Interview Episodes

Episode 179

February 28, 2024

with Tony Wang

 – Graduate Research Assistant (PhD), Stanford University

This week on The Data Stack Show, Eric and Kostas chat with Tony Wang, Graduate Research Assistant (PhD) at Stanford University. During the episode, Tony discusses his journey from China to studying electrical and hardware engineering at MIT, his transition to data processing systems for his Ph.D., and the academic-industry connection. Tony shares insights on cloud data processing, the limitations of academic hardware projects compared to industry giants like NVIDIA, and the potential for software innovation in academia. He also delves into his current research focus on time series data management, the challenges of integrating different data systems, the goal of improving data processing efficiency, the sales aspect of his research, and more. 

Episode 178

February 21, 2024

with Peter Chapman

 – GTM Consultant

This week on The Data Stack Show, Eric and Kostas chat with Peter Chapman, Peter is a consultant who specializes in helping PLG companies drive more revenue with data. With a background in data and revenue operations, Peter shares his experiences in building data stacks at startups like Heroku, emphasizing the early consideration of data architecture to avoid future issues. He highlights the significance of a cohesive data stack for product-led growth companies and the unique challenges faced by open-source companies in commercializing their projects. The conversation also explores the operationalization of data, the importance of aligning sales with a company’s technical ethos, debating the balance between inference and training costs, the strategic approach to margins by focusing on enterprise features over infrastructure reselling, and more. If you’d like to contact Peter about his advisory services, his email is peter@chapman-coaching.com.

Episode 177

February 14, 2024

with Rishabh Bhargava

 – Co-Founder and CEO, refuel

This week on The Data Stack Show, Eric and Kostas chat with Rishabh Bhargava, Co-Founder and CEO of refuel. During the episode, the group discusses the evolution of AI, machine learning, and large language models (LLMs). Rish shares his background and the inception of refuel, which focuses on making clean and reliable data accessible for businesses through data cleaning, labeling, and enrichment using LLMs. The conversation explores the impact of LLMs on data quality, the challenges of implementing LLM technology, and the user experience of working with LLMs. They also touch upon the importance of confidence scores in machine learning and the iterative process of model training, a practical use case involving refuel and RudderStack, and more.

Episode 176

February 7, 2024

with Viren Baraiya

 – Co-Founder & CTO, orkes.io

This week on The Data Stack Show, Eric and Kostas chat with Viren Baraiya, the Co-Founder and CTO of orkes.io. During the episode, Viren discusses the evolution of orchestration in the context of AI and large-scale systems. The group discusses the transition from Viren’s work at Netflix to founding orkes, the challenges of integrating AI into applications, and the importance of orchestration to manage these complexities. He also highlights the non-deterministic nature of AI, the need for guardrails, and the potential for AI to change technology interaction. The episode also covers the recent move of Netflix’s Conductor project to a community foundation, the future of AI in business and its impact on job creation, and more.

Episode 175

January 31, 2024

with Wes McKinney, Pedro Pedreira, Chris Riccomini, Ryan Blue

 – Wes McKinney (Co-Founder, Voltron), Pedro Pedreira Software Engineer, Meta), Chris Riccomini (Seed Investor, various startups), and Ryan Blue (Co-Founder and CEO, Tabular)

This week on The Data Stack Show, Eric and Kostas chat with a panel of experts as Wes McKinnyey (Cofounder, Voltron), Ryan Blue (Co-Founder and CEO, Tabular), Chris Riccomini (Seed Investor, Various Startups), Pedro Pedreira (Software Engineer, Meta), all share their thoughts around the topic of composable data stacks. During the conversation, the group chats about the importance of open standards and APIs for efficient interoperability in data management systems, the evolution of data workloads, the need for specialization, and the challenges in building composable components. The conversation also covered the significance of an intermediate representation (IR) for decoupling various layers of data systems, the complexities of data types, and the desire for more secure data sharing methods. The panelists explored the evolution of open standards and the trade-offs between composable and monolithic systems, expressing excitement about new data infrastructure projects and technologies, modular execution engines, new query interfaces, standardizing policy decisions across different data management platforms, and more.

Episode 174

January 24, 2024

with Artyom Keydunov

 – Co-Founder and CEO, Cube Dev

This week on The Data Stack Show, Eric and Kostas chat with Artyom Keydunov, Co-Founder and CEO, Cube Dev. During the episode, the group discusses the evolution of semantic layers, their importance in data management, and Cube’s growth and adaptation to industry needs. Artyom highlights the challenges in building a semantic layer and the solutions Cube has developed, including their own SQL engine. He also discusses the potential of integrating semantic layers with natural language processing technologies for improved accuracy and much more.

Episode 173

January 17, 2024

This week on The Data Stack Show, Eric and Kostas chat with Jay Henderson, SVP of Product Management at Alteryx. During the episode, Jay shares his career journey from accounting to data analytics, and his role at Alteryx. They discuss Alteryx’s analytics automation platform, designed for end users without coding skills. Jay explains how Alteryx democratizes analytics, providing a visual representation of data transformations and handling inputs from various systems. They also discuss the challenges of building low-code/no-code systems, the importance of collaboration in analytics, the potential impact of generative AI on analytics, and more.

Episode 172

January 10, 2024

with Matt Butcher

 – CEO, Fermyon Technologies

This week on The Data Stack Show, Eric and Kostas chat with Matt Butcher, the CEO of Fermyon Technologies. During the episode, Matt discusses his career journey from philosophy to software development, and his work with various programming languages and cloud ecosystems. He talks about his experiences with Google’s infrastructure and container technology, and his various stops along the way before becoming CEO at Fermyon. The discussion also covers the use of WebAssembly for cloud computing, its advantages over virtual machines and containers, its potential for revolutionizing application development, and more. 

Episode 171

January 3, 2024

with Sandy Ryza

 – Lead Engineer, Dagster

This week on The Data Stack Show, Eric and Kostas chat with Sandy Ryza, Lead Engineer at Dagster. During the episode, Sandy shares insights on data cleaning, data engineering processes, and the need for improved tools. He introduces Dagster, an orchestrator that focuses on assets like tables, datasets, and machine learning models, and contrasts it with traditional workflow systems. He also explains Dagster’s integration with DBT, while also exploring the changing dynamics in data roles, the impact of modern tooling, the potential for increased creativity in the field, and more. 

Episode 170

December 27, 2023

with Katie Bauer

 – Head of Data, GlossGenius

This week on The Data Stack Show, Eric and Kostas chat with Katie Bauer, the Head of Data at GlossGenius. During the episode, Katie shares her journey in data science, starting from academia to working in various industries including natural language search, social media, and now the beauty and salon space. She discusses the evolution of the data scientist role, the challenges faced in different companies, and the importance of understanding the specific needs of different business models. She also highlights the potential of using data products to provide value back to businesses, the importance of having an analytics engineer in an organization, and more.