In this episode, I sit down with Mark Freeman and Chad Sanderson (Gable.ai) to discuss the release of their new O’Reilly book, Data Contracts: Developing Production-Grade Pipelines at Scale. They dive deep into the chaotic journey of writing a 350-page book while simultaneously building a venture-backed startup. The conversation takes a sharp turn into the evolution of Data Contracts. While the concept started with data engineers, Mark and Chad explain why they pivoted their focus to software engineers. They argue that software engineers are facing a "Data Lake Moment, "prioritizing speed over craftsmanship, resulting in massive technical debt and integration failures.

Gable: https://www.gable.ai/

The Rise of the Data-Conscious Software Engineer: Bridging the Data-Software Gap | Mark Freeman | Shift Left Data Conference 2025

Data teams increasingly embrace software engineering practices to address quality and integration challenges, yet friction remains between software and data teams. This talk explores why standard practices alone aren’t enough and introduces the concept of the “Data-Conscious Software Engineer,” an emerging role critical to bridging these organizational divides. Attendees will learn how identifying and empowering engineers who deeply understand both software development and data workflows can foster stronger collaboration, improve data quality, and drive organizational change toward treating data as a strategic asset.

Mark Freeman joins me to chat about data contracts, the crazy life of being the first employee at a hot startup, writing books and creating content, and much more.

DataEngineering #Startups #AI #DataQuality #DataContracts

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.