#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