Level Up Your Data Platform With Active Metadata
Data Engineering Podcast
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Fully Automated vs Human in the Loop
There's a sliding scale entire flow. And i think there ave cases where it makes sense to have human in the loop. There are probably some desertions, like auto scaling your data ware hers. You don't want that to happen directly by a machine. Maybe you just want ou final dashboard as an output, and then you want a detanginor to make that final determination. That new ance, i think, is what drives that.
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Summary
Metadata is the lifeblood of your data platform, providing information about what is happening in your systems. A variety of platforms have been developed to capture and analyze that information to great effect, but they are inherently limited in their utility due to their nature as storage systems. In order to level up their value a new trend of active metadata is being implemented, allowing use cases like keeping BI reports up to date, auto-scaling your warehouses, and automated data governance. In this episode Prukalpa Sankar joins the show to talk about the work she and her team at Atlan are doing to push this capability into the mainstream.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Prukalpa Sankar about how data platforms can benefit from the idea of "active metadata" and the work that she and her team at Atlan are doing to make it a reality
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what "active metadata" is and how it differs from the current approaches to metadata systems?
- What are some of the use cases that "active metadata" can enable for data producers and consumers?
- What are the points of friction that those users encounter in the current formulation of metadata systems?
- Central metadata systems/data catalogs came about as a solution to the challenge of integrating every data tool with every other data tool, giving a single place to integrate. What are the lessons that are being learned from the "modern data stack" that can be applied to centralized metadata?
- Can you describe the approach that you are taking at Atlan to enable the adoption of "active metadata"?
- What are the architectural capabilities that you had to build to power the outbound traffic flows?
- How are you addressing the N x M integration problem for pushing metadata into the necessary contexts at Atlan?
- What are the interfaces that are necessary for receiving systems to be able to make use of the metadata that is being delivered?
- How does the type/category of metadata impact the type of integration that is necessary?
- What are some of the automation possibilities that metadata activation offers for data teams?
- What are the cases where you still need a human in the loop?
- What are the most interesting, innovative, or unexpected ways that you have seen active metadata capabilities used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on activating metadata for your users?
- When is an active approach to metadata the wrong choice?
- What do you have planned for the future of Atlan and active metadata?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
- Atlan
- What is Active Metadata?
- Segment
- Zapier
- ArgoCD
- Kubernetes
- Wix
- AWS Lambda
- Modern Data Culture Blog Post
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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