#99 Getting Philosophical About Knowledge and Sharing Experiences via Data - Interview w/ Andrew Padilla

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!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 / Scott Hirleman. 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.Andrew's LinkedIn: https://www.linkedin.com/in/andrew-padilla-8988094a/Datacequia website: https://www.datacequia.com/Andrew's personal Substack: https://datacequia.substack.com/Data Mesh Community newsletter Substack: https://datameshlearning.substack.com/In this episode, Scott interviewed Andrew Padilla, who runs a data and software consulting company - Datacequia - and serves as editor of the Data Mesh Learning community newsletter.This one is a bit more philosophical about sharing information/knowledge so it's one to sit and think over. Things in quotes are direct from Andrew.Some key takeaways/thoughts that come from Andrew's view of data mesh and the data space in general:To move from sharing the 1s and 0s of data to actually sharing knowledge, we need to harmonize data, metadata, and code - "the digital embodiment of knowledge". That's where Andrew hopes the mesh data products can head.Software development isn't cutting it for sharing knowledge. Will data product development? Do we need to move to knowledge-centered development instead? Remains to be seen.We still don't know how to model well - in data - what is going on in the real world. What are the experiences of the organization? Can we really define an "organizational experience"? Event storming tries but seems to fall short often.We must learn to treat organizations like living entities. Organizational experiences cross multiple domains and the types of experiences will change, will evolve - possibly quite quickly. We again have to get better at modeling those and evolving how we share knowledge about the experiences.Knowledge graphs are the best way we have currently for combining information across domains. We still haven't fully figured out how to leverage our cross domain knowledge though.Historically, we've bent our ways of working to the limitations of the machines. We need to spend more...

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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