9 Windowing and timestamps
This chapter covers
- Understanding the role of windows and the different types
- Handling out-of-order data
- Suppressing intermediate results
- Grokking the importance of timestamps
In previous chapters, you learned how to perform aggregations with KStream and KTable. This chapter will build on that knowledge and allow you to apply it to get more precise answers to problems involving aggregations. The tool you’ll use for this is windows. Using windows or windowing is putting aggregated data into discrete time buckets. This chapter teaches you how to apply windowing to your specific use cases.
Windowing is critical to apply because, otherwise, aggregations will continue to grow over time, and retrieving helpful ...
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