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.
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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
- 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?
- 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 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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