Track Overview
Modern Distributed Architectures
Building robust distributed systems is hard -- it requires a solid theoretical base grounded in hard-earned practical experience. What architectural patterns should you be aware of when designing for your distributed needs? What are some anti-patterns that have been revealed through war stories from the field? What do recently-released cloud services (e.g. AWS Lambda, GCP dataflow & dataproc, AWS Kinesis) now make possible in cloud-based architectures?
From this track
Straggler Free Data Processing in Cloud Dataflow
One of the main causes of performance problems in distributed data processing systems (from the original MapReduce to modern Spark and Flink) is "stragglers." Stragglers are parts of the input that take an unexpectedly long time to process, delaying the completion of the whole job, and wasting...
Eugene Kirpichov
Cloud Dataflow Sr SE @Google
Spotify's Reliable Event Delivery System
Spotify’s event delivery system is one of the foundational pieces of Spotify’s data infrastructure. It has a key requirement to reliably deliver complete data with a predictable latency and make it available to Spotify developers via well-defined interface. Delivered data is than used to...
Igor Maravic
Software Engineer @Spotify
Realtime & Personalized Notifications @Twitter
Twitter Notifications Infrastructure enables hundreds of millions of users to stay informed about what’s going on in their Twitter world. Our systems process large volumes of data (aka the Twitter firehose) and deliver realtime and personalized notifications to all kinds of users, ranging from...
Gary Lam
Tech Lead Notifications, Staff Software Engineer @ Twitter
Saurabh Pathak
Leads Notifications Team @Twitter
Distributed Systems Theory for Practical Engineers
Distributed Systems are a complex topic. There's abundant research about it but sometimes it is hard for a beginner to know where to start. I would like to outline the main concepts of distributed systems, so the interested person can have a clear path on how to start their own research as well....
Alvaro Videla
Distributed Systems Engineer
Distributed Architectures Open Space
Causal Consistency For Large Neo4j Clusters
In this talk we'll explore the new Causal clustering architecture for Neo4j. We'll see how Neo4j uses the Raft protocol for a robust underlay for intensive write operations, and how the asynchronous new scale-out mechanism provides enormous capacity for very demanding graph workloads. We'll...
Jim Webber
Chief Scientist @Neo4j
Speakers from this track
Eugene Kirpichov
Cloud Dataflow Sr SE @Google
Eugene is a Senior Software Engineer on the Cloud Dataflow team at Google, working primarily on the autoscaling and work rebalancing secret sauce as well as the Apache Beam programming model. He is also very interested in functional programming languages, data visualization (especially...
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Igor Maravic
Software Engineer @Spotify
As a part of the band he worked on developing and maintaining Spotify's gateways, migrating mobile clients from using custom TLV protocol to HTTP, designing and developing continuous delivery infrastructure, stress testing services... Currently he's living and breathing event delivery.
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Gary Lam
Tech Lead Notifications, Staff Software Engineer @ Twitter
Gary Lam is the technical lead of the Notifications timeline team at Twitter. He and his team are responsible for Twitter's notifications platform and notifications timeline. Prior to working on Notifications he was the technical lead for Twitter's Machine Learning Platform and helped build out...
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Saurabh Pathak
Leads Notifications Team @Twitter
Saurabh Pathak is an Engineering Manager at Twitter where he leads the Notifications timeline team which is a product & platform team rolled into one. Prior to joining Twitter, Saurabh was an engineer at Netflix where he architected & built their social infrastructure from scratch. He also worked...
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Alvaro Videla
Distributed Systems Engineer
Alvaro Videla works as a Distributed Systems Engineer and previously was a Core Developer for RabbitMQ. Before moving to Europe he used to work in Shanghai where he helped building one of Germany biggest dating websites. He co-authored the book "RabbitMQ in Action" for Manning Publishing. Some of...
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Jim Webber
Chief Scientist @Neo4j
Dr. Jim Webber is Chief Scientist with Neo Technology the company behind the popular open source graph database Neo4j, where he where he works on R&D for highly scalable graph databases and writes open source software. Jim has written two books on integration and distributed systems:...
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Track Host
Danny Yuan
Real-time Streaming Lead @Uber
Track Host
Danny Yuan
Real-time Streaming Lead @Uber
Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. Prior to joining Uber, he worked on building Netflix’s cloud platform. His work includes predictive autoscaling, distributed tracing service, real-time data pipeline that...
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