Product Evolution to a Platform

Alan outlines Databricks' expansion from Spark to MLflow, Delta, and lakehouse capabilities.

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Today we’re breaking down Databricks, a $130B private company that helps companies collect, store, and process very large amounts of data, and then use that data to run analytics and train machine learning models.

Databricks sits in the middle of modern data systems, connecting raw data pipelines to the tools teams use to analyze information and build AI. If you’ve worked on large-scale data or AI projects, there’s a good chance Databricks was part of the stack, often operating behind the scenes.

My guest is Alan Tu, portfolio manager and analyst at WCM Investment Management, which invested in Databricks in late 2024. Alan explains what Databricks actually does for customers, why it remains one of the least understood large private software companies, and how its academic origins and founding team shaped its evolution from an early data-engineering product into a broad commercial platform.

We also discuss common misconceptions about the business, how Databricks fits into the modern AI stack, what has changed since the last time we covered the company, and how its scale, product strategy, and capital position differentiate it from competitors.

Note: This conversation was recorded on December 10, 2025, so all numbers are reflective of what was publicly available on that date.

Please enjoy this breakdown of Databricks.

For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠⁠⁠

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Timestamps

(00:00:00) Welcome to Business Breakdowns

(00:02:34) Introducing Databricks and Guest Alan Tu

(00:03:22) Understanding Databricks' Core Functionality

(00:09:15) The Founding Story of Databricks

(00:23:54) Databricks' Evolution and Product Expansion

(00:30:06) Databricks vs. Snowflake: Market Competition

(00:35:36) Databricks' Strategic Vision and Market Impact