Event-Driven Big Data - Leveraging Big Data In-Motion

Events define the world and using them to understand it offers an up-to-date, rich interpretation. Rapidly integrating and interpreting the wide range of events is a true big-data challenge. Event-driven big data integrates advanced messaging technologies and architectures with data streaming services,time-based big data constructs using NoSQL stores to capture, alert on, and analyze real-time events to form a relevant decision-directed perspective.

Learn:

Benefits of event-based processing over traditional big data batch processing.
An event-driven architecture that provides:

Efficient surge processing and alerting on billions of diverse events.
Dynamic event routing based upon event characteristics such as topics, client interest, and priorities.
Guaranteed message processing and delivery
Integration of messaging with big data processing and NoSQL persistence.
Integration and processing of rapid-fire web services and streaming services.
NoSQL Data modeling techniques that enable efficient event processing

Access configured AMAZON EC2 Event-Driven instances.
Roadmap for moving from batch to event driven solutions.

The instances include configured Hadoop, RabbitMQ, Accumulo, and various web streaming services along with the demonstration code that incorporates Spring Integration Framework.

John Hebeler: professional developer, amateur beer brewer, and audiophile want-to-be. He is co-author of Semantic Web Programming and Peer-to-Peer Networking and holds two patents on distributed media measurement. He is currently focusing on real-time, large-scale event management, policy analytics, and convergent data integration through semantics and cloud computing. He has several published papers, is an adjunct professor, and presents at major technical conferences. He is currently pursuing his Ph.D. in Information Systems at UMBC. He holds a BSEE from RPI, MBA from Loyola University, and IS MS from UMBC. He is a Principal Engineer for Lockheed Martin.