InfoQ Homepage Streaming Content on InfoQ
-
Kafka Streams - from the Ground Up to the Cloud
Marius Bogoevici introduces the Kafka Streams API and the Kafka Streams processing engine, showing how to write and deploy Kafka Streams applications using Spring Cloud Stream.
/presentations/kafka-streams-spring-cloud/en/mediumimage/Marbig-1520042921336.JPG)
-
Data Decisions with Real-Time Stream Processing
Serhat Yilmaz talks about how Facebook is using stream processing at scale, the difficulties they have encountered and the solutions they have created to date.
/presentations/facebook-stream-processing/en/mediumimage/Serbig-1519961761464.JPG)
-
Streaming SQL Foundations: Why I ❤Streams+Tables
Tyler Akidau explores the relationship between the Beam Model and stream & table theory, stream processing in SQL with Apache Beam, Calcite, Flink, Kafka KSQL and Apache Spark’s Structured streaming.
/presentations/beam-model-stream-table=theory/en/mediumimage/Tybig-1518993323788.JPG)
-
The Power of Distributed Snapshots in Apache Flink
Stephan Ewen talks about how Apache Flink handles stateful stream processing and how to manage distributed stream processing & data driven applications efficiently with Flink's checkpoints&savepoints.
/presentations/distributed-stream-processing-flink/en/mediumimage/Stebig-1518991394004.JPG)
-
Panel: SQL over Streams, Ask the Experts
The panelists discuss the new generation of Stream Processing engines.
/presentations/sql-streams-panel/en/mediumimage/Pan1big-1512562189830.jpg)
-
Survival of the Fittest - Streaming Architectures
Michael Hansen talks about the core principles that will stand the tests of streaming evolution, the potential pitfalls that we may stumble over on our path to streaming and how to avoid these.
/presentations/hbc-digital-streaming/en/mediumimage/Michaelbig.JPG)
-
Streaming for Personalization Datasets at Netflix
Shriya Arora discusses challenges faced with stream processing unbounded datasets, comparing microbatch with event-based approaches using Spark and Flink.
/presentations/netflix-personalization-datasets-streaming/en/mediumimage/Shrbig.JPG)
-
Streaming Microservices: Contracts & Compatibility
Gwen Shapira discusses patterns of schema design, schema storage and schema evolution that help development teams build contracts through better collaboration and deliver resilient applications faster
/presentations/contracts-streaming-microservices/en/mediumimage/Gwebig.JPG)
-
Adopting Stream Processing for Instrumentation
Sean Cribbs talks about the interface Comcast has designed for their instrumentation system, how it works, how the stream processor manages flows on behalf of the user, and some trade-offs applied.
/presentations/stream-processing-instrumentation/en/mediumimage/Seabig.JPG)
-
When Streams Fail: Kafka Off the Shore
Anton Gorshkov discusses how to evaluate and architect a resilient streaming platform, focusing on Kafka and Spark streaming and sharing his experience of using them to process financial transactions.
/presentations/streaming-kafka-spark/en/mediumimage/Antbig.JPG)
-
Real-World Java 9
Trisha Gee shows via live coding how we can use the new Flow API to utilize Reactive Programming, how the improvements to the Streams API make it easier to control real-time streaming data, etc.
/presentations/java9-flow-stream-api/en/mediumimage/Tribig.JPG)
-
From Concurrent to Parallel
Brian Goetz explores the different goals, tools, and techniques involved between concurrency and parallelism approaches, and how to analyze a computation for potential parallelism.
/presentations/tecniques-parallelism-java/en/mediumimage/Bribig.JPG)