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?
May 23, 2024
The Nu Blog
Webinar video: Real-time data processing for the people
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 Nu Blog
Marcin Stelmaszczyk
Apr 25, 2025
Integrate Azure Databricks MLflow for machine learning model management and inference
This text provides a comprehensive guide on integrating Nussknacker Cloud with Azure Databricks' managed MLflow service, enabling users to easily incorporate machine learning models into their data processing workflows.
Marcin Stelmaszczyk
Apr 25, 2025
How to train and register ML models in Azure Databricks
This article explains how to train and register a machine learning model in Azure Databricks that can later be used for credit card fraud detection in Nussknacker Cloud.
Zbigniew Małachowski
Apr 11, 2025
Next-Generation Real-Time Rating Systems
This blog post explains the rationale behind the shift from asynchronous architectures to stateful stream processing in real-time rating systems.
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