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Databricks SQL Serverless Under the Hood: How We Use ML to Get the Best Price/Performance


This is Abe from the Lake House Department of the GLB Division. I wrote an article summarizing the contents of the session of Data + AI SUMMIT2023 (DAIS) that I participated in virtually.

I would like to talk about the Databricks SQL Serverless talk "Databricks SQL Serverless Under the Hood: How We Use ML to Get the Best Price/Performance". Speakers are Jeremy Lewallen, Mostafa Mokhtar, and Gaurav Saraf from Databricks.

This talk demonstrated how Databricks SQL can be used to optimize price and performance for AI-powered SQL warehouses.

An overview of Databricks SQL and the Lakehouse platform

First, let's talk about what Databricks SQL is and how it works. Databricks SQL is offered as part of the Databricks Lakehouse platform. This platform has the following features:

  1. Modern SQL engine: Fast and scalable query processing.
  2. Integration with Delta Lake: Easily query data stored in Delta Lake.
  3. AI-powered optimization: We leverage machine learning to optimize the balance between price and performance.

AI-powered price and performance optimization

Let's take a look at how Databricks SQL leverages AI to optimize price and performance.

  1. Query optimization: Optimize query plans and reduce query execution time.
  2. Resource optimization: Monitor resource usage and adjust resources as needed.
  3. Cache optimization: Cache frequently used data and calculation results to reduce query response time.

These optimization techniques enable Databricks SQL to keep costs down while maintaining high performance.

Data warehouse price and performance challenges

The challenge of achieving both low cost and high speed in data warehouses was addressed in this presentation. He explained that the problem is that the cost increases exponentially as the amount of data increases, and that it is important to keep the cost low and stable.

Optimizing price and performance with Databricks SQL

At Databricks SQL, we aim to leverage AI to optimize price and performance for data warehouses. Specifically, the following functions are provided.

  1. Query optimization: Optimize query execution plans and use resources efficiently.
  2. Leverage caching: Cache frequently accessed data to reduce query response time.
  3. Resource Autoscaling: Automatically scale resources according to data volume and query load.

With these features, Databricks SQL optimizes data warehouse cost and performance, and provides an environment in which enterprises can efficiently analyze data.


With Databricks SQL, you can leverage AI to optimize price and performance for your SQL warehouse. The Databricks Lakehouse platform includes a modern SQL engine, and SQL Warehouse is already available if you have data in Delta Lake. This allows you to efficiently perform data analysis and business intelligence tasks. I would like to keep an eye on the evolution of Databricks SQL in the future.


This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.

Translated by Johann


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