Track: Stream Processing at Large
The software industry has learned that the world’s data can be represented as unbounded queues of changes. It can be sliced into sliding windows. It can be aggregated, rolled up, and analyzed. We can choose a number of ways to do this work such as using Kafka Streams or Spark Streaming. We can opt for Apache Beam, Storm, Samza, Flume, or Flink. We have a large pool of options on which we can build powerful systems, but there is accidental complexity lurking in any of the choices:
- What if I need to rebuild all the data?
- How do I know when my system is not healthy?
- How do I reason about time in this system?
- What if things arrive out of order?
- How do I know things have arrived?
This track walks through uses of streaming technologies at large, the problems encountered, and how teams are coping with the state of this new world. As we approach maturity in streaming systems the companies using these systems are growing ecosystems and best practices around building and operating them. They are discovering new ways to reason about monitoring, testing, performance, and failure. This track is an opportunity to learn from their experiences.
Architectures You've Always Wondered about
Case studies from the most relevant names in software
How our industry is being attacked and what you can do about it.
Chaos & Resilience
Failures, edge cases and how we're embracing them.
Developer Experience: Level up your engineering effectiveness
Trends, tools and projects that we're using to maximally empower your developers.
High Velocity Dev Teams
Working Smarter as a team. Improving value delivery of engineers. Lean and Agile principles.
Immutable Infrastructures: Orchestration, Serverless, and More
What's next in infrastructure. How cloud function like lambda are making their way into production.
Innovations in Fintech
Technology, tools and techniques supporting modern financial services
Java - Propelling the Ecosystem Forward
Lessons from 8, prepping for 9, and peeking ahead at 10. Innovators in Java.
Machine Learning 2.0
Machine Learning 2.0, Deep Learning & Deep Learning Datasets
Microservices: Patterns & Practices
Practical experiences and lessons with Microservices
Modern Clientside Apps
Reactive, cross platform, progressive - webapp tech today
Modern CS in the Real World
Applied, practical, & real-world dive into industry adoption of modern CS
Next Gen APIs
Tooling, techniques, & practices building APIs today
Maximizing your impact as an engineer, as a leader, and as a person
Stream Processing at Large
Rapidly moving data at scale.