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Understanding Highly Available and Scalable Real-Time Data Processing

Recognizing important patterns in data, as they occur in real time, is critical to today’s enterprises. With complex event processing (CEP), organizations can recognize, understand, and react to business events faster by processing large volumes of inbound data.

Many critical CEP workloads require solutions that tolerate the failure of one or more processing nodes, while easily scaling to handle growing workloads. They must process a high volume, velocity, and variety of inbound data, infer the context, and take appropriate action in real time while adhering to stringent availability and scalability service level agreements.

In this on demand webcast, you'll learn:
• How an in-memory data grid can become the distributed working memory of a CEP engine, allowing it to scale dynamically from 2 to 100s of nodes and handle failure scenarios
• How sharding and data affinity can be used to obtain extreme performance
• How a message queuing platform can be used to optimize the distribution of data to multiple CEP engines
• Which use-cases are well suited to high-availability CEP
Recorded Jan 4 2017 59 mins
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Presented by
Fabio Marinelli Senior Middleware Architect, Red Hat
Presentation preview: Understanding Highly Available and Scalable Real-Time Data Processing

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Take your apps—and business—into the future with Red Hat
Join this channel to learn best practices and insights on how to: containerize existing apps for increased cost efficiency, deliver new cloud-native and process-driven apps using microservice architectures, take an agile approach to integrate APIs and data, and do it all in a culture of collaboration using DevOps best practices.

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