#26 Leveling Up Your Domain Teams w/ Introductory Data and Analytics Engineering – Interview w/ Brian McMillan

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.Brian is giving away 10 copies of his book for free to those who sign up for a chat to share more about your current challenges related to the book topic or shadow/domain-based IT. To claim your free book, fill out a contact form here and mention "Data Mesh Radio" in the comments.Brian's contact info:Email: brian at minimumviablearchitecture.comLinkedIn: https://www.linkedin.com/in/brianmcmillan01/Website: https://www.minimumviablearchitecture.com/Scott interviews Brian McMillan a former Enterprise Architect who took time off to write a book called 'Building Data Products: Introduction to Data and Analytics Engineering for Non-Programmers'. You can learn more about the book - and get a free copy, see below - here: https://www.minimumviablearchitecture.com/Brian's book lays out a path for the people who are doing the most with data in domains to elevate their skill sets and produce small-scale data products. They do this through a slow ramp from understanding SQL queries to learning data modeling to learning how to publish their data and use simple orchestration tooling. It isn't magic, it will take time and training, but it means you have more people with strong domain knowledge becoming part of the data and analytics engineering process, sharing their business context in scalable and repeatable ways.Brian's approach can also be used for a pretty easy path to an exploratory platform. There isn't a lot of pre-build to get going so teams can much more easily test out a hypothesis or two rather than it being a lengthy and costly approval and build cycle. There is also an easy path once someone finds a "there" there, to move it to something far more scalable and reliable in the cloud.There is a lot from the book and interview that can be adapted to help level up your teams' data literacy.Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him on LinkedIn: https://www.linkedin.com/in/scotthirleman/If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see

Om Podcasten

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