Ship Smarter Not Harder With Declarative And Collaborative Data Orchestration On Dagster+
Data Engineering Podcast
Enabling Multiplayer Data Access and Trust through Deep Integration
Enabling stakeholders to self-serve and discover data assets is crucial to fostering multiplayer data usage. A key challenge is the impedance mismatch between workflows and data catalogs, leading to outdated information and lack of trust. Maintaining constant alignment between workflow maintenance and catalog visualization is essential to avoid data catalog obsolescence. Deep integration between the data catalog and the orchestrator is vital to ensure operational data integrity and accuracy. Adopting a better programming model facilitates collaboration among multiple teams by enabling easy publishing of data assets.
00:00
Transcript
Play full episode
Transcript
Episode notes
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 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 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
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__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- 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 hosts@dataengineeringpodcast.com) with your story.
Links
- Dagster
- Dagster+ Launch Event
- Hadoop
- MapReduce
- Pydantic
- Software Defined Assets
- Dagster Insights
- Dagster Pipes
- Conway's Law
- Data Mesh
- Dagster Code Locations
- Dagster Asset Checks
- Dave & Buster's
- SQLMesh
- SDF
- Malloy
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. 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? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
- Dagster:  Data teams are tasked with helping organizations deliver on the premise of data, and with ML and AI maturing rapidly, expectations have never been this high. However data engineers are challenged by both technical complexity and organizational complexity, with heterogeneous technologies to adopt, multiple data disciplines converging, legacy systems to support, and costs to manage. Dagster is an open-source orchestration solution that helps data teams reign in this complexity and build data platforms that provide unparalleled observability, and testability, all while fostering collaboration across the enterprise. With enterprise-grade hosting on Dagster Cloud, you gain even more capabilities, adding cost management, security, and CI support to further boost your teams' productivity. Go to [dagster.io](https://dagster.io/lp/dagster-cloud-trial?source=data-eng-podcast) today to get your first 30 days free!
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
