We use a scenario to analyze the telemetry data of a taxi fleet in New York City in near-real time to optimize the fleet operation.

In this scenario, every taxi in the fleet is capturing information about completed trips. The tracked information includes the pickup and drop-off locations, number of passengers, and generated revenue. This information is produced into a Kinesis data stream as a simple JSON blob.

From there, the data is processed and analyzed to identify areas that are currently requesting a high number of taxi rides. The derived insights are finally visualized in a dashboard for operators to inspect.


Throughout the course of this workshop, you will build a fully managed infrastructure that can analyze the data in near-time, ie, within seconds, while being scalable and highly available. The architecture will leverage Amazon Kinesis Data Stream as a streaming store, Amazon Kinesis Data Analytics to run an Apache Flink application in a fully managed environment, and Amazon Elasticsearch Service and Kibana for visualization.

Along the way, we will learn about basic Flink concepts and common patterns for streaming analytics. We will also cover how KDA for Apache Flink is different from a self-managed environment and how to effectively operate and monitor streaming architectures.