407 episodes

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

Data Mesh Radio Data as a Product Podcast Network

    • Technology
    • 5.0 • 8 Ratings

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

    #293 Adapting Product Management to Data - Finding the Customer Pain and the Value - Interview w/ Amritha Arun Babu Mysore

    #293 Adapting Product Management to Data - Finding the Customer Pain and the Value - Interview w/ Amritha Arun Babu Mysore

    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 here
    Episode 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.
    Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
    Amritha's LinkedIn: https://www.linkedin.com/in/amritha-arun-babu-a2273729/
    In this episode, Scott interviewed Amritha Arun Babu Mysore, Manager of Technical Product Management in ML at Amazon. To be clear, she was only representing only own views on the episode.

    In this episode, we use the phrase 'data product management' to mean 'product management around data' rather than specific to product management for data products. It can apply to data products but also something like an ML model or pipeline which will be called 'data elements' in this write-up.

    Some key takeaways/thoughts from Amritha's point of view:
    "As a product manager, it's just part of the job that you have to work backwards from a customer pain point." If you aren't building to a customer pain, if you don't have a customer, is it even a product? Always focus on who you are building a product for, why, and what is the impact. Data product management is different from software product management in a few key ways. In software, you are focused "on solving a particular user problem." In data, you have the same goal but there are often more complications like not owning the source of your data and potentially more related problems to solve across multiple users.In data product management, start from the user journey and the user problem then work back to not only what a solution looks like but also what data you need. What are the sources and then do they exist yet?Product management is about delivering business value. Data product management is no different. Always come back to the business value from addressing the user problem.Even your data cleaning methodology can impact your data. Make sure consumers that care - usually data scientists - are aware of the decisions you've made. Bring them in as early as possible to help you make decisions that work for all.?Controversial?: Try not to over customize your solutions but oftentimes you will still need to really consider the very specific needs of your...

    • 1 hr 5 min
    #292 Aligning Your Data Transformation to the Business - Interview w/ Nailya Sabirzyanova

    #292 Aligning Your Data Transformation to the Business - Interview w/ Nailya Sabirzyanova

    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 here
    Episode 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.
    Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
    Nailya's LinkedIn: https://www.linkedin.com/in/nailya-sabirzyanova-5b724310b/
    In this episode, Scott interviewed Nailya Sabirzyanova, Digitalization Manager at DHL and a PhD Candidate around data architecture and data driven transformation. To be clear, she was only representing her own views on the episode.
    Some key takeaways/thoughts from Nailya's point of view:
    When it came to microservices and digital transformation, we aligned our application and business architectures. Now, we have to align our application, business, and data architectures if we want to really move towards being data-driven.To do data transformation well, you must align it to your application architecture transformation. Otherwise, you have two things transforming simultaneously but not in conjunction.It's crucial to involve business counterparts in your data architectural transformation. They know the business architecture best and the data architecture is there to best serve the business. That is a prerequisite to enable continuous business value-generation from the transformation.Re a transformation, ask two simple questions to your stakeholders: What should this transformation enable? How should we enable it? It will give them a chance to share their pain points and their ideas on how to address them. The business stakeholders know their business problems better than the data people 😅Your approach to data mesh, at the start and throughout your journey, MUST be adapted to your organization's organizational model and ways of working. Everyone starts from completely different places.Data mesh won't work if you overly decentralize. You must find your balances between centralization and decentralization yourself.?Controversial?: Historically, teams were charged for data work and resources but with something like data mesh, they can manage their data and data costs far more efficiently. Framework processes, tools, and skills help teams to identify which data is valuable for their own or other domains and requires...

    • 1 hr 5 min
    #291 Panel: Data as a Product in Practice - Led by Jen Tedrow w/ Martina Ivaničová and Xavier Gumara Rigol

    #291 Panel: Data as a Product in Practice - Led by Jen Tedrow w/ Martina Ivaničová and Xavier Gumara Rigol

    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 here
    Episode 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.
    Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
    Jen's LinkedIn: https://www.linkedin.com/in/jentedrow/
    Martina's LinkedIn: https://www.linkedin.com/in/martina-ivanicova/
    Xavier's LinkedIn: https://www.linkedin.com/in/xgumara/
    Xavier's blog post on data as a product versus data products: https://towardsdatascience.com/data-as-a-product-vs-data-products-what-are-the-differences-b43ddbb0f123
    Results of Jen's survey 'The State of Data as a Product in the Real World' (NOT info-gated 😎👍): https://pathfinderproduct.com/wp-content/uploads/2023/12/2023-State-of-DaaP-Real-World-Study.pdf?mtm_campaign=daap-study&mtm_source=pp-blog&mtm_content=pdf-daap-study

    In this episode, guest host Jen Tedrow, Jen Tedrow, Director, Product Management at Pathfinder Product, a Test Double Operation (guest of episode #98) facilitated a discussion with Martina Ivaničová, Data Engineering Manager and Tech Ambassador at Kiwi.com (guest of episode #112), and Xavier Gumara Rigol, Data Engineering Manager at Oda (guest of episode #40). As per usual, all guests were only reflecting their own views.

    The topic for this panel was data as a product generally and especially how can we actually apply it to data in the real world. This is Scott's #1 most important aspect to get when it comes to doing data - especially data mesh - well. It's the holistic practice of applying product management approaches to data. It ends up shaping all the other data mesh principles and is a much broader topic than data mesh is in his view. But it can...

    • 1 hr 1 min
    #290 Applying Platform Engineering Best Practices to Your Mesh Data Platform - Interview w/ Tom De Wolf

    #290 Applying Platform Engineering Best Practices to Your Mesh Data Platform - Interview w/ Tom De Wolf

    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 here
    Episode 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.
    Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
    Tom's LinkedIn: https://www.linkedin.com/in/tomdw/
    Data Mesh Belgium: https://www.meetup.com/data-mesh-belgium/
    Video by Tom: 'Platform Building for Data Mesh - Show me how it is done!': https://www.youtube.com/watch?v=wG2g67RHYyo
    ACA Group Data Mesh Landing Page: https://acagroup.be/en/services/data-mesh/
    In this episode, Scott interviewed Tom De Wolf, Senior Architect and Innovation Lead at ACA Group and Host of the Data Mesh Belgium Meetup.
    Some key takeaways/thoughts from Tom's point of view:
    Platform engineering, at its core, is about delivering a great and reliable self-service experience to developers. That's just as true in data as in software. Focus on automation, lowering cognitive load, hiding complexity, etc. If provisioning decision specifics don't matter, why make developers deal with them?The key to a good platform is something your users _want_ to use not simply must use. That's your user experience measuring stick.When building a platform, you want to hide a lot of the things that don't matter. But when you start, especially with a platform in data mesh, there will be many things you aren't sure if they matter. That's okay, automate those decisions that don't matter as you find them but exposing them early is normal/fine.Relatedly, make that hiding easy to see through the curtain if the developer cares. Sometimes it matters to 5% of use cases but also often, engineers really want to understand the details just because they are engineers 😅 Make a platform where people can customize their experience where possible without going overboard.?Controversial?: Few - if any - current tools in data are "aware" of the data product, they are still focused on their specific tasks instead of the target of creating an actual

    • 1 hr 5 min
    #289 Building the Right Foundations for Generative AI - Interview w/ May Xu

    #289 Building the Right Foundations for Generative AI - Interview w/ May Xu

    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 here
    Episode 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.
    Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
    May's LinkedIn: https://www.linkedin.com/in/may-xu-sydney/
    In this episode, Scott interviewed May Xu, Head of Technology, APAC Digital Engineering at Thoughtworks. To be clear, she was only representing her own views on the episode.
    We will use the terms GenAI and LLMs to mean Generative AI and Large-Language Models in this write-up rather than use the entire phrase each time :)

    Some key takeaways/thoughts from May's point of view:
    Garbage-in, garbage-out: if you don't have good quality data - across many dimensions - and "solid data architecture", you won't get good results from trying to leverage LLMs on your data. Or really on most of your data initiatives 😅There are 3 approaches to LLMs: train your own, start from pre-trained and tune them, or use existing pre-trained models. Many organizations should focus on the second.Relatedly, per a survey, most organizations understand they aren't capable of training their own LLMs from scratch at this point.It will likely take any organization around three months at least to train their own LLM from scratch. Parallel training and throwing money at the problem can only take you so far. And you need a LOT of high-quality data to train an LLM from scratch.There's a trend towards more people exploring and leveraging models that aren't so 'large', that have fewer parameters. They can often perform specific tasks better than general large parameter models.Similarly, there is a trend towards organizations exploring more domain-specific models instead of general purpose models like ChatGPT.?Controversial?: Machines have given humanity scalability through predictability and reliability. But GenAI inherently lacks predictability. You have to treat GenAI like working with a person and that means less inherent trust in their responses.Generative AI is definitely not the right approach to all problems. As always, you have to understand your tradeoffs. If you don’t feed your GenAI the right information, it will give you bad answers. It only knows what it

    • 51 min
    Major Programming Announcement

    Major Programming Announcement

    Announcing moving to one episode per week :)

    • 4 min

Customer Reviews

5.0 out of 5
8 Ratings

8 Ratings

jgperrin ,

Must listen for practitioners

Scott does a great job at interviewing data mesh experts and practitioners around the globe. He’s opinionated but fair, he’s a great listener and summary maker, he’s a wonderful asset to this community which he helps with so many outlet, like this podcasts, the Slack community, his activity on LinkedIn… I would personally recommend episode #130 but I may be biased.

pospischil ,

Excellent show

Great podcast for those interested in data mesh / modern data environment. Scott is a great interviewer and has done an amazing job getting wonderful guests on the show, discussing real world challenges with data mesh.

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