Past Presentations

Panel: SQL Over Streams, Ask the Experts

Queries over streams are generally "continuous," executing for long periods of time and returning incremental results. Yet operations over streams must have the ability to be monotonic. New Generation of Stream Processing Engines has added support for Stream SQL. This AMA / panel features a...

Julian Hyde Original Developer @ApacheCalcite, Co-Founder SQLstream, & Architect @Hortonworks
Tyler Akidau Engineer @Google & Founder/Committer on Apache Beam
Jay Kreps Co-Founder and CEO @Confluent
Michael Armbrust Initial Author of Apache Spark SQL & Leads Streaming Team @Databricks
Stephan Ewen Committer @ApacheFlink, CTO @dataArtisans
Experiences with Apache Beam

Apache Beam is an emerging programming API for streaming applications. This talk will discuss experience with Apache Beam from the "outside", including developing a runner for an existing streaming engine and how well Beam supports low latency streaming paradigms including complex analytics.

Dan Debrunner STSM, IBM Streams Programming Model Architect
Fix Spark Failures and Bottlenecks Faster & Easier

This talk presents the results of analyzing many Spark jobs on many multi-tenant production clusters. Kirk discusses common issues seen, the symptoms of those issues, and how developers can address them. At Pepperdata, we have gathered trillions of performance data points on production clusters...

Kirk Lewis Field Engineer @Pepperdata
Patterns of Streaming Applications

Stream processing engines are becoming pivotal in analyzing data. They have evolved beyond a data transport and simple processing machinery, to one that's capable of complex processing. The necessary features and building blocks of these engines are well known. And most capable engines have a...

Monal Daxini Distributed Systems Engineer / Leader @Netflix
Human-Centric Machine Learning Infrastructure @Netflix

Netflix has over 100 data scientists applying machine learning to a wide range of business problems from title popularity predictions to quality of streaming optimizations. Our unique culture gives data scientists plenty of freedom to choose the modeling approach, libraries, and even the...

Ville Tuulos Machine Learning Infrastructure Engineer @Netflix
Training Deep Learning Models at Scale on Kubernetes

Deep Learning has recently become very important for all kinds of AI applications from conversational chatbots to self-driving cars. In this talk, we will talk about how we use deep learning for natural language processing, utilize Tensorflow for training deep learning models, run Tensorflow on...

Deepak Bobbarjung Founding Engineer @PassageAI
Mitul Tiwari CTO @PassageAI


Matt Zimmer Real-time Data Infrastructure Senior Engineer @Netflix

Custom, Complex Windows @Scale Using Apache Flink

What's the focus of your work?

Recently, I’ve primarily been building data platforms. That is, platforms to enable Data and Software Engineers to collect and process data.

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Serhat Yilmaz Software Engineer @Facebook

Data Decisions With Realtime Stream Processing

QCon: What's the focus of your work and of the team that you're on at Facebook?

Rajesh: My team is working on stream processing, and we are part of the real-time data organization which focuses on faster, simpler, and smarter delivery of data. We want to reduce the time to results for people and our data driven products and people wait on that rely on data driven. Our organization encompasses the stream...

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Ville Tuulos Machine Learning Infrastructure Engineer @Netflix

Human-Centric Machine Learning Infrastructure @Netflix

Can you give an example of some of the questions you get from data scientists when you are trying to deploy models?

When it comes to common questions, as boring as it may sound, my experience is that machine learning infrastructure is much more about data than science. Most questions we get are related to data: how do I find the data I need, how do I set up the data pipeline, how do I handle the somewhat non-trivial amounts of data in python and R,...

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