h1>What is P99 Latency?</h1> The P99 latency (also known as the 99th percentile latency) for SQL queries is a metric used to measure the response time of SQL queries in a database system. It represents the latency at which 99% of the queries have a response time less than or equal to the P99 latency value, and 1% have a response time greater than the P99 latency value. <a href="https://canadiandataguy.medium.com/how-to-make-databricks-sql-work-blazing-fast-for-queries-at-scale-5ca544a6fca9">Click Here</a>