Скачать или смотреть From Coldplay to Code: AI Wins, Data Lakes, and the Age-Old Centralized vs. Distributed Debate

In this “Summer Catch-Up” episode of "How Many CTOs Does It Take?" podcast, hosts Scott Porad and Brad Hefta-Gaub swap stories from the field, covering everything from data pipeline migrations to the ongoing debate over centralized versus distributed data teams. They explore the critical importance of clear data definitions, the promise (and pitfalls) of data lakes, and how organizational structure impacts collaboration.

The conversation shifts into the fast-evolving world of AI in software engineering, with real-world examples of how AI tools are accelerating problem-solving, uncovering hidden performance issues, and boosting productivity across teams. Scott and Brad also reflect on the role of engineering leaders in helping their teams adapt to these tools, not just to deliver value faster, but to future-proof their careers in a rapidly changing tech landscape.

00:00 Introduction and Welcome 00:47 Company Updates and Data Pipeline Migration 01:12 Coldplay CEO Controversy 03:30 Debate: Distributed vs Centralized Data Teams 06:58 Data Definitions and Trust Issues 19:09 AI in Engineering and Toolset Discussion 25:38 Exploring AI in Collaborative Debugging 25:59 Defining Rubber Ducking and Whiteboarding 27:37 AI's Role in Codebase Analysis 29:58 AI Tools and Their Impact on Productivity 37:01 Security Concerns and Tool Selection 41:31 Encouraging AI Adoption in Teams 50:04 The Future of Software Engineering with AI 52:10 Conclusion and Final Thoughts Resources:

How Many CTOs Pod: https://howmanyctospod.com Scott Porad:   / scottporad   Brad Hefta-Gaub:   / bradheftagaub   #TechPodcast #EngineeringPodcast #DevTalks #PodcastForDevs #HowManyCtos #Podcast #Ctos #CtoPodcast #Technology #Engineering #SoftwareDevelopment #Entrepreneurship #DataEngineering #DataLakes #AIProductivity #EngineeringLeadership #AIInTech #CentralizedVsDistributed #FutureOfEngineering #TechLeadership #SoftwareEngineering #MachineLearning #DataScience #AIWins