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
Pawel Czajka
Apr 4, 2025
Real-time IoT data processing with Low-Code
By supporting the widely-used MQTT protocol, Nussknacker makes it easier to connect to and process data from various IoT sensors and devices without needing custom connectors.
Łukasz Ciołecki
Mar 20, 2025
Real-time transaction fraud detection
Fraudsters act instantly, and fraud detection systems must keep up. Yet, many businesses still rely on slow, outdated batch processing. This post explores the challenges of real-time fraud detection and how Nussknacker’s low-code platform enables fast, efficient transaction monitoring.
Łukasz Jędrzejewski
Jan 21, 2025
Telecom's credit scoring system with ML inference
Using Nussknacker with MLflow, enabled data scientists to deploy ML models directly while letting business analysts manage credit rules. This resulted in faster updates, reduced developer dependency, and more efficient credit risk assessment for their 13 million customers.
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