Snowflake was the first data warehouse designed from scratch to take advantage of Cloud economics. We'll talk about what that means, why it was such a big deal, and how its design differs from the approaches taken by similar systems. Stay until the end for some bonus content on how Snowflake is bringing stream processing into the DBMS. Zoom link:... Read More

This talk presents recent research and development at Snowflake aimed at pushing the boundaries of database performance and functionality. In the first section, we will introduce a series of optimizations designed to accelerate query execution within Snowflake’s platform. We will discuss the technical challenges associated with developing general-purpose optimizations and balancing performance improvements across a wide range of workloads. The... Read More

Snowflake internals tech talk. Read More

Why settle for 1 cool db talk when you can get 3? Snowflake is pushing the boundaries of what a unified cloud data platform can do. Today we'll talk about how Snowflake can be combined with open standards like Apache Iceberg, hard tech to stream data into Snowflake and bring transactional and analytical workloads together in a single platform. Apache... Read More

Snowflake is an analytic data warehouse offered as a fully-managed service in the cloud. It is faster, easier to use, and far more scalable than traditional on-premise data warehouse offerings and is used by thousands of customers around the world. Snowflake's data warehouse is not built on an existing database or "big data" software platform such as Hadoop—it uses a... Read More

In this talk, I will give an introduction to Snowflake's query optimizer. I will talk about the main features of Snowflake's optimizer, explain the main philosophy behind the design decisions, and delve into some unique aspects of the implementation. I will also later expand into our infrastructures to facilitate optimizer development and discuss the opportunities and challenges for implementing and... Read More

For partitioned tables, maintaining good clustering properties for frequently accessed dimensions is critical for partition pruning performance. Naive methods of clustering maintenance could be expensive, especially when the clustering dimensions are different from the dimensions with which the data is loaded. On the other hand, approximate clustering is cheaper to maintain while still resulting in good pruning performance. In this... Read More