Building a well managed data ecosystem for your organization requires a holistic view of all of the producers, consumers, and processors of information. The team at Metaphor are building a fully connected metadata layer to provide both technical and social intelligence about your data. In this episode Pardhu Gunnam and Mars Lan explain how they have designed the architecture and user experience to allow everyone to collaborate on the data lifecycle and provide opportunities for automation and extensible workflows.
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- 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 Pardhu Gunnam and Mars Lan about Metaphor Data, a platform aiming to be the system of record for your data ecosystem
- How did you get involved in the area of data management?
- Can you describe what Metaphor is and the story behind it?
- On your site it states that you are aiming to be the "system of record" for your data platform. Can you unpack that statement and its implications?
- What are the shortcomings in the "data catalog" approach to metadata collection and presentation?
- Who are the target end users of Metaphor and what are the pain points for each persona that you are prioritizing?
- How has that focus informed your priorities for user experience design and feature development?
- Can you describe how the Metaphor platform is architected?
- What are the lessons that you learned from your work at DataHub that have informed your work on Metaphor?
- There has been a huge amount of focus on the "modern data stack" with an assumption that there is a cloud data warehouse as the central component that all data flows through. How does Metaphor’s design allow for usage in platforms that aren’t dominated by a cloud data warehouse?
- What are some examples of information that you can extract through integrations with an organization’s communication platforms?
- Can you talk through a few example workflows where that information is used to inform the actions taken by a team member?
- What is your philosophy around data modeling or schema standardization for metadata records?
- What are some of the challenges that teams face in stitching together a meaningful set of relations across metadata records in Metaphor?
- What are some of the features or potential use cases for Metaphor that are overlooked or misunderstood as you work with your customers?
- What are the most interesting, innovative, or unexpected ways that you have seen Metaphor used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Metaphor?
- When is Metaphor the wrong choice?
- What do you have planned for the future of Metaphor?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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