Divide, Distribute and Conquer: Stream v. Batch
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 Hazelcast Jet can be used for in-memory stream processing.
Viktor Gamov is a Senior Solution Architect at Hazelcast, the leading open-source in-memory technologies company. Viktor has comprehensive knowledge and expertise in enterprise application architecture leveraging open source technologies. He's helping companies build low latency, scalable and highly available distributed systems. He is co-organizer of Princeton JUG and New York Hazelcast User Group. He is a co-author of O'Reilly's «Enterprise Web Development». Viktor’s presenting at the conferences (http://lanyrd.com/gamussa/), blogging and producing a podcast. Follow Viktor on Twitter @gamussa.
