In a blog post by Fabian Hueske, the process of building real-time dashboard applications using Apache Flink, Elasticsearch, and Kibana is detailed, highlighting the architecture and implementation of a stream data analytics solution. The architecture leverages Apache Flink for stream processing, Elasticsearch for data storage with low latency, and Kibana for data visualization. The post explains the features of Apache Flink, such as its support for event time and out-of-order streams, expressive APIs, and fault tolerance, which make it suitable for streaming applications. A demo application is described, which analyzes taxi ride events in New York City, showcasing how Flink's DataStream API can be used to compute passenger counts at various locations using sliding window operations. The integration with Elasticsearch and Kibana allows for real-time data visualization, and the post provides steps for setting up and configuring these components. Hueske concludes by encouraging readers to experiment with the demo and explore the capabilities of the tools used.