#203 Panel: Making Privacy Practical and Scalable in Data and Data Mesh - Led by Debra Farber w/ Samia Rahman and Katharine Jarmul
#203 Panel: Making Privacy Practical and Scalable in Data and Data Mesh - Led by Debra Farber w/ Samia Rahman and Katharine Jarmul
#203 Panel: Making Privacy Practical and Scalable in Data and Data Mesh - Led by Debra Farber w/ Samia Rahman and Katharine Jarmul
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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.
Debra's LinkedIn: https://www.linkedin.com/in/privacyguru/
Debra's Shifting Privacy Left Podcast: https://shiftingprivacyleft.com/
Katharine's LinkedIn: https://www.linkedin.com/in/katharinejarmul/
Katharine's book: https://www.oreilly.com/library/view/practical-data-privacy/9781098129453/
Samia's LinkedIn: https://www.linkedin.com/in/samia-rahman-b7b65216/
Quick acronyms to know: PETs - privacy enhancing technologies; SMEs - subject matter experts
Scott Note Warning: there is some nerding out about how awesome it could be if some advanced privacy approaches and PETs were implemented at a broad scale across the industry to protect individual's privacy. It's pretty early days so warning about getting your hopes up :)
In this episode, guest host Debra Farber, privacy expert and host of the Shifting Privacy Left podcast facilitated a discussion with Katharine Jarmul, the author of the upcoming book Practical Data Privacy and Principal Data Scientist at Thoughtworks (guest of episode #157) and Samia Rahman, Director of Data and AI Strategy and Architecture at life sciences company Seagen (guest of episode #67).
Scott note: given this is a newer area, I wanted to share my takeaways rather than trying to reflect the nuance of the...
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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=sharingData 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=DataMeshRadioJoin Podchaser to...
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