The text discusses how to turn a stream of records into a graph and leverage graph processing to enrich and visualize data, with a focus on integrating this with event streaming applications like Kafka. The author uses Neo4j, a native graph database, to store and query the data as a graph, using Cypher, a declarative query language, to describe patterns in graphs. The text then covers how to use graph algorithms, such as the Common Neighbors Algorithm, to generate recommendations for potential friends in a social network example. Finally, it shows how to publish the results of these graph queries back to Kafka, either by publishing ad-hoc or by using Neo4j's streams source feature to automatically publish nodes and edges as they're created. The author concludes that this approach can be applied to any event streaming application, effectively adding "graph superpowers" to it.