Unifying Big Data Batch and Real-Time Streaming with Apache Flink
Apache Flink continues on the ideas from Hadoop but adds optimization and transformation mechanisms from distributed databases and parallel collections. Flink runs on top of HDFS and YARN, but the execution is optimized, in a similar way as the relational databases optimize SQL. The execution model is based on a memory management scheme that favors in-memory processing, but then gracefully degrades to disk when necessary. The same engine supports both batch and true streaming. Flink has a very elegant Scala based API, which highly resembles using Scala collection libraries.
Join us in exploring the Apache Flink’s state of the art data processing capabilities, end user benefits and new Big Data algorithms.
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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