Connecting the Dots on Snowflake's Data Cloud Ambitions

Over the past several months we’ve produced a number of in-depth analyses laying out our mental model for the future of data platforms. There are two core themes: 1) Data from people, places, things, and activities in the real world drives applications, not people typing into a UI; and 2) Informing and automating decisions means all data must be accessible. That drives a change from data locked in application silos to application logic being embedded in a platform that manages an end-to-end representation of an enterprise in its data. This week’s Snowflake Summit further confirmed our expectations with a strong top line message of “All Data / All Workloads” and a technical foundation that supports an expanded number of ways to access data. Squinting through the messaging and firehose of product announcements, we believe Snowflake’s core differentiation is its emerging ability to be a complete platform for data applications. Just about all competitors either analyze data or manage data. But no one vendor truly does both. To be precise, managing data doesn’t mean running pipelines or serving analytic queries or AI/ML models. It means managing operational data so that analytics can inform or automate operational activities captured in transactions. With data as the application foundation, the platform needs robust governance. In this week’s Breaking Analysis, we try to connect the dots between Snowflake’s high level messaging and its technical foundation to better understand the core value it brings to customers and partners. As well, we’ll explore the ETR data with some initial input from the Databricks Data + AI Summit to assess the position and prospects of these two leaders along with the key public cloud players.