#280 Enabling Your Domains to Create Maintainable Data Products - Interview w/ Alexandra Diem, PhD

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.Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.Alexandra's LinkedIn: https://www.linkedin.com/in/dralexdiem/In this episode, Scott interviewed Alexandra Diem, PhD, Head of Cloud Analytics and MLOps at Norwegian insurance company Gjensidige.Gjensidige's approach closely aligns with data mesh but they are starting with a focus on consumer-aligned data products as they have a well-functioning data warehouse and are not looking to replace what isn't broken.Some key takeaways/thoughts from Alexandra's point of view:Advice to past data mesh self: stop talking to people about data mesh, talk to the changes in the way of working. It can be very tiresome to try to explain data mesh instead of those changes. Data mesh isn't the point.There aren't really any reasons we can't apply many software engineering best practices to data, it's simply we haven't done it broadly in the data world.There is a push and pull between software best practices and data understanding. Consider which you see as more important and when. Do you bring data understanding to software engineers or software best practices to those with data understanding.When you leverage pair programming between enablement software engineers and data analysts that understand the domain, the software engineers learn more about data and the domain and the analysts learn good software engineering/product practices. It's a win-win.The people you enable to do work in a data mesh way should serve as ambassadors of your ways of working, especially within the domain. Both helping others learn and as champions. That provides organizational scale. You can't individually enable every person in a large company."Too many cooks spoil the broth." Think about having that 'two pizza team' kind of approach so you have concentrated understanding by those involved in creating data products who then can again help others learn. This is good for those in the domain and also for an enablement team bringing learnings back to a platform team.Having a team with intimate knowledge of what data products/data product features have...

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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