How Orchestration Impacts Data Platform Architecture Data Engineering podcast

Tobias Macey द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री Tobias Macey या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal।

Data Engineering Podcast »
How Orchestration Impacts Data Platform Architecture

6d ago 59:39

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MP3•एपिसोड होम

Summary
The core task of data engineering is managing the flows of data through an organization. In order to ensure those flows are executing on schedule and without error is the role of the data orchestrator. Which orchestration engine you choose impacts the ways that you architect the rest of your data platform. In this episode Hugo Lu shares his thoughts as the founder of an orchestration company on how to think about data orchestration and data platform design as we navigate the current era of data engineering.
Announcements

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Your host is Tobias Macey and today I'm interviewing Hugo Lu about the data platform and orchestration ecosystem and how to navigate the available options

Interview

Introduction
How did you get involved in building data platforms?
Can you describe what an orchestrator is in the context of data platforms?
- There are many other contexts in which orchestration is necessary. What are some examples of how orchestrators have adapted (or failed to adapt) to the times?
What are the core features that are necessary for an orchestrator to have when dealing with data-oriented workflows?
Beyond the bare necessities, what are some of the other features and design considerations that go into building a first-class dat platform or orchestration system?
There have been several generations of orchestration engines over the past several years. How would you characterize the different coarse groupings of orchestration engines across those generational boundaries?
How do the characteristics of a data orchestrator influence the overarching architecture of an organization's data platform/data operations?
- What about the reverse?
How have the cycles of ML and AI workflow requirements impacted the design requirements for data orchestrators?
What are the most interesting, innovative, or unexpected ways that you have seen data orchestrators used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration?
When is an orchestrator the wrong choice?
What are your predictions and/or hopes for the future of data orchestration?

Contact Info

Parting Question

From your perspective, what is the biggest thing data teams are missing in the technology 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.
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Links

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

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#Tech #Tobias Macey #Data Engineering #Big Data #Database #Data Pipeline #Podcasting Education