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Data is flowing everywhere around us, from phones, credit cards, sensor-equipped buildings, vending machines, thermostats, trains, buses,planes, posts to social media, digital pictures and video and so on.Simple data collection is not enough anymore. Most of the current systems do data processing via nightly extract, transform, and load (ETL)operations, which is common in enterprise environments, requires decision makers to wait an entire day (or night) for reports to become available.
But businesses don’t want «Big Data» anymore. They want «Fast Data».What distinguishes a «streaming systems» from the batch systems is that the event stream is unbounded or “infinite” from a system perspective.
Decision-makers need to analyze these streaming events as a whole to make business decisions as new information arrives.In this talk, after a short introduction to common approaches and architectures (lambda, kappa), Viktor will demonstrate how to use open-source steam processing tools (Flink, Kafka Streams, Hazelcast Jet) for stream processing.
Speaker: Viktor Gamov
Viktor Gamov is a Solution Architect at Confluent, a company behind Apache Kafka and Confluent Streaming Platform.
Previously, Viktor worked at Hazelcast, open-source In-Memory Data Grid (IMDG) company.
Viktor has comprehensive knowledge and expertise in enterprise application architecture leveraging open source technologies. He is helping different organizations build low latency, scalable and highly available distributed systems.
He is co-organizer of Princeton JUG.
Follow Viktor on Twitter @gamussa.
At Cerner, we’re continuously building on our foundation of intelligent solutions for the health care industry. Our technologies connect people and systems at more than 25,000 facilities worldwide, and our wide range of services support the clinical, financial and operational needs of organizations of every size.
Food Sponsor: Confluent
Every byte of data has a story to tell, something of significance that will inform the next thing to be done. In a data-driven enterprise, how we move our data becomes nearly as important as the data itself. With greater speed and agility, data’s value increases exponentially.
From its very early days, we have open-sourced <a>Apache Kafka</a>™ and have led it to impressive industry-wide adoption across several thousand companies. Now we are focusing on building a streaming platform to help other companies get easy access to enterprise data as real-time streams.