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#202 Creating a Balanced, Sustainable Approach to Your Data Mesh Journey - Interview w/ Kiran Prakash
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.
Kiran's LinkedIn: https://www.linkedin.com/in/kiran-prakash/
Kiran's article on the 'Curse of the Data Lake Monster': https://www.thoughtworks.com/insights/blog/curse-data-lake-monster
In this episode, Scott interviewed Kiran Prakash, Principal Engineer at Thoughtworks.
Some key takeaways/thoughts from Kiran's point of view:
- ?Controversial?: You MUST have exec sponsorship to move forward with your data mesh implementation. You need the top-down push for necessary reorganization when the time comes. Scott note: only kinda controversial, really more often ignored :D
- ?Controversial?: Data mesh, if done well, doesn't need to have a huge barrier to entry. That's a misconception. If you think about gradual improvement/evolution, you'll be on the right track.
- "The Curse of the Data Lake Monster" was like the data field of dreams - there was expectation that if you build a great data lake, value will just happen. If you ingest and process as much as you can, the use cases will just happen. And it really wasn't the case. So we should apply product thinking to data to focus on what matters.
- The 'Curse' was a manifestation of Conway's Law - the strong separation between IT and the business led to mismatched goals and subpar outcomes. With microservices, that started to be much less of an issue on the operational plane so why not try with data?
- It's easy to lose sight of Conway's Law and aim for distributed architecture first but the organizations doing data mesh well are changing their architectural and cultural approaches and patterns together. Don't...
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By Data as a Product Podcast Network
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99 ratings
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.
Kiran's LinkedIn: https://www.linkedin.com/in/kiran-prakash/
Kiran's article on the 'Curse of the Data Lake Monster': https://www.thoughtworks.com/insights/blog/curse-data-lake-monster
In this episode, Scott interviewed Kiran Prakash, Principal Engineer at Thoughtworks.
Some key takeaways/thoughts from Kiran's point of view:
- ?Controversial?: You MUST have exec sponsorship to move forward with your data mesh implementation. You need the top-down push for necessary reorganization when the time comes. Scott note: only kinda controversial, really more often ignored :D
- ?Controversial?: Data mesh, if done well, doesn't need to have a huge barrier to entry. That's a misconception. If you think about gradual improvement/evolution, you'll be on the right track.
- "The Curse of the Data Lake Monster" was like the data field of dreams - there was expectation that if you build a great data lake, value will just happen. If you ingest and process as much as you can, the use cases will just happen. And it really wasn't the case. So we should apply product thinking to data to focus on what matters.
- The 'Curse' was a manifestation of Conway's Law - the strong separation between IT and the business led to mismatched goals and subpar outcomes. With microservices, that started to be much less of an issue on the operational plane so why not try with data?
- It's easy to lose sight of Conway's Law and aim for distributed architecture first but the organizations doing data mesh well are changing their architectural and cultural approaches and patterns together. Don't...