Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Dean Wampler

Dean Wampler
Vice President, Fast Data Engineering, Lightbend

Website | @deanwampler

Dean Wampler is the vice president of fast data engineering at Lightbend, where he leads the Lightbend Fast Data Platform project, a distribution of scalable, distributed stream processing tools including Spark, Flink, Kafka, and Akka, with machine learning and management tools. Dean’s the author of Programming Scala and Functional Programming for Java Developers and the coauthor of Programming Hive, all from O’Reilly. He’s a contributor to several open source projects. A frequent Strata speaker, he’s also the co-organizer of several conferences around the world and several user groups in Chicago.


11:20am12:00pm Wednesday, September 27, 2017
Data Engineering & Architecture, Stream processing and analytics
Location: 1E 07/08 Level: Intermediate
Secondary topics:  Streaming
Dean Wampler (Lightbend)
Average rating: ***..
(3.00, 3 ratings)
While stream processing is now popular, streaming architectures must be more reliable and scalable than ever before—more like microservice architectures in fact. Dean Wampler defines "stream" based on characteristics for such systems, using specific tools like Kafka, Spark, Flink, and Akka as examples, and argues that big data and microservices architectures are converging. Read more.
11:20am12:00pm Thursday, September 28, 2017
Secondary topics:  Streaming
Dean Wampler (Lightbend), Jun Rao (Confluent), Karthik Ramasamy (Streamlio), Pramod Immaneni (DataTorrent)
Average rating: ***..
(3.00, 1 rating)
In a series of three 11-minute presentations, key members of Apache Kafka, Heron, and Apache Apex discuss their respective implementations of exactly once delivery and semantics. Read more.
1:15pm1:55pm Thursday, September 28, 2017
Location: O'Reilly booth (Table A)
Dean Wampler (Lightbend)
Join Dean to discuss all things streaming, especially with Kafka, Spark, Flink, Akka Streams, and Kafka Streams—from the future of machine learning in a streaming context to integrating stream processing with microservices. Read more.