#87 Choosing Tech for the Now and Future and Potential Woes of Decentralizing Data Teams - Interview w/ Jesse Anderson

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.Relevant Links:Jesse's Data Teams Book: https://www.amazon.com/Data-Teams-Management-Successful-Data-Focused-ebook/dp/B08JLFTPBVBig Data Institute website: https://www.bigdatainstitute.io/Data Dream Team podcast: https://sodapodcast.libsyn.com/siteJesse's LinkedIn: https://www.linkedin.com/in/jessetanderson/In this episode, Scott interviewed Jesse Anderson, Managing Director at consulting company Big Data Institute, host of the Data Dream Team podcast, and author of 3 books, most recently Data Teams.To start, a few takeaways from Jesse's perspective on the choosing technology side:You should make sure you have the right team in place to make good technology decisions - the team needs to be in place firstBefore selecting any technology, it's crucial to understand what you are trying to accomplish. And to understand that the technology will provide help in addressing the challenge but won't solve anything itselfFocus on: is this the right tool or solution for us now and in the future? What is the roadmap and vibrancy of the solution?"Technology must earn its keep", meaning you should understand the total cost of ownership and what is your expected return on investmentData tooling cycles are probably going to be 10 years at the most - prepare for obsolescence so you aren't overly reliant on any one technologyAnd some takeaways from Jesse's point of view on decentralizing data teams:Currently, software engineers aren't ready to be data product developers so you'd need embedded data engineers to handle creating and maintaining data products in data meshBut many data engineers are not willing to be embedded into domainsManaging the dotted line versus solid line of reporting between a functional team and the domain is very difficultThere are a number of cracks where crucial data can

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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