Architecture
Building an Architecture to Predict Customer Behavior in a Revenue-Critical System
Wednesday Jun 14 / 01:40PM EDT
At Neon digital bank in Brazil, we strive to make revenue-impacting predictions based on customer behavior. Building a low latency and high availability distributed system that meets this requirement becomes especially challenging.
Yves Junqueira
Distinguished Software Engineer @Neon
Streaming from Apache Iceberg - Building Low-Latency and Cost-Effective Data Pipelines
Tuesday Jun 13 / 11:50AM EDT
Apache Flink is a very popular stream processing engine featuring sophisticated state management, even-time semantics, exactly-once state consistency. For low latency processing, Flink jobs typically consume data from streaming sources like Apache Kafka.
Steven Wu
Software Engineer @Apple and Apache Iceberg PMC
How to Build a Reliable Kafka Data Processing Pipeline, Focusing on Contention, Uptime and Latency
Wednesday Jun 14 / 10:35AM EDT
Shifting workloads from synchronous to asynchronous can simplify the operational cost of high-throughput HTTP services. But understanding the evolution of performance metrics in the world of complex, high-concurrency, asynchronous distributed systems can be quite challenging.
Lily Mara
Engineering Manager @OneSignal
Reliable Architectures Through Observability
Wednesday Jun 14 / 02:55PM EDT
We want our systems to be reliable, but testing alone isn't enough. In a complex, multi-service system, it's impossible to test your way to correctness. That's why we need observability. Observability is the ability to see what our code is doing, in production and in development.
Kent Quirk
Staff Engineer @Honeycomb.io
The Rise of the Serverless Data Architectures
Tuesday Jun 13 / 01:40PM EDT
For a while, it looked like Serverless was just a convenient way to run stateless functions in the cloud. But in the last year we’ve seen the rapid rise in serverless data stores.
Gwen Shapira
Founder @Nile, PMC Member @Kafka
Laying the Foundations for a Kappa Architecture - The Yellow Brick Road
Tuesday Jun 13 / 10:35AM EDT
In the ever changing landscape of big data, focus is slowly moving away from batch and towards realtime analytics. Data Science workflows are evolving to adapt to this changing landscape.
Sherin Thomas
Staff Software Engineer @Chime