Data Mesh Radio

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Sponsored by NextData, Zhamak's company that is helping ease data product creation.

For more great content from Zhamak, check out her book on data mesh, a book she collaborated on, her LinkedIn, and her Twitter.

In this episode, Scott and Zhamak covered the misconception many have when she says "leave the data where it is" - it's about leaving the ownership with those who should have it, not leaving the data in the source system. If you only do source system, all you have is current state, so your data isn't even immutable or bi-temporal! They also discussed the need to be smarter about processing data - should it be at the source or should it be on query? There isn't a universal approach but we also shouldn't have to move the data around just to process it, bring the processing to the data. Lastly, we need systems to get smarter around efficient processing. We have far too much manual work on making data processing efficient.

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Data Mesh Radio episode 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.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

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 here

Episode 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. You can download their Data Mesh for Dummies e-book (info gated) here.

Andrew's LinkedIn: https://www.linkedin.com/in/andrewsharp27/

Blog post "Data Mesh – Is this the evolutionary trigger to reinvigorate Data Governance?":

https://www.theoaklandgroup.co.uk/data-mesh-is-this-the-evolutionary-trigger-to-reinvigorate-data-governance/

In this episode, Scott interviewed Andrew Sharp, Data Governance Lead at the consulting company The Oakland Group based in Leeds in the United Kingdom.

Some key takeaways/thoughts from Andrew's point of view:

1. Data Governance changes that are necessary for data mesh are both a threat and an opportunity. Are you throwing away the good with the bad? Or can you reinvent your practices broadly to do governance better in general and clean out bad habits/approaches when moving to a federated model?
2. !Controversial! Of the four pillars of data mesh, governance is the most challenging and least mature.
3. There are no roadmaps to doing federated governance for data mesh well - and likely there can't really be a specific roadmap for all as every organization is different. People are just starting to find their way on how to do governance in data mesh well and people will need to explore for their organization.
4. ?Controversial? Data mesh will likely require a seismic - or maybe tectonic - shift in the way we approach data governance. That doesn't mean organizations have to completely change it all at once - that is overly high risk - but it probably won't work if we just try small shifts to our governance approach instead of small steps leading to a large...

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 here

Episode 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.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

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 here

Episode 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. You can download their Data Mesh for Dummies e-book (info gated) here.

Vlad's LinkedIn: https://www.linkedin.com/in/vladikk/

Vlad's book Learning Domain-Driven Design: Aligning Software Architecture and Business Strategy: https://www.oreilly.com/library/view/learning-domain-driven-design/9781098100124/

Before we jump in, the phrase DDD is used a LOT in this episode. It stands for Domain Driven Design. We've had past episodes on it but I want to make that clear. It's also important to note, there are some very specific terms used in DDD and it is easy to get overwhelmed. Look for the meaning and ignore the terms - we are designing how things work together, whether business capability, software systems, or general data flows.

There's also the importance of the difference between published language and ubiquitous language in DDD. Essentially, the ubiquitous language is the language of the domain and the published language is the language used to share information from the domain externally to other domains. So ubiquitous is internal facing between the business and software engineers in the domain and the published language is external facing to the rest of the organization.

In this episode, Scott interviewed Vlad Khononov, Author of Learning Domain Driven Design (DDD) through O'Reilly, Senior Cloud Architect at DoIT International, and independent consultant on DDD and distributed systems.

Some key takeaways/thoughts from Vlad's point of view:

1. DDD is a big topic to learn - you don't have to learn every aspect to take good value from it.
2. Make the implicit explicit. The more something is

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 here

Episode 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.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

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 here

Episode 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. You can download their Data Mesh for Dummies e-book (info gated) here.

Alexa's LinkedIn: https://www.linkedin.com/in/alexandrawestlake/

Alexa's Medium: https://medium.com/@westlakealexa

In this episode, Scott interviewed Alexa Westlake, Senior Data Analyst at Okta. To be clear though, she was only representing her own views.

Some key takeaways/thoughts from Alexa's point of view:

1. Data can be transformational but it is expensive to do the work. "Without literacy, all your analytics is is expensive." So do the data literacy work to make your data work actually valuable.
2. ?Controversial?: Don't focus your transformation initiatives around a negative, focus on a goal/aspiration. It is hard to maintain momentum around pain, especially as it starts to ease with early wins. While pain points can pique attention, shared goals and collective outcomes will keep people bought in and motivated.
3. ?Controversial?: Data is "not going to make or break you [in every case], it is there to help you be better, it is there to help you unlock your full potential." Use it to sharpen your competitive advantage.
4. As you scale your organization, if you do not prioritize data - the way you manage the people, processes, and tech around data - you will generate a ton of friction. It will create a "feedback loop of pain."
5. "Never jump and hope." You need to make sure you have the support to get your initiatives going and then maintain momentum.
6. !Important!: Data work is often not the number one priority for the stakeholders you serve. Understand that and keep close enough to make sure you are working on analytics to support their top priorities.
7. Alignment...

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 here

Episode 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.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Sponsored by NextData, Zhamak's company that is helping ease data product creation.

For more great content from Zhamak, check out her book on data mesh, a book she collaborated on, her LinkedIn, and her Twitter.

What tech is already available that could be used for data mesh? There are so many amazing approaches and technologies in data but they've been used for the pipeline approach only. We need to think more like developers - not accepting the grunt work or death by a thousand cuts of data - and take a hard look at what we've done historically in data and what should be replaced.

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 here

Data Mesh Radio episode 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.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

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 here

Episode 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.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

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 here

Episode 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. You can download their Data Mesh for Dummies e-book (info gated) here.

Salim's LinkedIn: https://www.linkedin.com/in/salim-syed-11981521/

Capital One Software: https://www.capitalone.com/software/

Capital One Slingshot: https://www.capitalone.com/software/solutions/

Capital One blog post on their use of Snowflake for data mesh: https://www.capitalone.com/software/blog/operationalizing-data-mesh/

In this episode, Scott interviewed Salim Syed, VP of Engineering at Capital One Software.

Some key takeaways/thoughts from Salim's point of view:

1. !Contrarian!: "Data mesh works with central policy, central tooling, but federated ownership." Work to federate your infrastructure ownership, not just data ownership.
2. No matter what size of domain or LoB, look to have the same responsibilities owned by each domain. They may fall under different people but the role they serve should look the same across the domains.
3. Get your roles and responsibilities established first, then look to start tackling other challenges.
4. Do your best to hide the data engineering complexity from your self-serve platform users. Help them do their job, not learn data engineering. Focus on creating a great experience for them in their workflows.
5. Think about data risk from an opportunity standpoint: if you have strong risk controls, you can feel more comfortable giving more people wider access - you know what is allowed so you can potentially