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...

Next Steps in Stateful Streaming with Apache Flink

Come learn how Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable. Over the last years, data stream processing has redefined how many of us build data pipelines. Apache Flink is...

Drivetribe: A Social Network on Streams

Drivetribe is the world's biggest motoring destination, as envisioned by Jeremy Clarkson, Richard Hammond, and James May. Built on top of the Event Sourcing/CQRS pattern, the Drivetribe platform uses Apache Kafka as its source of truth and Apache Flink as its processing backbone. This talk aims...

Streaming SQL Foundations: Why I ❤ Streams+Tables

What does it mean to execute robust streaming queries in SQL? What is the relationship of streaming queries to classic relational queries? Are streams and tables the same thing conceptually, or different? And how does all of this relate to the programmatic frameworks like we’re all familiar...

Streaming Reactive Systems & Data Pipes w. squbs

Reactive libraries are nothing new to the JVM. Reactive Streams as an SPI has even made its way into Java 9. However, their uses within microservice components are still for relatively narrow purposes like service orchestration. But we think differently. Our whole presence and universe can be...

Stream Processing Open Space