Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK
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 is 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.


9:0012:30 Tuesday, 23 May 2017
Spark & beyond
Location: Capital Suite 4
Level: Intermediate
Dean Wampler (Lightbend)
Average rating: ****.
(4.50, 2 ratings)
Apache Spark is written in Scala. Hence, many if not most data engineers adopting Spark are also adopting Scala, while most data scientists continue to use Python and R. Dean Wampler offers an overview of the core features of Scala you need to use Spark effectively, using hands-on exercises with the Spark APIs. Read more.
16:3517:15 Wednesday, 24 May 2017
Stream processing and analytics
Location: Hall S21/23 (A)
Level: Intermediate
Dean Wampler (Lightbend)
Average rating: ****.
(4.57, 7 ratings)
"Stream" is a buzzword for several things that share the idea of timely handling of never-ending data. Big data architectures are evolving to be stream oriented. Microservice architectures are inherently message driven. Dean Wampler defines "stream" based on characteristics for such systems, using specific tools as examples, and argues that big data and microservices architectures are converging. Read more.
12:0512:45 Thursday, 25 May 2017
Location: Capital Hall (N24) - O'Reilly booth - Table B
Dean Wampler (Lightbend)
Dean will discuss trends in streaming data (so-called "fast data"), including Spark, Flink, Kafka, and even Scala, and explain what this means for Hadoop and emerging alternatives. Read more.