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. 255: When Dashboards Lie: Unpacking the Myths of Self-Service Analytics with the Cynical Data Guy

    1天前

    255: When Dashboards Lie: Unpacking the Myths of Self-Service Analytics 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 quirky world of data analytics, exploring common challenges like unrealistic data requests, the limitations of self-service BI, and the evolving role of data analysts. They also discuss the importance of understanding business context, the need for effective data storytelling, and the emerging trend of "BI as code" which promises more flexible and version-controlled analytics tools. The conversation highlights the gap between technical data capabilities and business user needs, emphasizing that the real value of data professionals lies not just in tool proficiency, but in their ability to provide meaningful insights and guide decision-making. Key takeaways include the importance of context in data analysis, the limitations of self-service tools, the ongoing evolution of data roles in modern organizations, and more.  Highlights from this week’s conversation include: Reading and Reacting to the LinkedIn Data Request Post (1:36)Changing KPIs and Data Skepticism (2:21)The Burden of Proving Data Integrity (5:00)Handling Metric Changes and Historical Comparisons (7:16)Preparing Stakeholders for New Metrics (9:16)BI Code, Version Control, and Modern Dashboards (11:20)Scoping and Business Context in Data Roles (14:38)Technical vs. Business Understanding in Data Teams (16:29)GUI vs. Code in Dashboard Customization (20:41)The Analyst’s Role: Guidance Over Tools (23:23)Hiring and the Real-World Analyst Skillset (28:11)Final Thoughts and Takeaways (30:36)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.

    31 分钟
  2. 254: Context is King: Building Intelligent AI Analytics Platforms with Paul Blankley of Zenlytic

    7月23日

    254: Context is King: Building Intelligent AI Analytics Platforms with Paul Blankley of Zenlytic

    This week on The Data Stack Show, John chats with Paul Blankley, Founder and CTO of Zenlytic, live from Denver! Paul and John discuss the rapid evolution of AI in business intelligence, highlighting how AI is transforming data analysis and decision-making. Paul also explores the potential of AI as an "employee" that can handle complex analytical tasks, from unstructured data processing to proactive monitoring. Key insights include the increasing capabilities of AI in symbolic tasks like coding, the importance of providing business context to AI models, and the future of BI tools that can flexibly interact with both structured and unstructured data. Paul emphasizes that the next generation of AI tools will move beyond traditional dashboards, offering more intelligent, context-aware insights that can help businesses make more informed decisions. It’s an exciting conversation you won’t want to miss. Highlights from this week’s conversation include: Welcoming Paul Back and Industry Changes (1:03)AI Model Progress and Superhuman Domains (2:01)AI as an Employee: Context and Capabilities (4:04)Model Selection and User Experience (7:37)AI as a McKinsey Consultant: Decision-Making (10:18)Structured vs. Unstructured Data Platforms (12:55)MCP Servers and the Future of BI Interfaces (16:00)Value of UI and Multimodal BI Experiences (18:38)Pitfalls of DIY Data Pipelines and Governance (22:14)Text-to-SQL, Semantic Layers, and Trust (28:10)Democratizing Semantic Models and Personalization (33:22)Inefficiency in Analytics and Analyst Workflows (35:07)Reasoning and Intelligence in Monitoring (37:20)Roadmap: Proactive AI by 2026 (39:53)Limitations of BI Incumbents, Future Outlooks and Parting Thoughts (41:15)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.

    42 分钟
  3. 253: Why Traditional Data Pipelines Are Broken (And How to Fix Them) with Ruben Burdin of Stacksync

    7月16日

    253: Why Traditional Data Pipelines Are Broken (And How to Fix Them) with Ruben Burdin of Stacksync

    This week on The Data Stack Show, Eric and welcomes back Ruben Burdin, Founder and CEO of Stacksync as they together dismantle the myths surrounding zero-copy ETL and traditional data integration methods. Ruben reveals the complex challenges of two-way syncing between enterprise systems like Salesforce, HubSpot, and NetSuite, highlighting how existing tools often create more problems than solutions. He also introduces Stacksync's innovative approach, which uses real-time SQL-based synchronization to simplify data integration, reduce maintenance overhead, and enable more efficient operational workflows. The conversation exposes the limitations of current data transfer techniques and offers a glimpse into a more declarative, flexible approach to managing enterprise data across multiple systems. You won’t want to miss it. Highlights from this week’s conversation include: The Pain of Two-Way Sync and Early Integration Challenges (2:01)Zero Copy ETL: Hype vs. Reality (3:50)Data Definitions and System Complexity (7:39)Limitations of Out-of-the-Box Integrations (9:35)The CSV File: The Original Two-Way Sync (11:18)Stacksync’s Approach and Capabilities (12:21)Zero Copy ETL: Technical and Business Barriers (14:22)Data Sharing, Clean Rooms, and Marketing Myths (18:40)The Reliable Loop: ETL, Transform, Reverse ETL (27:08)Business Logic Fragmentation and Maintenance (33:43)Simplifying Architecture with Real-Time Two-Way Sync (35:14)Operational Use Case: HubSpot, Salesforce, and Snowflake (39:10)Filtering, Triggers, and Real-Time Workflows (45:38)Complex Use Case: Salesforce to NetSuite with Data Discrepancies (48:56)Declarative Logic and Debugging with SQL (54:54)Connecting with Ruben and Parting Thoughts (57:58)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.

    59 分钟
  4. 252: What the Heck is Happening in Data Right Now with the Cynical Data Guy

    7月9日

    252: What the Heck is Happening in Data Right Now with the Cynical Data Guy

    This week on The Data Stack Show, Eric and John welcome back Matt Kelliher-Gibson for another edition of the Cynical Data Guy. The group explores the current state of data engineering and team dynamics while critically examining the evolving landscape of analytics engineering, dissecting the hype around the modern data stack and its tools. The conversation also explores the challenges of data team management, including headcount reductions, rising technology costs, and the struggle to maintain efficiency. Key discussions revolve around the need for open standards, the impact of AI on data roles, the complex hiring practices in tech startups, and so much more.    Highlights from this week’s conversation include: The Evolution of Analytics Engineer Roles (1:53)Job Titles and Role Consolidation in Data (3:20)Standardization and Open Data Standards (7:51)SQL as a Universal Standard & Vendor Lock-In (11:58)Modern Data Stack: Hype vs. Reality (13:29)The State of Data Teams in 2025 (18:12)Morale and Job Market Realities for Data Professionals (25:17)Bonus Round: Extreme Work Culture Satire (28:41)Honesty in Hiring and Team Building (33:18)Challenges of Building and Leading Data Teams (37:31)Final Thoughts and Takeaways (41:15)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.

    42 分钟

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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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