#264 Will GenAI and Data Mesh Really Mix? - Interview w/ Madhav Srinath

Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Madhav's LinkedIn: https://www.linkedin.com/in/madhavsrinath/In this episode, Scott interviewed Madhav Srinath, CEO at Nexusleap.Overall, we are super early in the Generative AI cycle and hype is huge. This discussion is one of early impressions, not fully formed answers. It's far too early for that.Also, FYI, there were some technical difficulties in this episode where the recording kept shutting down and had to be restarted. So thanks to Madhav for sticking through and hopefully it isn't too noticeable. Generative AI will mostly be shortened to GenAI throughout these notes. LLM stands for large language models which power GenAI.Some key takeaways/thoughts from Madhav's point of view:?Controversial?: An emerging best practice seems to be having layers of LLMs - one model where you might ask it complicated questions and the second model is trained specifically to vet the answers for correctness and governance concerns.The cost of running many models in production is typically actually quite low, at least infrastructure wise. Instead of an always-on architecture, most organizations are leveraging a serverless architecture - or leverage APIs from others providing the models - so they essentially only pay a few cents per query.?Controversial?: Use GenAI as a "scalpel, not a broadsword". Many are trying to use them in overly broad ways and getting not great results.The ability to take a mountain of data and get something out of it in a structured way isn't a new concept. We've been trying to do that with data mining for years. It's just that it is finally...

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Interviews with data mesh practitioners, deep dives/how-tos, anti-patterns, panels, chats (not debates) with skeptics, "mesh musings", and so much more. Host Scott Hirleman (founder of the Data Mesh Learning Community) shares his learnings - and those of the broader data community - from over a year of deep diving into data mesh. Each episode contains a BLUF - bottom line, up front - so you can quickly absorb a few key takeaways and also decide if an episode will be useful to you - nothing worse than listening for 20+ minutes before figuring out if a podcast episode is going to be interesting and/or incremental ;) Hoping to provide quality transcripts in the future - if you want to help, please reach out! Data Mesh Radio is also looking for guests to share their experience with data mesh! Even if that experience is 'I am confused, let's chat about' some specific topic. Yes, that could be you! You can check out our guest and feedback FAQ, including how to submit your name to be a guest and how to submit feedback - including anonymously if you want - here: https://docs.google.com/document/d/1dDdb1mEhmcYqx3xYAvPuM1FZMuGiCszyY9x8X250KuQ/edit?usp=sharing Data Mesh Radio is committed to diversity and inclusion. This includes in our guests and guest hosts. If you are part of a minoritized group, please see this as an open invitation to being a guest, so please hit the link above. If you are looking for additional useful information on data mesh, we recommend the community resources from Data Mesh Learning. All are vendor independent. https://datameshlearning.com/community/ You should also follow Zhamak Dehghani (founder of the data mesh concept); she posts a lot of great things on LinkedIn and has a wonderful data mesh book through O'Reilly. Plus, she's just a nice person: https://www.linkedin.com/in/zhamak-dehghani/detail/recent-activity/shares/ Data Mesh Radio is provided as a free community resource by DataStax. If you need a database that is easy to scale - read: serverless - but also easy to develop for - many APIs including gRPC, REST, JSON, GraphQL, etc. all of which are OSS under the Stargate project - check out DataStax's AstraDB service :) Built on Apache Cassandra, AstraDB is very performant and oh yeah, is also multi-region/multi-cloud so you can focus on scaling your company, not your database. There's a free forever tier for poking around/home projects and you can also use code DAAP500 for a $500 free credit (apply under payment options): https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio