Conference:March 6-8, 2017
Workshops:March 9-10, 2017
Track: Modern Distributed Architectures
Location:
- Fleming, 3rd flr.
Day of week:
- Wednesday
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?
by Gary Lam
Tech Lead Notifications, Staff Software Engineer @ Twitter
by Saurabh Pathak
Leads Notifications Team @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 Katy Perry with ~95M followers to brand new users trying out our product for the very first time. We will give an overview of unique challenges in building our notifications infrastructure, present how...
by Alvaro Videla
Distributed Systems Engineer
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. In this talk I will review the different models: asynchronous vs. synchronous distributed systems; message passing vs shared memory communication; failure detectors and...
by Igor Maravic
Software Engineer @Spotify
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 produce Discover Weekly, Fresh Finds, Spotify Party and many other Spotify features. Currently 1M events is delivered via Spotify's event delivery system every second. To...
by Eugene Kirpichov
Cloud Dataflow Sr SE @Google
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 resources that stay idle. Stragglers can happen due to imbalance of data distribution or processing complexity, hardware/networking anomalies, and a variety of other...
by Jim Webber
Chief Scientist @Neo4j
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 discuss the cluster architecture's new causal consistency model. Causal consistency is a big leap forward compared to the commonplace...
Tracks
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Architecting for Failure
Building fault tolerate systems that are truly resilient
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Architectures You've Always Wondered about
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Modern Distributed Architectures
Migrating, deploying, and realizing modern cloud architecture.
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Fast & Furious: Ad Serving, Finance, & Performance
Learn some of the tips and technicals of high speed, low latency systems in Ad Serving and Finance
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Java - Performance, Patterns and Predictions
Skills embracing the evolution of Java (multi-core, cloud, modularity) and reenforcing core platform fundamentals (performance, concurrency, ubiquity).
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Performance Mythbusting
Performance myths that need busting and the tools & techniques to get there
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Dark Code: The Legacy/Tech Debt Dilemma
How do you evolve your code and modernize your architecture when you're stuck with part legacy code and technical debt? Lessons from the trenches.
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Modern Learning Systems
Real world use of the latest machine learning technologies in production environments
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Practical Cryptography & Blockchains: Beyond the Hype
Looking past the hype of blockchain technologies, alternate title: Weaselfree Cryptography & Blockchain
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Applied JavaScript - Atomic Applications and APIs
Angular, React, Electron, Node: The hottest trends and techniques in the JavaScript space
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Containers - State Of The Art
What is the state of the art, what's next, & other interesting questions on containers.
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Observability Done Right: Automating Insight & Software Telemetry
Tools, practices, and methods to know what your system is doing
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Data Engineering : Where the Rubber meets the Road in Data Science
Science does not imply engineering. Engineering tools and techniques for Data Scientists
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Modern CS in the Real World
Applied, practical, & real-world dive into industry adoption of modern CS ideas
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Workhorse Languages, Not Called Java
Workhorse languages not called Java.
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Security: Lessons Learned From Being Pwned
How Attackers Think. Penetration testing techniques, exploits, toolsets, and skills of software hackers
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Engineering Culture @{{cool_company}}
Culture, Organization Structure, Modern Agile War Stories
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Softskills: Essential Skills for Developers
Skills for the developer in the workplace