Open||Source||Data Determinism in Complex Environments and Workflow Services with Maxim Fateev
This episode features an interview with Maxim Fateev, Co-founder and CEO of Temporal, an open source, distributed, and scalable workflow orchestration engine capable of running millions of workflows. He has 20 years of experience architecting mission-critical systems at Uber, Google, Amazon, and Microsoft.
In this episode, Sam sits down with Maxim to discuss workflow services, the power behind Temporal, and bringing determinism to highly complex environments.
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“[Temporal] has this notion of workflows, which can run for a very long time and handle external events, you can treat them as a durable actor. And they're very good at implementing a lifecycle. For example, you can have an object per model and let this object handle all the events. Like, new data came in, notify this object, this object will go and retrain it. Or, it'll run an activity to superiorly check the status. So you can have end-to-end lifecycle implemented fully in Temporal.” – Maxim Fateev
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Episode Timestamps:
(01:03): What’s top of mind for Maxim in workflow services
(04:09): What open source data means to Maxim
(11:07): Maxim explains his time at AWS and building Cadence at Uber
(23:09): Use cases and the community of Temporal
(28:26): How Temporal is being used for ML workloads
(32:28): One question Maxim wishes to be asked
(36:38): Maxim’s advice for those working with complex distributed systems
(39:11): Backstage takeaways with executive producer, Audra Montenegro
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Links:
Watch Maxim’s talk “Designing a Workflow Engine from First Principles”