Learn how real data teams, from solo practitioners to enterprise-scale organizations, build in Dagster’s new eBook, Scaling Data Teams.
Building and scaling a data platform has never been more important or more challenging. Whether you’re a solo data practitioner juggling requests or part of a twenty-person team powering customer-facing products, the core question remains the same: how do you design systems that scale with clarity and confidence?
We’re excited to announce the release of our new eBook, Scaling Data Teams, a practical guide for data teams navigating the journey from one person to twenty.
What’s Inside
This eBook isn’t theory, it’s a roadmap built around the real challenges teams face as they grow:
- Solo (1 person): Balancing one-off requests with reusable applications and choosing tools that will scale.
- Small team (5 people): Earning trust through quality testing, workflows, and collaboration practices that build credibility.
- Growing team (10 people): Making reliability the foundation with modular design and guardrails that keep the platform stable.
- Enterprise team (20 people): Turning the data platform into organizational infrastructure with governance, cross-team collaboration, and product integration.
Throughout the book, you’ll find real-world case studies from Dagster customers like Erewhon, StashAway, Vanta, and Otto, showing how teams at every stage overcame challenges and scaled successfully.
Why Read It
Scaling data teams isn’t about chasing the latest tool or building prematurely, it’s about making the right decisions at the right time. This guide focuses on practices that deliver value today while laying the groundwork for tomorrow. You’ll gain mental models, practical examples, and guiding principles to help you scale with confidence.
Get Your Copy
Scaling Data Teams is available now free to download. Whether you’re just starting to build a data platform or leading a mature data organization, this guide will help you scale your impact, accelerate your team, and prepare for the future of data-driven products.





