Webinar video: Real-time data processing for the people
In this webinar recording, Zbigniew Małachowski lists the features real-time data tools need beyond drag and drop, for fraud detection, streaming ML or IoT.
Can domain experts be so happy with a tool that the experience can be likened to nirvana? This provocative question is intended to start a discussion about the features needed to make real-time data products accessible to ordinary (non-technical) people. Zbigniew Małachowski presents his own list of required features. But are they enough to trigger nirvana-like relief?
Abstract:
Acting on data in real time, such as fraud detection, next best action, streaming ML, clickstream analysis, IoT sensor analysis, etc., requires algorithms that are rarely trivial enough to be created with no code: just drag and drop.
In the streaming world, the reason why domain experts find themselves forced to go into deep technical details of Kafka, Flink, Spark, REST, etc is that while many platforms are quite successful in abstracting Kafka, Flink, Spark, etc, they are just simple visual overlays on the (streaming) SQL and do not allow for much more.
There needs to be more to author a serious actions-on-data algorithm. What features are needed for these tools to truly succeed?
The visual Flink interface for real-time decision logic.
Enrich, decide, and act on every event.
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