Apache Kafka - Real-time Stream Processing (Master Class) - Creating Tumbling Windows

The video tutorial introduces the concept of windowing in Kafka Streams API, focusing on time-based windowing. It presents a problem of counting invoices in a 5-minute window for each store and provides a solution using windowed aggregation. The tutorial includes a detailed implementation guide with code, explaining the use of a timestamp extractor and event time semantics. It also covers running the application using Docker and explains the concept of tumbling windows, highlighting their fixed size and non-overlapping nature.

What are the key components of the invoice schema as mentioned in the lecture?

What is the expected outcome when processing invoices in a 5-minute window?

Summarize the process of extracting the timestamp from the consumer record.

What is a tumbling time window and how is it defined?