#284 Breaking Down the Monolith - Incentivizing Good Choices - Interview w/ Frederik Nielsen

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.Frederik's LinkedIn: https://www.linkedin.com/in/frederikgnielsen/In this episode, Scott interviewed Frederik Nielsen, Engineering Manager at Pandora (the jewelry one, not the music one 😅).Some key takeaways/thoughts from Frederik's point of view:Your data technology and architecture choices incentivize certain behaviors. Consider what behaviors you want before you lock yourself in to anything. Advice to past data mesh self: "construct a data architecture and platform that can adapt to the business requirements and wishes [which] will change over time." Build a composable platform as it's "easier to adapt to changing business requirements." Focus on decentralization features and make it decoupled and composable.Trying to go too wide with your data mesh implementation at the start with all your domains makes it harder to really find your groove and build momentum.Cost transparency can be a big driver for data mesh adoption. Teams want to understand their costs and many organizations are driving cost cutting initiatives. Decomposing the monolithic approach to data means better understanding the cost of individual pieces of data work.Relatedly, when teams are responsible for their own costs, it's easier to spot when someone is making tradeoffs related to cost. It's a more tangible decision and can be a conscious decision to take on tech debt.When taking a concept like data mesh to the highest levels in the organization, attach it to tangible use cases. Make it something that is worth their while, the 'juice must be worth the squeeze'. Focus on the strategic business goals and priorities.It's okay to leverage management consultants. But your data ownership should very clearly be internal - external parties should not own any aspects if you want long-term success. Regarding consultants: "you would rather be driving them than them driving you."It's absolutely normal for some teams to be more data mature than others. If teams raise their hands saying they need help with their data work, your culture 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