The data that you have access to affects the questions that you can answer. By using external data sources you can drastically increase the range of analysis that is available to your organization. The challenge comes in all of the operational aspects of finding, accessing, organizing, and serving that data. In this episode Mark Hookey discusses how he and his team at Demyst do all of the DataOps for external data sources so that you don’t have to, including the systems necessary to organize and catalog the various collections that they host, the various serving layers to provide query interfaces that match your platform, and the utility of having a single place to access a multitude of information. If you are having trouble answering questions for your business with the data that you generate and collect internally, then it is definitely worthwhile to explore the information available from external sources.
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 Mark Hookey about Demyst Data, a platform for operationalizing external data
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Demyst is and the story behind it?
What are the services and systems that you provide for organizations to incorporate external sources in their data workflows?
Who are your target customers?
What are some examples of data sets that an organization might want to use in their analytics?
How are these different from SaaS data that an organization might integrate with tools such as Stitcher and Fivetran?
What are some of the challenges that are introduced by working with these external data sets?
If an organization isn’t using Demyst what are some of the technical and organizational systems that they will need to build and manage?
Can you describe how the Demyst platform is architected?
What have been the most complex or difficult engineering challenges that you have dealt with while building Demyst?
Given the wide variance in the systems that your customers are running, what are some strategies that you have used to provide flexible APIs for accessing the underlying information?
What is the process for you to identify and onboard a new data source in your platform?
What are some of the additional analytical systems that you have to run to manage your business (e.g. usage metering and analytics, etc.)?
What are the most interesting, innovative, or unexpected ways that you have seen Demyst used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Demyst?
When is Demyst the wrong choice?
What do you have planned for the future of Demyst?
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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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