Streaming Data Architectures Open Space

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Rutherford, 4th flr.


Streaming Data Architectures


Silicon ValleyStreamingArchitecture


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SESSION + Live Q&A Interview Available

Streaming a Million likes/second: Real-time Interactions on Live Video

When a broadcaster like BBC streams a live video on LinkedIn, tens of thousands of viewers will watch it concurrently. Typically, hundreds of likes on the video will be streamed in real-time to all of these viewers. That amounts to a million likes/second streamed to viewers per live video. How do...

Akhilesh Gupta

Sr. Staff Software Engineer @LinkedIn

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Internet of Tomatoes: Building a Scalable Cloud Architecture

Five years ago we started on a journey of building a website monitoring tool. Little did I know that this would land up morphing into a full IoT based agriculture platform. Discussing if tomatoes need dark hours to sleep was not the type of question I had anticipated having to answer. But...

Flavia Paganelli

CTO and Founder @30Mhz

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Databases and Stream Processing: A Future of Consolidation

Are databases and stream processors wholly different things, or are they really two sides of the same coin? Certainly, stream processors feel very different from traditional databases when you use them. In this talk, we’ll explore why this is true, but maybe more importantly why it's...

Benjamin Stopford

Author of “Designing Event Driven Systems” & Senior Director @confluentinc

SESSION + Live Q&A Interview Available

From Batch to Streaming to Both

In this talk I walk through how the streaming data platform at Skyscanner evolved over time. This platform now processes hundreds of billions of events per day, including all our application logs, metrics and business events. But streaming platforms are hard, and we did not get it right on day...

Herman Schaaf

Senior Software Engineer @Skyscanner

SESSION + Live Q&A Silicon Valley

Machine Learning Through Streaming at Lyft

Uses of Machine Learning are pervasive in today’s world. From recommendations systems to ads serving. In the world of ride sharing we use Machine Learning to make a lot of decisions in realtime, for example: supply/demand curves are used to get an accurate ETA(estimated time of arrival) and...

Sherin Thomas

Senior Software Engineer @Lyft

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