The third installment in a series on Apache Kafka explores its processing fundamentals, focusing on streams and tables as tools for implementing distributed applications that enable parallel data processing. Building on the storage concepts discussed earlier, the article explains how events stored in Kafka topics are transformed into streams and tables for further processing using tools like ksqlDB and Kafka Streams. It delves into the concept of consumer groups, which facilitate scalable parallel processing by enabling distributed applications to form groups that read from the same input topics, thereby allowing dynamic workload distribution as application instances join or leave. The article also covers the state management necessary for tables, emphasizing the use of state stores for maintaining application state and ensuring efficient and fault-tolerant data processing. Additionally, it highlights the significance of data contracts and schema management in ensuring proper data serialization and deserialization across different clients, underscoring the advantages of using a schema registry for data governance. The discussion extends to more advanced topics like global tables and the partitioned design of Kafka's processing layer, which enhance scalability and performance. The article concludes by hinting at further discussions on elastic scaling and fault tolerance in the subsequent part of the series.