The Data Stack Show

Rudderstack

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.

  1. 261: Will AI Permanently Disrupt the Bundling and Unbundling Cycle?

    1日前

    261: Will AI Permanently Disrupt the Bundling and Unbundling Cycle?

    This week on The Data Stack Show, Eric Dodds and John Wessel explore how AI is reshaping the data industry, focusing on the ongoing cycles of bundling and unbundling within data infrastructure. They discuss the potential for closed ecosystems like Notion to deliver personalized, integrated experiences and examine recent industry moves such as Fivetran’s acquisitions. The conversation also highlights the challenges faced by both startups and incumbents, the influence of enterprise customers on product development, and the enduring importance of trade-offs when choosing between bundled and unbundled solutions. Key takeaways include the complexity of implementing AI across platforms, the likelihood that market cycles will persist despite technological advances, and the need for organizations to carefully weigh integration, flexibility, and long-term risk when adopting new data tools. Highlights from this week’s conversation include: AI’s Value and Early Ecosystem Integration (1:11)Closed Ecosystems and AI Opportunities (3:21)Personalized Software and the Blank Page Problem (6:17)Transition to Data Industry: Bundling Trends (9:56)Market Cycles and AI’s Role in Bundling (12:56)Incumbents, Innovation, and AI Layering (15:53Longevity of Legacy Systems and Ecosystem Risks (17:56)Switching Costs and Incumbent Advantages (20:33)People Dynamics and the Startup-to-Incumbent Arc (22:50)Enterprise Data Infrastructure: Engineering Challenges (26:33)Fragmentation, Bundling Value, and AI’s Insulation Effect (29:54)Too Many Tools: The Real Meaning Behind Bundling Demand (31:36)Trade-offs in Bundling, Unbundling, and AI (33:40)Final Thoughts and Takeaways (34:34)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. 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.

    35 分鐘
  2. 260: Return of the Dodds: APIs, Automation, and the Art of Data Team Survival

    9月3日

    260: Return of the Dodds: APIs, Automation, and the Art of Data Team Survival

    This week on The Data Stack Show, the crew welcomes Eric Dodds back to the show as they dive into the realities of integrating AI and large language models into data team workflows. Eric, Matt and John discuss the promise and pitfalls of AI-driven automation, the persistent challenges of working with APIs, and the evolution from big data tools to AI-powered solutions. The conversation also highlights the risks of over-reliance on single experts, the critical importance of documentation and context, and the gap between AI marketing hype and practical implementation. Key takeaways for listeners include the necessity of strong data fundamentals, the hidden costs and risks of AI adoption, the importance of balancing efficiency gains with long-term team resilience, and so much more. Highlights from this week’s conversation include: Eric is Back from Europe (0:37)AI and Data: Jurisdiction and Comfort Level (4:00)APIs, Tool Calls, and Practical AI Limitations (5:08)Scaling, Big Data, and AI’s Current Constraints (9:16)Stakeholder-Facing AI and Data Team Risks (13:20)Self-Service Analytics and AI’s Real Impact (16:04)AI Hype vs. Reality and Uneven Impact (20:27)Cost, Context, and AI’s Practical Barriers (25:25)AI for Admin Tasks and Business Logic Complexity (29:13)Tribal Knowledge, Documentation, and Context Engineering (32:07)AI as a Productivity Accelerator and the “Gary Problem” (35:10)Healthy Conflict, Team Dynamics, and AI’s Limits (39:15)Back to Fundamentals: Good Practices Enable AI (41:47)Lightning Round: Favorite AI Tools and Workflow Integration (45:56)AI in Everyday Life and Closing Thoughts (48:14)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. 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.

    51 分鐘
  3. 259: AI is All About Working with Data with Kostas Pardalis of typedef

    8月27日

    259: AI is All About Working with Data with Kostas Pardalis of typedef

    This week on The Data Stack Show, Eric and John welcome back Kostas Pardalis, long-time co-host of the Data Stack Show and now Co-Founder of typedef. The group discusses the rapid evolution of AI and data infrastructure. The conversation also explores how AI is accelerating industry change, the challenges of integrating large language models (LLMs) into data workflows, and the limitations of current semantic layers. Kostas shares insights on building next-generation query engines, the importance of using familiar engineering paradigms, and the need to make AI seamless and almost invisible in user experiences. Key takeaways include the necessity of practical, incremental innovation, the reality behind AI hype, strategies for making advanced data tools accessible and reliable for engineers and businesses alike, and so much more.  Highlights from this week’s conversation include: Kostas’s Background and Career Timeline (1:10)Transition from RudderStack to Starburst Data (4:25)AI Acceleration and Industry Impact (9:37)AI Hype, Investment, and Polarized Reactions (12:05)Historical Parallels and Tech Adoption (13:54)AI Disrupting Tech Workers and Internal Drama (18:56)Experimentation Phase and Future AI Applications (24:01)Invisible AI and User Experience (28:21)AI in Data Infrastructure and LLMs (34:24)SQL, LLMs, and Engineering Solutions (36:35)Standardization, Semantic Layers, and Data Modeling (41:01)Introduction to typedef (45:49)Productionizing AI Workloads with typedef (51:36)Familiarity, Reliability, and Engineering Best Practices (57:24)Security, Enterprise Concerns, and Open Source Models (1:00:48)Final Thoughts and Takeaways (1:01:47)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. 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.

    1 小時 2 分鐘
  4. 258: Confidently Wrong: Why AI Needs Tools (and So Do We)

    8月20日

    258: Confidently Wrong: Why AI Needs Tools (and So Do We)

    This week on The Data Stack Show, John and Matt dive into the latest trends in AI, discussing the evolution of GPT models, the role of tools in reducing hallucinations, and the ongoing debate between data warehouses and agent-based approaches. They also explore the complexities of risk-taking in data teams, drawing lessons from Nate Silver’s book on risk and sharing real-world analogies from cybersecurity, football, and political campaigns. Key takeaways include the importance of balancing innovation with practical risk management, the need for clear recommendations from data professionals, the value of reading fiction to understand human behavior in data, and so much more.  Highlights from this week’s conversation include: Initial Impressions of GPT-5 (1:41)AI Hallucinations and the Open-Source GPT Model (4:06)Tools and Determinism in AI Agents (6:00)Risks of Tool Reliance in AI (8:05)The Next Big Data Fight: Warehouses vs. Agents (10:21)Real-Time Data Processing Limitations (12:56)Risk in Data and AI: Book Recommendation (17:08)Measurable vs. Perceived Risk in Business (20:10)Security Trade-Offs and Organizational Impact (22:31)The Quest for Certainty and Wicked Learning Environments (27:37)Poker, Process, and Data Team Longevity (29:11)Support Roles and Limits of Data Teams (32:56)Final Thoughts and Takeaways (34:20)  The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. 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.

    35 分鐘
  5. 257: Data Tools, Templates, and the Trouble with “Easy” Solutions with the Cynical Data Guy

    8月13日

    257: Data Tools, Templates, and the Trouble with “Easy” Solutions with the Cynical Data Guy

    This week on The Data Stack Show, John and Matt bring you another edition of the Cynical Data Guy. John and Matt dive into the evolution of customer data infrastructure, the growing influence of low-code tools like Clay, and the blurred lines around the “engineer” title in modern data roles. They also discuss the trade-offs between SaaS adoption and building custom solutions, the pitfalls of enterprise software buying, and the realities of platform lock-in—using Palantir’s unique business model as a case study. Key takeaways include the importance of simplicity and scalability in data engineering, the need for clear requirements when evaluating tools, and a healthy skepticism toward sales pitches and “art of the possible” features. Don’t miss this month’s Cynical Data Guy.  Highlights from this week’s conversation include: Reacting to the Rise of the GTM Engineer (1:11)Is "Engineer" the Right Term? (4:49)Low-Code Tools, AI, and Future Workflows (7:14)Simplicity in Data Engineering (14:38)The Pitfalls of "Simple" Solutions (15:18)Choosing SaaS vs. Building In-House (18:26)Business Process Abstraction and SaaS Adoption (21:31)Enterprise Software: Art of the Possible vs. Practicality (24:31)Sales Advice: Focus on Customer Needs (27:11)Forward Deployed Engineers and Delivery Models (29:05)Platform Lock-In: When Is It a Dirty Word? (36:41)Legacy Systems and the Reality of Lock-In (39:53)Final Thoughts and Takeaways (40:55)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. 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.

    41 分鐘

關於

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.

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