Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
Boris Lublinsky

Boris Lublinsky
Principal Architect, Lightbend

Website

Boris Lublinsky is a principal architect at Lightbend, where he specializes in big data, stream processing, and services. Boris has over 30 years’ experience in enterprise architecture. Previously, he was responsible for setting architectural direction, conducting architecture assessments, and creating and executing architectural road maps in fields such as big data (Hadoop-based) solutions, service-oriented architecture (SOA), business process management (BPM), and enterprise application integration (EAI). Boris is the coauthor of Applied SOA: Service-Oriented Architecture and Design Strategies, Professional Hadoop Solutions, and Serving Machine Learning Models. He’s also cofounder of and frequent speaker at several Chicago user groups.

Sessions

1:30pm–5:00pm Tuesday, March 6, 2018
Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams
Data engineering and architecture, Streaming systems and real-time applications
 Location: 210 C/G
Dean Wampler (Anyscale), Boris Lublinsky (Lightbend)
Average rating: ***..
(3.50, 2 ratings)
Join Dean Wampler and Boris Lublinsky to learn how to build two microservice streaming applications based on Kafka using Akka Streams and Kafka Streams for data processing. You'll explore the strengths and weaknesses of each tool for particular design needs and contrast them with Spark Streaming and Flink, so you'll know when to choose them instead. Read more.
2:40pm–3:20pm Thursday, March 8, 2018
Ask Me Anything: Streaming architectures and applications (Kafka, Spark, Akka, and microservices)
Ask Me Anything
 Location: 212 A-B
Dean Wampler (Anyscale), Boris Lublinsky (Lightbend)
Average rating: *****
(5.00, 1 rating)
Join Dean Wampler and Boris Lublinsky to discuss all things streaming, from architecture and implementation to streaming engines and frameworks. Be sure to bring your questions about techniques for serving machine learning models in production, traditional big data systems, or software architecture in general. Read more.