#181 Learnings from BlaBlaCar's Early Data Mesh Journey: Positive Transformation for the People and the Organization - Interview w/ Kineret Kimhi

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.Kineret's LinkedIn: https://www.linkedin.com/in/kineret-kimhi/Kineret's Blog Post 'Do’s and Don’ts of Data Mesh': https://medium.com/blablacar/dos-and-don-ts-of-data-mesh-e093f1662c2dIn this episode, Scott interviewed Kineret Kimhi, Analytics Lead at BlaBlaCar.Some key takeaways/thoughts from Kineret's point of view:!Interesting Decision!: BlaBlaCar reorganized their data organization but did not fully decentralize by embedding people into domains. Instead, they kept a central team but combined multiple functions into a squad around domains - a key domain might have a data engineer, data analyst, data scientist, and a software engineer.!Scott Mantra Too!: Sharing your experience - data mesh or otherwise - early and often with the broader data community means better and quicker feedback, not just internal experience. It's okay to be vulnerable about what didn't go well, you can get better info and help save others the same pain.?Crucial?: It's very important that when you split up your teams from functional data role teams, people keep in contact with functional role peers. If not, it can be very lonely as the only data engineer inside a domain. There is a significant turnover risk and a risk to not having scalable learning and knowledge transfer of data work if not handled well.Data mesh will lead to a lot of potential changes to people's ways of working, especially with each other. Don't shy away from that, people need to know you aren't forgetting they need career development and that you'll support them as they learn and get...

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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