Laying The Foundation Of Your Data Platform For The Era Of Big Complexity With Dagster

Summary The technology for scaling storage and processing of data has gone through massive evolution over the past decade, leaving us with the ability to work with massive datasets at the cost of massive complexity. Nick Schrock created the Dagster framework to help tame that complexity and scale the organizational capacity for working with data. In this episode he shares the journey that he and his team at Elementl have taken to understand the state of the ecosystem and how they can provide a foundational layer for a holistic data platform. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform and blazing fast NVMe storage there’s nothing slowing you down. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. 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Your host is Tobias Macey and today I’m interviewing Nick Schrock about the evolution of Dagster and its path forward Interview Introduction How did you get involved in the area of data management? Can you describe what Dagster is and the story behind it? How has the project and community changed/evolved since we last spoke 2 years ago? How has the experience of the past 2 years clarified the challenges and opportunities that exist in the data ecosystem? What do you see as the foundational vs transient complexities that are germane to the industry? One of the emerging ideas in Dagster is the "software defined data asset" as the central entity in the framework. How has that shifted the way that engineers approach pipeline design and composition? How did that conceptual shift inform the accompanying refactor of the core principles in the framework? (jobs, ops, graphs) One of the powerful elements of the Dagster framework is the investment in rich metadata as a foundational principle. What are the opportunities for integrating and extending that context throughout the rest of an organizations data platform? What do you see as the potential for efforts such as OpenLineage and OpenMetadata to allow for other components in the data platform to create and propagate that context more freely? What are some of the project architecture/repository structure/pipeline composition patterns that have begun to form in the community and your own internal work with Dagster? What are some of the anti-patterns that you have seen users fall into when working with Dagster? Along with your recent refactoring of the core API you have also started to roll out the Dagster Cloud offering. What was your process for determining the path to commercialization for the Dagster project and community? How are you managing governance and long-term viability of the open source elements of Dagster? What are your design principles for deciding the boundaries between OSS and commercial features? What do you see as the role of Dagster in the creation of a data platform architecture? What are the opportunities that it creates for data platform engineers? What is your perspective on the tradeoffs of pipelines as software vs. pipelines as "code" vs. low/no-code pipelines? What (if any) option do you see for language agnostic/multi-language pipeline definitions in Dagster? What do you see as the biggest threats to the future success of Dagster/Elementl? You were a relative outsider to the data ecosystem when you first started Dagster/Elementl. What have been the most interesting and surprising experiences as you have invested your time and energy in contributing to the community? What are the most interesting, innovative, or unexpected ways that you have seen Dagster used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Dagster? When is Dagster the wrong choice? What do you have planned for the future of Dagster? Contact Info LinkedIn @schrockn on Twitter schrockn on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Elementl Series A Announcement Video on software-defined assets Dagster Podcast Episode GraphQL dbt Podcast Episode Open Source Data Stack Conference Meltano Podcast Episode Amundsen Podcast Episode DataHub Podcast Episode Hashicorp Vercel The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast