Stream processing facilitates the collection, processing, and analysis of real-time data and enables the continuous generation of insights and quick reactions to emerging situations. This capability is useful when the value of derived insights diminishes over time. Hence, the faster you can react to a detected situation, the more valuable the reaction is going to be. Consider, for instance, a streaming application that analyzes and blocks fraudulent credit card transactions while they occur....

Sample Apache Flink application that can be deployed to Kinesis Analytics for Java. It reads taxi events from a Kinesis data stream, processes and aggregates them, and ingests the result to an Amazon Elasticsearch Service cluster for visualization with Kibana. https://github.com/aws-samples/amazon-kinesis-analytics-taxi-consumer

AWS makes it easy to build and operate a highly scalable and flexible data platforms to collect, process, and analyze data so you can get timely insights and react quickly to new information. In this session, we will demonstrate how you can quickly build a fully managed data platform that transforms, cleans, and analyses incoming data in real time and persist the cleaned data for subsequent visualizations and through exploration by means of SQL....

In today’s business environments, data is generated in a continuous fashion by a steadily increasing number of diverse data sources. Therefore, the ability to continuously capture, store, and process this data to quickly turn high-volume streams of raw data into actionable insights has become a substantial competitive advantage for organizations. Apache Flink is an open source project that is well-suited to form the basis of such a stream processing pipeline. It offers unique capabilities that are tailored to the continuous analysis of streaming data....