Data systems are inherently complex and often require integration of multiple technologies. Orchestrators are centralized utilities that control the execution and sequencing of interdependent operations. This offers a single location for managing visibility and error handling so that data platform engineers can manage complexity. In this episode Nick Schrock, creator of Dagster, shares his perspective on the state of data orchestration technology and its application to help inform its implementation in your environment.
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- Your host is Tobias Macey and today I'm welcoming back Nick Schrock to talk about the state of the ecosystem for data orchestration
- How did you get involved in the area of data management?
- Can you start by defining what data orchestration is and how it differs from other types of orchestration systems? (e.g. container orchestration, generalized workflow orchestration, etc.)
- What are the misconceptions about the applications of/need for/cost to implement data orchestration?
- How do those challenges of customer education change across roles/personas?
- Because of the multi-faceted nature of data in an organization, how does that influence the capabilities and interfaces that are needed in an orchestration engine?
- You have been working on Dagster for five years now. How have the requirements/adoption/application for orchestrators changed in that time?
- One of the challenges for any orchestration engine is to balance the need for robust and extensible core capabilities with a rich suite of integrations to the broader data ecosystem. What are the factors that you have seen make the most influence in driving adoption of a given engine?
- What are the most interesting, innovative, or unexpected ways that you have seen data orchestration implemented and/or used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration?
- When is a data orchestrator the wrong choice?
- What do you have planned for the future of orchestration with Dagster?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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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- K8s == Kubernetes
- DAG == Directed Acyclic Graph
- Software Defined Assets
- Gradient Flow State Of Orchestration Report 2022
- MLOps Is 98% Data Engineering