Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? Batch-mode processing isn’t going away, but exclusive use of these systems is now a competitive disadvantage. You’ll learn that, while fast data architectures are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention.
In this webinar, Lightbend’s Big Data Architect, Dr. Dean Wampler, examines the rise of streaming systems for handling time-sensitive problems. We’ll explore the characteristics of fast data architectures, and the open source tools for implementing them.
We’ll also take a brief look at Lightbend’s upcoming Fast Data Platform (FDP - http://lightbend.com/fast-data-platform ), a comprehensive solution of OSS and commercial technologies. FDP includes installation, integration, and monitoring tools tuned for various deployment scenarios, plus sample applications to help you sort out which tools to use for which purposes.
*Learn step-by-step how a basic fast data architecture works
*Understand why event logs are the core abstraction for streaming architectures, while message queues are the core integration tool
*Use methods for analyzing infinite data sets, where you don’t have all the data and never will
*Take a tour of open source streaming engines, and discover which ones work best for different use cases
*Get recommendations for making real-world streaming system responsive, resilient, elastic, and message driven
*Explore an example streaming application for the IoT: telemetry ingestion and anomaly detection for home automation systems
LEARN MORE: lightbend.com/fast-data-platform