read the blog post here:

"In a matter of a couple of hours I created a real-time rating prototype." Read how Nussknacker enabled creating a functioning real-time rating prototype (stateful stream processing) in a fraction of the time traditional development would require

https://t.co/TehAeJB3HE

Using Shopify as an example, we showcase how to processes clickstream and app events in REAL TIME With our MLflow component pre-trained machine learning models can be effortlessly integrated into the streaming process, enabling businesses to deploy custom models instantly

We demonstrate how Nussknacker, paired with Snowplow, and @MLflow can transform raw event data into actionable insights and deliver real-time personalized product recommendations

We explore the comparison between SQL-based #datastreaming applications and our approach. Read the whole story here:

When an event arrives, the immediate question is, “What do I do with this event now?” In such scenarios, there’s no set—just a single event. Why force everyone to think of a stream as a table?

We @Nussknacker_io believe that there is no inherent reason why acting on stream data, including streaming ML, should remain the realm of professional Kafka, Flink, Spark developers. For this to succeed a very different approach to building streaming apps is needed ...

A decision making scenario must be easy to change, even with real-time data - just ask your business teams It takes about 1 minute to add a new real-time processing branch that uses time windows and sends processed data back to #Kafka

With the latest 1.18 release we have added new Activity Panels to replace Versions, Comments and Attachments panels. ☑️Now you can browse all scenario activities on one chronological list. Check out what's more in the latest release: https://t.co/7KK8Y4VAeQ

Thanks for coming to our presentation at the @DSS_conference in Warsaw. If you are interested in finding out more about this topic, please get in touch with us or contact Zbigniew Małachowski directly

In this tutorial we demonstrate how to develop an inventory monitoring system using Nussknacker, going from a basic stock-level tracking solution into a dynamic, demand-aware system https://t.co/S7VkKszNfG

Release 1.17 introduces #Flink Catalogs integration. Thanks to Catalogs, Nussknacker can be used to act on data stored in 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲𝘀. 👉 Check our tutorial on Apache #Iceberg integration https://t.co/cTHyFmoDan

In this blog post, @ark_adius will show you how to use Nussknacker to build a data pipeline in this setup 👉 https://t.co/cTHyFmoDan

🟢 Ingest data: Load data into your Data Lakehouse. 🟠 Transform data: Clean, filter, and restructure your data. 🔴 Aggregate data: Summarize and group data. 🔵 Enrich data: Use ML inference, joins etc to add context to your data. 🟣 Apply 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗹𝗼𝗴𝗶𝗰 to data

With the latest version, Nussknacker has become a powerful tool for those working with Apache #Iceberg based Data Lakehouses. It can handle both 𝗯𝗮𝘁𝗰𝗵 𝗮𝗻𝗱 𝗻𝗲𝗮𝗿 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 workloads. By integrating Nussknacker with Apache Iceberg, you can do the following:

3️⃣ The Nussknacker ML runtime handles the underlying model execution, making it easy for domain experts to integrate ML models or switch between different versions of an ML model https://t.co/pS5T5a6SXs

1️⃣ Nussknacker’s MLflow enricher discovers available models, their versions and input and output parameters 2️⃣ To invoke an ML model in Nussknacker, you simply select the desired model and its version, populate the input parameters, and the scenario is ready to be deployed

https://t.co/jRM3SVQBm2

See how Nussknacker captures, transforms and acts on disparate real time data - simplifying real-time decision-making in various use cases