Re-Bundling The Data Stack With Data Orchestration And Software Defined Assets Using Dagster
Data Engineering Podcast
Orchestration Layer for Data Lake Management
The orchestration layer in dagster can clone the snowflake scheme to support new changes, managing test environments and data workflows. Different vendors and tools offer varying levels of support, with some requiring custom IO managers. The orchestration layer's built-in capability allows it to manage all test environments, making it ideal for data lake use cases. Additionally, Slike Lake FS supports branch and merge style workflows for data in S3.
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Summary
The current stage of evolution in the data management ecosystem has resulted in domain and use case specific orchestration capabilities being incorporated into various tools. This complicates the work involved in making end-to-end workflows visible and integrated. Dagster has invested in bringing insights about external tools’ dependency graphs into one place through its "software defined assets" functionality. In this episode Nick Schrock discusses the importance of orchestration and a central location for managing data systems, the road to Dagster’s 1.0 release, and the new features coming with Dagster Cloud’s general availability.
Announcements
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- Your host is Tobias Macey and today I’m interviewing Nick Schrock about software defined assets and improving the developer experience for data orchestration with Dagster
Interview
- Introduction
- How did you get involved in the area of data management?
- What are the notable updates in Dagster since the last time we spoke? (November, 2021)
- One of the core concepts that you introduced and then stabilized in recent releases is the "software defined asset" (SDA). How have your users reacted to this capability?
- What are the notable outcomes in development and product practices that you have seen as a result?
- What are the changes to the interfaces and internals of Dagster that were necessary to support SDA?
- How did the API design shift from the initial implementation once the community started providing feedback?
- You’re releasing the stable 1.0 version of Dagster as part of something called "Dagster Day" on August 9th. What do you have planned for that event and what does the release mean for users who have been refraining from using the framework until now?
- Along with your 1.0 commitment to a stable interface in the framework you are also opening your cloud platform for general availability. What are the major lessons that you and your team learned in the beta period?
- What new capabilities are coming with the GA release?
- A core thesis in your work on Dagster is that developer tooling for data professionals has been lacking. What are your thoughts on the overall progress that has been made as an industry?
- What are the sharp edges that still need to be addressed?
- A core facet of product-focused software development over the past decade+ is CI/CD and the use of pre-production environments for testing changes, which is still a challenging aspect of data-focused engineering. How are you thinking about those capabilities for orchestration workflows in the Dagster context?
- What are the missing pieces in the broader ecosystem that make this a challenge even with support from tools and frameworks?
- How has the situation improved in the recent past and looking toward the near future?
- What role does the SDA approach have in pushing on these capabilities?
- 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 bringing Dagster to 1.0 and cloud to GA?
- When is Dagster/Dagster Cloud the wrong choice?
- What do you have planned for the future of Dagster and Elementl?
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.
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Links
- Dagster Day
- Dagster
- Elementl
- GraphQL
- Unbundling Airflow
- Feast
- Spark SQL
- Dagster Cloud Branch Deployments
- Dagster custom I/O manager
- LakeFS
- Iceberg
- Project Nessie
- Prefect
- Astronomer
- Temporal
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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