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Intermix - Featured Startup SF '18

In this blog series leading up to our SF18 conference, we invite our featured startups to tell us more about their data engineering challenges. Today, we speak with Intermix, an early-stage company building performance analytics tools for Amazon Redshift.

 
Q:  What surprised you most as an engineer about the work you did that you'll be telling us about in your talk? 

Paul Lappas: The fact that using AWS Lambda (to push Redshift data into Cloudwatch) guarantees "at least once" semantics versus our assumption of "exactly once". This means that your application needs to support the possibility that the Lambda job will run more than once with the same message body and inputs.  This fact is not documented well and so it took us a bit to figure out what was happening.


Q: What do you think a listener will get out of this this talk vs. other talks on distributed data processing and data versioning that they've previously heard?

Paul Lappas: We'll discuss solving a real-world problem that customers care about - many talks are low-level and as such can't focus on end-user experience as much. 
 
Q: Is there any additional back-story to you and your company that you want our audience to know?
 
Paul Lappas: We are building a new class of monitoring company's for data platforms. We're positioning it as "APM for Data'. We are data engineers ourselves and so are excited to be solving a real-world problem for other data teams!
 
 
 
 

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About the Startups Track

The data-oriented Startups Track at DataEngConf features dozen of startups forging ahead with innovative approaches to data and new data technologies. We find the most interesting startups at the intersection of ML, AI, data infrastructure and new applications of data science and highlight them in technical talks by their CTOs and lead engineers who are building these platforms. 

Data Engineering, Data Warehouse, Data Strategy

Robert Winslow

Written by Robert Winslow

Robert is a seasoned software consultant with a decade of experience shipping great products. He thrives in early-stage startup environments, and works primarily in Go, Python, and Rust. He has led backend development at companies like RankScience and Spot.com; created a rigorous, open-source time-series benchmarking suite for InfluxData; and rapidly prototyped software in a skunkworks-type product lab. He’s taught graduate statistics at GalvanizeU and mentored at the Stanford d.school. He helps maintain Google’s FlatBuffers project, one of the world’s fastest serialization libraries. A colleague once described him as “the developer equivalent of ‘The Wolf’ from Pulp Fiction."

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