Summary
In this episode Preeti Somal, EVP of Engineering at Temporal, talks about the durable execution model and how it reshapes the way teams build reliable, stateful systems for data and AI. She explores Temporal’s code‑first programming model—workflows, activities, task queues, and replay—and how it eliminates hand‑rolled retry, checkpoint, and error‑handling scaffolding while letting data remain where it lives. Preeti shares real-world patterns for replacing DAG-first orchestration, integrating application and data teams through signals and Nexus for cross-boundary calls, and using Temporal to coordinate long-running, human-in-the-loop, and agentic AI workflows with full observability and auditability. Shee also discusses heuristics for choosing Temporal alongside (or instead of) traditional orchestrators, managing scale without moving large datasets, and lessons from running durable execution as a cloud service.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.
Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.
Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.
Your host is Tobias Macey and today I'm interviewing Preeti Somal about how to incorporate durable execution and state management into AI application architectures

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what durable execution is and how it impacts system architecture?
With the strong focus on state maintenance and high reliability, what are some of the most impactful ways that data teams are incorporating tools like Temporal into their work?
One of the core primitives in Temporal is a "workflow". How does that compare to similar primitives in common data orchestration systems such as Airflow, Dagster, Prefect, etc.?
- What are the heuristics that you recommend when deciding which tool to use for a given task, particularly in data/pipeline oriented projects?
Even if a team is using a more data-focused orchestration engine, what are some of the ways that Temporal can be applied to handle the processing logic of the actual data?
AI applications are also very dependent on reliable data to be effective in production contexts. What are some of the design patterns where durable execution can be integrated into RAG/agent applications?
What are some of the conceptual hurdles that teams experience when they are starting to adopt Temporal or other durable execution frameworks?
What are the most interesting, innovative, or unexpected ways that you have seen Temporal/durable execution used for data/AI services?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Temporal?
When is Temporal/durable execution the wrong choice?
What do you have planned for the future of Temporal for data and AI systems?

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 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.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

State, Scale, and Signals: Rethinking Orchestration with Durable Execution

Data Engineering Podcast

Task Queues, Replay, and Picking Up After Crashes

The AI-powered Podcast Player