#51 A DevOps Angle to Data Mesh and WePay's Journey - Interview w/ Chris Riccomini

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.What the Heck is a Data Mesh?! Post: https://cnr.sh/essays/what-the-heck-data-meshChris' Twitter: https://twitter.com/criccominiChris' LinkedIn: https://www.linkedin.com/in/riccomini/Chris' website: https://cnr.sh/The Missing README book: https://themissingreadme.com/In this episode, Scott interviewed Chris Riccomini, a Software Engineer, Author, and Investor. Chris led the infrastructure team at WePay when they embarked on a data mesh journey and made a well-written post on thinking about data mesh in DevOps terms.Like a number of people/organizations that have come on the podcast, at WePay, Chris was pursuing the general goals of data mesh and was applying some of the approaches as well - but it was not nearly as cohesive as Zhamak laid things out.Their initial setup had two teams managing the pipeline/transformation infrastructure. Chris's team was mostly handing the extracting and loading and then there was a team of analytics engineers handling the transformations. The Transformation team saw a major increase in demand and quickly became overloaded -> a bottleneck. Chris' team also started to get overloaded so they knew they had to evolve.One way the team started to address the bottlenecks was by decentralizing the pipelines. Teams could make a request and a scalable and reliable pipeline would essentially get automatically set up for them. WePay is in the financial services space so as part of those pipelines, to prevent risk, teams could mark their sensitive/PII columns and the infra team also put in some autodetection capabilities to make sure they didn't miss any.WePay created a "canonical data representation" or CDR, which is pretty analogous to a data product in data mesh. Chris really liked WePay's use of the embedded analytics engineer to serve as a data product developer.One key innovation for WePay was tooling to...

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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