As big data is shifting from a world at rest to a world in motion, we need visualization applications that have been architected to handle streaming systems for use cases from cybersecurity to the IoT to financial services. After all, time is money, and the sooner you deliver insights the greater the impact.
However, all of our existing business intelligence and visualization tools have been designed to work with legacy batch-oriented systems. Streaming visual analytics is a new technique for visualizing and interacting with streaming data in near real time. Shant Hovsepian explains how lambda- and polling-based architectures are being disrupted by reactive visualization systems, as streaming engines embrace the CQRS pattern, and offers analysis of visualizing streams from Apache Kafka, Apache Flink, and Apache Spark. Shant explores current approaches to doing visual analysis on streaming data, along with some of their shortcomings, and details what’s possible with the next generation for streaming analysis systems. You’ll learn how streaming visual analytics can be implemented atop Spark Structured Streaming, Kafka Streams, and Flink and Queryable State.
Shant Hovsepian is a cofounder and CTO of Arcadia Data, where he is responsible for the company’s long-term innovation and technical direction. Previously, Shant was an early member of the engineering team at Teradata, which he joined through the acquisition of Aster Data. Shant interned at Google, where he worked on optimizing the AdWords database, and was a graduate student in computer science at UCLA. He is the coauthor of publications in the areas of modular database design and high-performance storage systems.
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