Summary A core differentiator of Dagster in the ecosystem of data orchestration is their focus on software defined assets as a means of building declarative workflows. With their launch of Dagster+ as the redesigned commercial companion to the open source project they are investing in that capability with a suite of new features. In this episode Pete Hunt, CEO of Dagster labs, outlines these new capabilities, how they reduce the burden on data teams, and the increased collaboration that they enable across teams and business units. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Pete Hunt about how the launch of Dagster+ will level up your data platform and orchestrate across language platforms Interview Introduction How did you get involved in the area of data management? Can you describe what the focus of Dagster+ is and the story behind it? What problems are you trying to solve with Dagster+? What are the notable enhancements beyond the Dagster Core project that this updated platform provides? How is it different from the current Dagster Cloud product? In the launch announcement you tease new capabilities that would be great to explore in turns: Make data a team sport, enabling data teams across the organization Deliver reliable, high quality data the organization can trust Observe and manage data platform costs Master the heterogeneous collection of technologies—both traditional and Modern Data Stack What are the business/product goals that you are focused on improving with the launch of Dagster+ 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 the design and launch of Dagster+? When is Dagster+ the wrong choice? What do you have planned for the future of Dagster/Dagster Cloud/Dagster+? Contact Info Twitter (https://twitter.com/floydophone) LinkedIn (https://linkedin.com/in/pwhunt) 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 shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) 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 hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Dagster (https://dagster.io/) Podcast Episode (https://www.dataengineeringpodcast.com/dagster-data-applications-episode-104) Dagster+ Launch Event (https://dagster.io/dagster-plus-launch-event) Hadoop (https://hadoop.apache.org/) MapReduce (https://en.wikipedia.org/wiki/MapReduce) Pydantic (https://docs.pydantic.dev/latest/) Software Defined Assets (https://docs.dagster.io/concepts/assets/software-defined-assets) Dagster Insights (https://docs.dagster.io/dagster-cloud/insights) Dagster Pipes (https://docs.dagster.io/guides/dagster-pipes) ...
