An Exploration Of The Expectations, Ecosystem, and Realities Of Real-Time Data Applications
Data Engineering Podcast
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Do You Have a Batch or a Real Time Environment?
Real time analytics can be done in real time at a much lower cost, oftentimes than doing it on the bat system. It's more driven by the use case s thef forsting and its also driven by queries, not just the data arrival. If you really have used cases with the quarries not never stop coming, that is a number on ontin a picking a real tim anlytic solution.
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Summary
Data has permeated every aspect of our lives and the products that we interact with. As a result, end users and customers have come to expect interactions and updates with services and analytics to be fast and up to date. In this episode Shruti Bhat gives her view on the state of the ecosystem for real-time data and the work that she and her team at Rockset is doing to make it easier for engineers to build those experiences.
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 Shruti Bhat about the growth of real-time data applications and the systems required to support them
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what is driving the adoption of real-time analytics?
- architectural patterns for real-time analytics
- sources of latency in the path from data creation to end-user
- end-user/customer expectations for time to insight
- differing expectations between internal and external consumers
- scales of data that are reasonable for real-time vs. batch
- What are the most interesting, innovative, or unexpected ways that you have seen real-time architectures implemented?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Rockset?
- When is Rockset the wrong choice?
- What do you have planned for the future of Rockset?
Contact Info
- @shrutibhat on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Rockset
- Embedded Analytics
- Confluent
- Kafka
- AWS Kinesis
- Lambda Architecture
- Data Observability
- Data Mesh
- DynamoDB Streams
- MongoDB Change Streams
- Bigeye
- Monte Carlo Data
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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