Zero Prime Podcast

In this episode, I have the pleasure of hosting Spencer Kimball, a former Google engineer turned tech entrepreneur, and now the Co-Founder & CEO of Cockroach Labs. Spencer shares his journey from working at Google to creating his project, Viewfinder, which eventually led to the birth of CockroachDB. Spencer discusses his advice for founders as they approach the challenge of starting a company, finding co-founders, and more. We also delve into the decision to change CockroachDB's licensing to protect it from being monetized by other companies.
Highlights
[00:00] - From Google engineer to tech entrepreneur, Spencer shares his journey and the birth of his project, Viewfinder. He reveals the dream that sparked his desire for more than just a tech job at Google.
[01:02] - The struggle to find a suitable database for Viewfinder led to the creation of CockroachDB. Spencer discusses the need for a scalable, transactional, and open-source database that didn't yet exist.
[03:24] - Passion and belief are key to weathering the ups and downs of a startup. Spencer shares how a founder's vision can drive the company forward despite inevitable disappointments.
[08:53] - Finding the right co-founder is crucial. Spencer shares his advice, emphasizing the importance of shared experiences and mutual respect.
[13:09] - The need for a database that could handle rapid growth and complexity was the key insight behind Cockroach Labs. Spencer discusses how this need led to the creation of CockroachDB.
[18:40] - To protect CockroachDB from being monetized by other companies, Spencer explains the decision to change its licensing. He also discusses the challenges of balancing open source with business needs.
[20:59] - The story of Elastic serves as a cautionary tale. Spencer shares how Amazon exploited Elastic's open core business model, leading to a reevaluation of Cockroach Labs' own model.
[21:53] - The Business Source License (BSL) at Cockroach Labs provides a time period of protection from direct competition. Spencer explains how it reverts to a pure open source license after a certain term.
[26:01] - Quantum Round (Rapid-fire questions)

👉 👉 Don't forget to join us at Data Council - where hundreds of data geeks, founders & investors join each other in building the future of data.

In this episode, I chat with Jordan Tigani, the dynamic engineer-founder of MotherDuck who previously led Google's BigQuery team and headed up product at SingleStore. Jordan shares the inspiring origin story of MotherDuck, which emerged from the overlooked analytics' long tail, focusing on serving small users in data analytics. Our conversation delves into thought-provoking topics such as the conundrum of big data, bridging the gap between unstructured AI data and traditional data structures, the impact of scale-up data warehouses on customer decisions and MotherDuck's relationship with the open-source DuckDB. Tune in to uncover more gems from the founding story of one of the hottest data infrastructure startups in the market today.
Highlights:
[00:45] - Jordan shares his “aha” moments working at Google BigQuery that gave him the inspiration to start MotherDuck
[3:14] - Jordan digs into where we’re at in the era of data evolution and whether big data is still an issue
[6:14] - We discuss how data and compute requirements are shifting with the rise of AI & ML
[8:15] - Jordan shares the significance of scale up vs. scale out and how it impacts user experience and the speed of development
[12:52] - We explore MotherDuck’s relationship with the open source project DuckDB and how they work together
[15:45] - Jordan talks about the user progression from DuckDB to MotherDuck
[18:20] - Quantum Round

👉 👉 Don't forget to join us at Data Council - where hundreds of data geeks, founders & investors join each other in building the future of data.

In this episode, we introduce a new format for the podcast—a deep dive into a technical topic with an engineer-founder who is an expert on the subject. For our first Deep Dive, I sit down with Vijay Subramanian, the visionary behind Trace, to explore the evolution and future of metric layers in business metrics and cross-functional alignment.
Vijay demystifies the concept of metrics layers, metric trees and semantic layers, explaining their components, functionality and crucial role in today’s tech-driven environment, while also clarifying the differences between each concept. We discuss how metrics layers drive organizational transformation, standardize processes and are being utilized by tech giants like Uber and Airbnb to optimize data modeling and business operations. Vijay also emphasizes the need for data analysts to adopt an operational mindset and highlights the transformative role of AI in data analysis, particularly generative AI’s potential to revolutionize business data interpretation and storytelling.
Highlights:
[1:23] - Vijay defines metric layers, metric trees and semantic layers, distinguishing between each and discussing why they’re important
[5:21] - We discuss how the world was introduced to the idea of a metrics layer and why it was important
[8:15] - We analyze what made the hyper-scalers (Airbnb, Uber, DoorDash) get excited about the idea of a metrics layer at around the same time and Vijay shares where the market stands now in adopting metric layers
[13:09] - Vijay digs specifically into metric trees, what they are and where they’re useful and shares more about his decision to build his company, Trace, around the concept of metric trees
[17:09] - We discuss build vs. buy and what led to Vijay’s bet that companies were not going to build their own solution internally
[21:50] - Vijay expands on how metric trees can bridge the gap between technical teams and business teams within an organization
[25:20] - A quick discussion on how the rise of generative AI impacts this space

👉 👉 Don't forget to join us at Data Council - where hundreds of data geeks, founders & investors join each other in building the future of data.

Pete interviews Lindsay Pettingill, CEO & Founder of Iggy. Iggy allows users to quickly understand physical locations to make better investment and management decisions. Lindsay gives us an overview of Iggy, how working at Airbnb informed founding Iggy, what neighborhood data is, and what the long term vision for Iggy is. Check it out!
Highlights:
[1:20] What does Iggy do?
[5:20] How did your experience working at Airbnb inform founding Iggy?
[10:06] Neighborhood data
[13:22] Iggy’s long term vision (Wikipedia for places)
[14:50] Insights for founders from a founder

👉 👉 Don't forget to join us at Data Council - where hundreds of data geeks, founders & investors join each other in building the future of data.

Today on the podcast, I interview DJ Patil, an entrepreneur, investor, scientist and leader in public policy. The former (and first) U.S. Chief Data Scientist, DJ wrote the book on being Data Driven (literally: O'Reilly Data Driven) and now puts his knowledge to work as General Partner of Great Point Ventures.
Highlights
00:29 - Intro
[1:50] - A discussion of the “Sexiest Job in History: Data Scientist” HBR article and how data science has changed since that article's publication
[5:00] - As far as we've come with Data Science, there are still problems - DJ shares some of the problems he sees that remain and how the Data Science community helps solve them
[5:43] - We dig into the emergence of the Data Engineer as a key role due to the requirements of usable data for Data Science (cleaning, prepping, etc.)
[9:00] - Data Challenges are culture problems moreso than deployment or process challenges - it's more about how to think in the proper way and get an entire organization on the same page
[10:10] - We discuss some of the difficulties of introducing ideas into the public sphere/government and how to create strategies to overcome them
[14:44] - How the Data Community helped via "crowdsourcing" with the national Covid response
[17:52] - Building off of the Covid response, DJ explains why he's begun to think of Data Scientists as a new kind of First Responder
[19:51] - How do we make use of data in organizations and how do startups compare to enterprises when it comes to making use of data?
[25:33] - Platform/Infrastructure shifts surrounding AI, LLMs, generative AI and how the industry is responding as well as how founders can better collaborate/respond
[27:37] - Quantum Round (Rapid-fire questions)

👉 👉 Don't forget to join us at Data Council - where hundreds of data geeks, founders & investors join each other in building the future of data.