Simplifying Data Pipelines with Durable Execution
Data Engineering Podcast
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Summary
In this episode of the Data Engineering Podcast Jeremy Edberg, CEO of DBOS, about durable execution and its impact on designing and implementing business logic for data systems. Jeremy explains how DBOS's serverless platform and orchestrator provide local resilience and reduce operational overhead, ensuring exactly-once execution in distributed systems through the use of the Transact library. He discusses the importance of version management in long-running workflows and how DBOS simplifies system design by reducing infrastructure needs like queues and CI pipelines, making it beneficial for data pipelines, AI workloads, and agentic AI.
Announcements
Parting Question
In this episode of the Data Engineering Podcast Jeremy Edberg, CEO of DBOS, about durable execution and its impact on designing and implementing business logic for data systems. Jeremy explains how DBOS's serverless platform and orchestrator provide local resilience and reduce operational overhead, ensuring exactly-once execution in distributed systems through the use of the Transact library. He discusses the importance of version management in long-running workflows and how DBOS simplifies system design by reducing infrastructure needs like queues and CI pipelines, making it beneficial for data pipelines, AI workloads, and agentic AI.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Jeremy Edberg about durable execution and how it influences the design and implementation of business logic
- Introduction
- How did you get involved in the area of data management?
- Can you describe what DBOS is and the story behind it?
- What is durable execution?
- What are some of the notable ways that inclusion of durable execution in an application architecture changes the ways that the rest of the application is implemented? (e.g. error handling, logic flow, etc.)
- Many data pipelines involve complex, multi-step workflows. How does DBOS simplify the creation and management of resilient data pipelines?
- How does durable execution impact the operational complexity of data management systems?
- One of the complexities in durable execution is managing code/data changes to workflows while existing executions are still processing. What are some of the useful patterns for addressing that challenge and how does DBOS help?
- Can you describe how DBOS is architected?
- How have the design and goals of the system changed since you first started working on it?
- What are the characteristics of Postgres that make it suitable for the persistence mechanism of DBOS?
- What are the guiding principles that you rely on to determine the boundaries between the open source and commercial elements of DBOS?
- What are the most interesting, innovative, or unexpected ways that you have seen DBOS used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on DBOS?
- When is DBOS the wrong choice?
- What do you have planned for the future of DBOS?
Parting Question
- 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 AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
- 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.
- DBOS
- Exactly Once Semantics
- Temporal
- Sempahore
- Postgres
- DBOS Transact
- Idempotency Keys
- Agentic AI
- State Machine
- YugabyteDB
- CockroachDB
- Supabase
- Neon
- Airflow
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