#14 Knowledge First Approach and Reusing Existing Standards for Data Mesh - Interview w/ Juan Sequeda

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 (most interviews from #32 on) hereProvided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.Juan's contact info and related links:Email: juan at data.worldTwitter: @juansequeda / https://twitter.com/juansequedaLinkedIn: https://www.linkedin.com/in/juansequeda/Catalog & Cocktails Podcast: https://data.world/podcasts/Juan's post about Zhamak's appearance on the Data Engineering Podcast: https://www.linkedin.com/pulse/my-takeaways-data-engineering-podcast-episode-mesh-zhamak-sequeda/Juan's post about knowledge first: https://www.linkedin.com/feed/update/urn:li:activity:6884179569277059072/Standards related links:Dublin Core Metadata Initiative: https://dublincore.org/RDF (Resoruce Description Framework): https://www.w3.org/2001/sw/wiki/RDFOWL (Web Ontology Language): https://www.w3.org/OWL/PROV-O: The PROV Ontology: https://www.w3.org/TR/prov-o/In this episode, Scott interviews Juan Sequeda, Principal Scientist at data.world and co-host of the Catalog and Cocktails podcast. They discussed Juan's knowledge first approach: putting the meaning and value of the data first instead of focusing on the amount of data we are handling/producing. Knowledge first has 3 components, 1) context, 2) people, and 3) relationships. Juan is a big proponent of knowledge graphs and the relationships side is one many people miss.Juan also gave some thoughts on what his approach to data mesh hinges on: treating data as a product and finding a balance between centralization and decentralization for all the aspects of building out an implementation. Juan mentioned Intuit's approach of fixed, flexible/extensible, or customizable as a good general tool and to look for (and embrace) what he calls intellectual friction.Lastly, Juan and Scott talked about the general drive to reduce toil, of reinventing the wheel re data interoperability and standard schemas in data mesh. Juan points to a lot of existing research and standards - e.g. RDF, OWL, and many more (see below) - as a starting point.Data Mesh Radio is...

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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