Rerelease of #65 What's a Data Contract Between Friends - Setting Expectations with Data Contracts - Interview w/ Abe Gong

Due to health-related issues, we are on a temporary hiatus for new episodes. Please enjoy this rerelease of episode #65 with Abe Gong all about how people are implementing data contracts in the wild. There are so many ways people can just do only defensive data contracts and I think that is such a missed opportunity. Maybe it's where you will have to start but there's a much better way and we talk a bit about why I think that is so distressing that people aren't talking to each other.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 hereAbe's Twitter: @AbeGong / https://twitter.com/AbeGongAbe's LinkedIn: https://www.linkedin.com/in/abe-gong-8a77034/Great Expectations Community Page: https://greatexpectations.io/communityIn this episode, Scott interviewed Abe Gong, the co-creator Great Expectations (an open source data quality / monitoring / observability tool) and co-founder/CEO of Superconductive. One caveat before jumping in is that Abe is passionate about the topic and has created tooling to help address it. So try to view Abe's discussion of Great Expectations as an approach rather than a commercial for the project/product.To start the conversation, Abe shared some of his background experience living the pain of unexpected upstream data changes causing data chaos / lots of work to recover from and adapt. Part of where we need to get to using something like data contracts is to remove the need to recover in addition to adapting and move towards controlled/expected adaptation. Abe believes that the best framing for data contracts is to think about them as a set of expectations.To define expectations here, this would include not just schema but also the content of data, such as value ranges/types/distributions/relationships across tables/etc. So for instance, a column may be a one to five for rankings and then the application team changes it one to 10. The schema may not be broken - it is still passing whole numbers - but the new range is not within expectations so the contract is broken.At current, Abe sees the best way to not break social expectations is via

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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