#123 Reflecting on Multiple Data Mesh Implementations: Iterating Your Way to Success - Interview w/ Sunny Jaisinghani and Simon Massey

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. You can download their Data Mesh for Dummies e-book (info gated) here.Get in touch with Simon and Sunny: data@esynergy.co.ukA webinar (info gated) Sunny and Simon did on data mesh: https://events.esynergy.co.uk/data-mesh-experimentation-to-industrialisation-on-demandSimon's LinkedIn: https://www.linkedin.com/in/simon-massey-82718a3/Sunny's LinkedIn: https://www.linkedin.com/in/sunnysjaisinghani/In this episode, Scott interviewed Sunny Jaisinghani and Simon Massey who are both Principal Consultants at the consulting company esynergy. They have been involved in multiple data mesh implementations including at a large bank. This episode could also have been titled: Aligning Incentives, Reducing Friction, and Continuous Improvement/Value Delivery but it doesn't roll off the tongue very well.From here forward in this write-up, S&S will refer to Simon and Sunny rather than trying to specifically call out who said which part as that leads to confusion.Some key takeaways/thoughts from S&S's points of view:We are all still early in our learnings about how to do data mesh well. There is still a ton left to learn. Which is why people should share what they are learning more broadly. Helping others will help you.Data mesh, whether it's your overall implementation, your platform, your data products, your ways of working, etc. is all about evolution, incremental improvement, iteration, etc. You...

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