Track: Architecting for Failure
Location:
- Churchill, G flr.
Day of week:
- Monday
Complex systems fail in spectacular ways. Failure isn’t a question of if, but when. Resilient systems recover from failure; robust systems resist failure. In this track we’ll hear from experts who have designed systems that shifted from fragility to resilience and robustness in the face of failure. Attendees will learn architectural patterns and approaches that didn’t and did work, with take-aways that can be applied to their own systems.
by Gavin Stevenson
Technology R&D Engineering Lead @WilliamHill
How do you design a system that handles 7,000,000+ product price changes per day, 160TB of data flowing through your network, at peak 460 transactions per second, 3 billion transactions per year. We trade globally, taking and settling the millions of bets placed during the Grand National, World Cup, Euros, Cheltenham, Melbourne Cup, Superbowl, from the smallest local event to the biggest globally.
How do you make such a system resilient to failure, robust enough to route around slow...
by Richard Kasperowski
Author of The Core Protocols: A Guide to Greatness
Open Space
by Sid Anand
Data Architect @AgariInc, previously Engineering VP @Etsy, Search Architect @LinkedIn, and Cloud Architect @Netflix
Big Data companies (e.g. LinkedIn, Facebook, Google, and Twitter) have historically built custom data pipelines over bare metal in custom-designed data centers. In order to meet strict requirements on data security, fault-tolerance, cost control, job scalability, and uptime, they need to closely manage their core technology. Like serving systems (e.g. web application servers and OLTP databases) that need to be up 24x7 to display content to users, data pipelines...
by Sankalp Kohli
Engineer/Lead Cassandra Storage @Apple
Apple runs Cassandra at a very large scale which leads to some interesting challenges. This talk will cover many such challenges including Corruption Detection during Gossip, Distributed Deletes coupled with corrupt data and consistent host replacement.
by Martin Kleppmann
Software Engineer, Author, & Commiter to Samza and Avro
For the very simplest applications, a single database is sufficient, and then life is pretty good. But as your application needs to do more, you often find that no single technology can do everything you need to do with your data. And so you end up having to combine several databases, caches, search indexes, message queues, analytics tools, machine learning systems, and so on, into a heterogeneous infrastructure...
Now you have a new problem: your data is stored in several different...
by Sadek Drobi
Co-founder & CEO @prismic.io
For a service built to handle millions of requests/hour, it's insufficient to rely on latest trendy components or datastores to save you from system failures, instead it's necessary to deeply understand the properties and the mechanics of your system, and to partition its different dimensions to avoid a domino style failure cascade.
Partitioning time is about uncoupling subsystems that don't absolutely need to be updated in sync, whereas partitioning space is achieved by separating...
Tracks
Covering innovative topics
Monday, 7 March
-
Back to Java
What to expect in Java 9 and Spring 5
-
Stream Processing @ Scale
Big data, fast-moving data. Practical implementation lessons on Real-time Data
-
DevOps & CI/CD
Lessons/stories on optimizing the deployment pipeline
-
Head-to-Tail Functional Languages
Free-range Monads, Tackling immutability, tales from production, and more...
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Architecting for Failure
Your system will fail. Take control before it takes you with it
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21st Century Culture from Geeks on the Ground
New ways to organise technology companies and workplace culture
Tuesday, 8 March
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Architectures You've Always Wondered about
In-depth technical case studies from giants like: Microsoft, Netflix, Google, Twitter, and more...
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Close to the Metal
Get efficiency back into your code, concepts like: cache efficient algorithm and lock free data structures
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Containers (in production)
Real-world lessons on scalability and reliability in production container deployments
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Modern CS in the real world
Real-world Industry adoption of modern CS ideas
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Security, Incident Response & Fraud Detection
Master-level classes on building security into your system and responding to incidents when things go wrong.
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Optimizing You
Keeping life in balance is always a challenge. Learning lifehacks
Wednesday, 9 March
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Disrupting Finance
Technology advances in finance (blockchain, P2P, Machine Learning, API's)
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Modern Native Languages
Modern native languages: Safe efficiency with Go, Rust, Swift
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Full Stack Javascript
Level up Javascript with topics like Angular, React/ReactNative, Node, Mongo/Couch/Other, Falcor, GraphQL, etc
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Data Science & Machine Learning Methods
A developer's data science and machine learning toolkit
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Microservices for Mega-Architectures
Practical lessons on Microservices success.
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Modern Agile Development
Revisiting Agile today and tackling challenges we are seeing in the wild