From the course: Apache Flink: Real-Time Data Engineering
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Streaming with Apache Flink - Flink Tutorial
From the course: Apache Flink: Real-Time Data Engineering
Streaming with Apache Flink
- [Instructor] Stream processing is growing in importance and adaption in data processing pipelines today. There are some unique characteristics and limitations while processing streaming data. Let's try to understand what they are and how they impact data processing. Streaming data is unbounded. At any point during the processing, it is not possible to know the total number of records we are dealing with. New data keeps coming in at mostly unpredictable intervals. Constant new data also means that the results are not repeatable. The same query executed five minutes back will generate different results if executed again. This also means that the results derived from stream queries expire quickly, and they need to be executed again to get the latest status. Most stream processing applications deal with state and they become a key part in processing also. For example, we may want to analyze current active users on a website, to…
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