All Episodes

Episode 196

July 3, 2024

with David Wynn

 – Principal Solution Architect, Edge Delta

This week on The Data Stack Show, Eric and John chat with David Wynn of Edge Delta. During the conversation, Dave shared his background, including his econometrics work at UPS during the 2008 recession and his tenure at Google Cloud, where he focused on BigQuery and customer-facing architecture in the gaming industry. The discussion covers the landscape of data warehouse products like Snowflake and Databricks, the complexities of cloud platforms, and the challenges of observability. They also delve into the cautious integration of AI in observability, emphasizing the need for better mental models and practical approaches, and so much more. 

Episode 195

June 26, 2024

with Jeff Skoldberg

 – Principal Consultant, Data Architecture and Analytics, Green Mountain Data Solutions

This week on The Data Stack Show, Eric and John chat with Jeff Skoldberg, Principal Consultant, Data Architecture and Analytics at Green Mountain Data Solutions. Jeff has been a data consultant specializing in supply chain analytics and cost optimization and shares his journey from starting as a business analyst at Keurig in 2008 to becoming an independent consultant. They discuss the evolution of the data landscape, including shifts from Microsoft SQL Server to SAP HANA and later to Snowflake. Jeff emphasizes the importance of cost optimization, detailing strategies for managing data costs effectively. The group also discusses two frameworks for using data to control business processes and create actionable dashboards, and more.

Episode 194

June 19, 2024

with Clint Dunn

 – Co-Founder, Wilde

This week on The Data Stack Show, Eric and John chat with Clint Dunn, Co-Founder at Wilde. During this conversation, Clint shares his journey from an economics major to a data professional, detailing his experiences at Afterpay and in building data teams for e-commerce companies. The discussion covers Wilde’s focus on predicting customer lifetime value (LTV) and churn for retail brands, emphasizing the importance of accurate data for business decisions. The group also explores the challenges of integrating data predictions into marketing workflows, technical aspects of managing and analyzing large datasets, and more.