Task Madness - Modern on demand processing (batch processing, ETL, data science tasks)

Track: Data, Integration & IoT
Skill Level: Intermediate
Room: Room A312
Time Slot: Wed 2/17, 2:30 PM
Tags: machine learning , spring , cloud

It’s March Madness! Be the office pool champion by using machine learning to predict your bracket for the NCAA tournament. Using a new Spring project, Spring Cloud Tasks, we will demonstrate how on demand processing can be used to handle various tasks including batch processing, ETL, data science tasks, and other non-descript functions that need to be executed on demand. A general understanding of the Spring Framework is assumed.

Michael Minella

Michael Minella is a software engineer, teacher, speaker, and author with over 15 years of enterprise development experience. He currently works for Pivotal as the project lead for the Spring Batch and Spring Cloud Task projects as well as an instructor at DePaul University. Michael has spoken on a number of java, Spring, and big data topics and is a JavaOne Rockstar. He was a member of the expert group for JSR-352 (java batch processing). Michael is the author of Pro Spring Batch from Apress and the popular Refcard JUnit and EasyMock.

Outside of the daily grind, Michael enjoys spending time with his family and enjoys woodworking, photography and InfoSec hobbies.

Glenn Renfro

As a Pivotal engineer, Glenn Renfro is a core committer for Spring Cloud Task, Spring Batch and Spring Cloud Data Flow. He has 13 years experience in designing, building and delivering enterprise level applications in Java and 20 years total of software development experience.