#263 Panel: Applying Site Reliability Engineering Practices to Data - Led by Emily Gorcenski w/ Amy Tobey and Alex Hidalgo

Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/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. 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.Emily's LinkedIn: https://www.linkedin.com/in/emily-gorcenski-0a3830200/Amy's LinkedIn: https://www.linkedin.com/in/amytobey/Alex's LinkedIn: https://www.linkedin.com/in/alex-hidalgo-6823971b7/Alex's Book Implementing Service Level Objectives: https://www.alex-hidalgo.com/the-slo-bookIn this episode, guest host Emily Gorcenski, Head of Data and AI for Thoughtworks Europe (guest of episode #72) facilitated a discussion with Amy Tobey, Senior Principal Engineer at Equinix and Alex Hidalgo, Principal Reliability Advocate at Nobl9. As per usual, all guests were only reflecting their own views.The topic for this panel was applying reliability engineering practices to data. This is different than engineering for data reliability which is focused on data quality specifically. The overall concept is taking what we've learned from reliability engineering across disciplines but mostly in software, especially SRE/site reliability engineering, and bringing those learnings to data to make data - especially data production and serving - more reliable and scalable. Scott note: this is probably one of the most frustrating topics in data for me because it feels like it's basic foundational work yet most organizations aren't tackling this well yet if at all really. The best starting...

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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