Distributed stream processing frameworks are used to rapidly ingest and aggregate large volumes of incoming data. These frameworks often require the application developer to write imperative logic describing how that data should be processed.  For example, a high volume of clickstream data that is getting buffered to Kafka needs to have a stream processing system

Distributed stream processing frameworks are used to rapidly ingest and aggregate large volumes of incoming data. These frameworks often require the application developer to write imperative logic describing how that data should be processed. For example, a high volume of clickstream data that is getting buffered to Kafka needs to have a stream processing system

Distributed stream processing frameworks are used to rapidly ingest and aggregate large volumes of incoming data. These frameworks often require the application developer to write imperative logic describing how that data should be processed.&nbsp; For example, a high volume of clickstream data that is getting buffered to Kafka needs to have a stream processing system

Materialize: Streaming SQL on Timely Data with Arjun Narayan and Frank McSherry

Databases and data engineering episodes of Software Engineering Daily

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Materialize: Streaming SQL on Timely Data with Arjun Narayan and Frank McSherry

Materialize: Streaming SQL on Timely Data with Arjun Narayan and Frank McSherry