Sign up to save your podcasts
Or
Share #198 How Do We Make Data Contracts Easy, Scalable, and Meaningful - Interview w/ Ananth Packkildurai
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
#198 How Do We Make Data Contracts Easy, Scalable, and Meaningful - Interview w/ Ananth Packkildurai
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.
Ananth's LinkedIn: https://www.linkedin.com/in/ananthdurai/
Schemata: https://schemata.app/
Data Engineering Weekly newsletter: https://www.dataengineeringweekly.com/
In this episode, Scott interviewed Ananth Packkildurai, Author of Data Engineering Weekly and the creator of Schemata.
Scott note: we discuss Schemata quite a bit in this episode but it's an open source offering that I think can fill in some of the major gaps in our tooling and even ways of working collaboratively around data.
Some key takeaways/thoughts from Ananth's point of view:
- !Important!: Collaboration around data is crucial. The best way to get people bought in on collaboration around data is to integrate into their workflow, not to create yet another one-off tool in yet another pane of glass.
- ?Controversial?: There is so much friction between initial data producers - the domain developers - and data consumers because they are constantly speaking past each other. The data consumers have to learn too much about the domain and the data producers rarely really understand the context of most analytical asks.
- Data creation is a human-in-the-loop problem. Autonomous data creation is not likely to create significant value because the systems can't understand the context well enough right now.
- As Zhamak has also pointed out, there is far too much tool fragmentation. It made sense with lots of readily available VC money and finding how to approach things with cloud but we need holistic approaches, not spot approaches to things...
View all episodes
By Data as a Product Podcast Network
5
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.
Ananth's LinkedIn: https://www.linkedin.com/in/ananthdurai/
Schemata: https://schemata.app/
Data Engineering Weekly newsletter: https://www.dataengineeringweekly.com/
In this episode, Scott interviewed Ananth Packkildurai, Author of Data Engineering Weekly and the creator of Schemata.
Scott note: we discuss Schemata quite a bit in this episode but it's an open source offering that I think can fill in some of the major gaps in our tooling and even ways of working collaboratively around data.
Some key takeaways/thoughts from Ananth's point of view:
- !Important!: Collaboration around data is crucial. The best way to get people bought in on collaboration around data is to integrate into their workflow, not to create yet another one-off tool in yet another pane of glass.
- ?Controversial?: There is so much friction between initial data producers - the domain developers - and data consumers because they are constantly speaking past each other. The data consumers have to learn too much about the domain and the data producers rarely really understand the context of most analytical asks.
- Data creation is a human-in-the-loop problem. Autonomous data creation is not likely to create significant value because the systems can't understand the context well enough right now.
- As Zhamak has also pointed out, there is far too much tool fragmentation. It made sense with lots of readily available VC money and finding how to approach things with cloud but we need holistic approaches, not spot approaches to things...