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#224 Building Out Scalable Automated Access for Data Mesh at Disney Streaming - Interview w/ Himateja Madala
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.
Himateja's LinkedIn: https://www.linkedin.com/in/himatejam/
Himateja's AWS ReInvent presentation (link starts at her part): https://youtu.be/y1p0BGsPxvw?t=1991
In this episode, Scott interviewed Himateja Mandala, Senior Data Engineering Manager and Head of the Data Mesh Data Platform at Disney Streaming. To be clear, she was only representing her own views on the episode.
Some key takeaways/thoughts from Himateja's point of view:
- ?Controversial?: Your existing data platform(s) might not be able to serve data mesh well, even with reasonable augmentation - especially if your data platform has become hard to change. You might have to build from scratch.
- When the data platform's key users aren't part of the centralized team, you need to think about enabling automated capabilities by default, e.g. security the second data lands or easy to leverage and understand monitoring/observability.
- ?Controversial?: Data products serving different use cases often end up looking relatively different. Is your data product for dashboards and reporting/analytics; is it for serving a recommendation engine or machine learning model; or is it more for internal usage? Be okay with data products not being uniform.
- Even if your data mesh platform operates outside the traditional paradigms, many data producers - especially data engineers - will still be thinking data pipelines. Be prepared for that, it's an ingrained way of thinking for many.
- Data contracts are very helpful in defining and maintaining quality. If you set up good observability on your data products, owners can quickly identify when there
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By Data as a Product Podcast Network
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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.
Himateja's LinkedIn: https://www.linkedin.com/in/himatejam/
Himateja's AWS ReInvent presentation (link starts at her part): https://youtu.be/y1p0BGsPxvw?t=1991
In this episode, Scott interviewed Himateja Mandala, Senior Data Engineering Manager and Head of the Data Mesh Data Platform at Disney Streaming. To be clear, she was only representing her own views on the episode.
Some key takeaways/thoughts from Himateja's point of view:
- ?Controversial?: Your existing data platform(s) might not be able to serve data mesh well, even with reasonable augmentation - especially if your data platform has become hard to change. You might have to build from scratch.
- When the data platform's key users aren't part of the centralized team, you need to think about enabling automated capabilities by default, e.g. security the second data lands or easy to leverage and understand monitoring/observability.
- ?Controversial?: Data products serving different use cases often end up looking relatively different. Is your data product for dashboards and reporting/analytics; is it for serving a recommendation engine or machine learning model; or is it more for internal usage? Be okay with data products not being uniform.
- Even if your data mesh platform operates outside the traditional paradigms, many data producers - especially data engineers - will still be thinking data pipelines. Be prepared for that, it's an ingrained way of thinking for many.
- Data contracts are very helpful in defining and maintaining quality. If you set up good observability on your data products, owners can quickly identify when there