DataOps For Streaming Systems With Lenses.io

Episode • Jul 6, 2020 • 45m

Summary

There are an increasing number of use cases for real time data, and the systems to power them are becoming more mature. Once you have a streaming platform up and running you need a way to keep an eye on it, including observability, discovery, and governance of your data. That’s what the Lenses.io DataOps platform is built for. In this episode CTO Andrew Stevenson discusses the challenges that arise from building decoupled systems, the benefits of using SQL as the common interface for your data, and the metrics that need to be tracked to keep the overall system healthy. Observability and governance of streaming data requires a different approach than batch oriented workflows, and this episode does an excellent job of outlining the complexities involved and how to address them.

Announcements

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Your host is Tobias Macey and today I’m interviewing Andrew Stevenson about Lenses.io, a platform to provide real-time data operations for engineers

Interview

Introduction
How did you get involved in the area of data management?
Can you start by describing what Lenses is and the story behind it?
What is your working definition for what constitutes DataOps?
- How does the Lenses platform support the cross-cutting concerns that arise when trying to bridge the different roles in an organization to deliver value with data?
  - What are the typical barriers to collaboration, and how does Lenses help with that?
Many different systems provide a SQL interface to streaming data on various substrates. What was your reason for building your own SQL engine and what is unique about it?
What are the main challenges that you see engineers facing when working with streaming systems?
What have you found to be the most notable evolutions in the community and ecosystem around Kafka and streaming platforms?
One of the interesting features in the recent release is support for topologies to map out the relations between different producers and consumers across a stream. Why is that a difficult problem and how have you approached it?
On the point of monitoring, what are the foundational challenges that engineers run into when trying to gain visibility into streams of data?
- What are some useful strategies for collecting and analyzing traces of data flows?
As with many things in the space of data, local development and pre-production testing and validation are complicated due to the potential scale and variability of a production system. What advice do you have for engineers who are trying to establish a sustainable workflow for streaming applications?
- How do you facilitate the CI/CD process for enabling a culture of testing and establishing confidence in the correct functionality of your systems?
How is the Lenses platform implemented and how has its design evolved since you first began working on it?
What are some of the specifics of Kafka that you have had to reconsider or redesign as you began adding support for additional streaming engines (e.g. Redis and Pulsar)?
What are some of the most interesting, unexpected, or innovative ways that you have seen the Lenses platform used?
What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with Lenses?
When is Lenses the wrong choice?
What do you have planned for the future of the platform?

Contact Info

LinkedIn
@StevensonA_D on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

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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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