Real-time capabilities have quickly become an expectation for consumers. The complexity of providing those capabilities is still high, however, making it more difficult for small teams to compete. Meroxa was created to enable teams of all sizes to deliver real-time data applications. In this episode DeVaris Brown discusses the types of applications that are possible when teams don't have to manage the complex infrastructure necessary to support continuous data flows.
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 DeVaris Brown about the impact of real-time data on business opportunities and risk profiles
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Meroxa is and the story behind it?
How have the focus and goals of the platform and company evolved over the past 2 years?
Who are the target customers for Meroxa?
What problems are they trying to solve when they come to your platform?
Applications powered by real-time data were the exclusive domain of large and/or sophisticated tech companies for several years due to the inherent complexities involved. What are the shifts that have made them more accessible to a wider variety of teams?
What are some of the remaining blockers for teams who want to start using real-time data?
With the democratization of real-time data, what are the new categories of products and applications that are being unlocked?
How are organizations thinking about the potential value that those types of apps/services can provide?
With data flowing constantly, there are new challenges around oversight and accuracy. How does real-time data change the risk profile for applications that are consuming it?
What are some of the technical controls that are available for organizations that are risk-averse?
What skills do developers need to be able to effectively design, develop, and deploy real-time data applications?
How does this differ when talking about internal vs. consumer/end-user facing applications?
What are the most interesting, innovative, or unexpected ways that you have seen Meroxa used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Meroxa?
When is Meroxa the wrong choice?
What do you have planned for the future of Meroxa?
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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