Highlights from this week’s conversation include:

Stefan’s background in data (2:39)
What is DAGWorks? (3:55)
How building point solutions influenced Stefan’s journey (5:03)
Solving the tooling problems of self-service at an organization (11:44)
Creating Hamilton (15:53)
How Hamilton works with definitions and time-series data (19:34)
What makes Hamilton an ML-oriented framework? (23:39)
Navigating the differences between ML teams and other data teams (26:27)
Understanding the fundamentals of Hamilton (28:25)
Dealing with types and conflicts in programming (33:18)
How Hamilton helps improve pipelines and maintaining data (37:11)
Why unit testing is important for a data scientist (44:54)
The ups and downs of founding building a data solution (46:32)
Connecting with DAGWorks and trying out Hamilton (50:01)
Final thoughts and takeaways (52:46)

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.

RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

148: Exploring the Intersection of DAGs, ML Code, and Complex Code Bases: An Elegant Solution Unveiled with Stefan Krawczyk of DAGWorks