Fast Data: Big Data Analytics Using Streams
Fast Data is the next step in the evolution of Big Data technologies. Traditional Big Data techniques are dealing with large volumes of data, but Fast Data introduces a new dimension: Velocity, which demands near real-time response to Big Data.
We begin with characteristics of Stream Processing, and then discuss the dominant architectures for Streaming systems and continue into examples of technologies and application solutions for streaming applications. We compare the important technologies, such as Storm, Spark, Flink and Apache Beam. We then discuss the use new area of Fast Data in the AI space. We conclude with implementation guide and a summary of best practices for Fast Data.
Dr. Vladimir Bacvanski is a software architect, consultant and mentor whose focus is on making companies successful in adopting modern software and data technologies. His clients include Fortune 500 companies, government organizations and startups.
Vladimir is published worldwide and is a frequent speaker, session chair, and workshop organizer at leading industry events. He was awarded the title of the IBM Champion for 5 years in a row 2009-2013. Vladimir is founder of SciSpike, a development and consulting firm specializing in advanced software technologies. He is currently the interim Director of Software Architecture at VHA, Inc.
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