Adopting Real-Time Data At Organizations Of Every Size
Data Engineering Podcast
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Streaming and the Challenges It Faces
The challenges that come from the inherent limitations of some specific stream processors in my contention. have been holding us back, right? We need to get to a world where the underlying streaming systems are on par or close with the capabilities of batch data warehouses. So state management needs to basically no longer be a consideration and people should stop creating window size restrictions for their Kafka topics. And as long as that is a thing, streaming will never be as easy as batch database technology.
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Summary
The term "real-time data" brings with it a combination of excitement, uncertainty, and skepticism. The promise of insights that are always accurate and up to date is appealing to organizations, but the technical realities to make it possible have been complex and expensive. In this episode Arjun Narayan explains how the technical barriers to adopting real-time data in your analytics and applications have become surmountable by organizations of all sizes.
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 Arjun Narayan about the benefits of real-time data for teams of all sizes
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what your conception of real-time data is and the benefits that it can provide?
- types of organizations/teams who are adopting real-time
- consumers of real-time data
- locations in data/application stacks where real-time needs to be integrated
- challenges (technical/infrastructure/talent) involved in adopting/supporting streaming/real-time
- lessons learned working with early customers that influenced design/implementation of Materialize to simplify adoption of real-time
- types of queries that are run on materialize vs. warehouse
- how real-time changes the way stakeholders think about the data
- sourcing real-time data
- What are the most interesting, innovative, or unexpected ways that you have seen real-time data used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Materialize to support real-time data applications?
- When is real-time the wrong choice?
- What do you have planned for the future of Materialize and real-time data?
Contact Info
- @narayanarjun on Twitter
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
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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