Airports are complex networks consisting of an immense number of systems that are necessary to keep the daily stream of passengers in constant motion. Connecting these systems in order to make the big picture transparent to the people running the show, authorities and last but not least the passengers is no simple endeavor.
In this talk I will describe a fictional airport and its effort to restructure the IT infrastructure around Kafka Streams to serve the real-time data needs of a busy airport. I will start by giving a brief overview of Kafka Streams, KSQL and the opportunities they offer for real-time stream processing. Following that we will explore the the target architecture, which relies heavily on manifested views to serve up-to-date data, while also persisting to a traditional data lake for larger analytics workflows. Additionally we will take a look at the generic data transformation framework that was created to minimize integration effort of the data receiving systems. To illustrate these ideas I will describe some examples of possible integrations: joining flight data with radar and weather data to predict arrival time at the gate down to the second, constantly updated processing data from the luggage conveyor belts as well as results from prediction models for passenger flow, and many more.