The View From The Lakehouse Of Architectural Patterns For Your Data Platform

The ecosystem for data tools has been going through rapid and constant evolution over the past several years. These technological shifts have brought about corresponding changes in data and platform architectures for managing data and analytical workflows. In this episode Colleen Tartow shares her insights into the motivating factors and benefits of the most prominent patterns that are in the popular narrative; data mesh and the modern data stack. She also discusses her views on the role of the data lakehouse as a building block for these architectures and the ongoing influence that it will have as the technology matures.

Announcements

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Your host is Tobias Macey and today I’m interviewing Colleen Tartow about her views on the forces shaping the current generation of data architectures

Interview

Introduction
How did you get involved in the area of data management?
- In your opinion as an astrophysicist, how well does the metaphor of a starburst map to your current work at the company of the same name?
Can you describe what you see as the dominant factors that influence a team’s approach to data architecture and design?
Two of the most repeated (often mis-attributed) terms in the data ecosystem for the past couple of years are the "modern data stack" and the "data mesh". As someone who is working at a company that can be construed to provide solutions for either/both of those patterns, what are your thoughts on their lasting strength and long-term viability?
What do you see as the strengths of the emerging lakehouse architecture in the context of the "modern data stack"?
- What are the factors that have prevented it from being a default choice compared to cloud data warehouses? (e.g. BigQuery, Redshift, Snowflake, Firebolt, etc.)
- What are the recent developments that are contributing to its current growth?
- What are the weak points/sharp edges that still need to be addressed? (both internal to the platforms and in the external ecosystem/integrations)
What are some of the implementation challenges that teams often experience when trying to adopt a lakehouse strategy as the core building block of their data systems?
- What are some of the exercises that they should be performing to help determine their technical and organizational capacity to support that strategy over the long term?
One of the core requirements for a data mesh implementation is to have a common system that allows for product teams to easily build their solutions on top of. How do lakehouse/data virtualization systems allow for that?
- What are some of the lessons that need to be shared with engineers to help them make effective use of these technologies when building their own data products?
- What are some of the supporting services that are helpful in these undertakings?
What do you see as the forces that will have the most influence on the trajectory of data architectures over the next 2 – 5 years?
What are the most interesting, innovative, or unexpected ways that you have seen lakehouse architectures used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Starburst product?
When is a lakehouse the wrong choice?
What do you have planned for the future of Starburst’s technology platform?

Contact Info

LinkedIn
@ctartow on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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