The Nu Blog
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.
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.
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.
Real-time rating system. A one manday prototype
This blog post is a companion to the real-time rating scenario placed on the Nussknacker demo site.
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.
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 Yet, many businesses still rely on slow, outdated batch processing.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.
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.
Streaming SQL alternative
Many streaming applications require significant domain knowledge and continuous updates, however SQL is neither up to the task nor user-friendly for domain experts
Real-Time ML-Driven Recommendations Use Case
Nussknacker simplifies the integration of machine learning models into streaming data processes. Software teams can now build intelligent recommendation systems using Nussknacker, Snowplow, and MLflow.
Using Nussknacker with Apache Iceberg: Periodical report example
Nussknacker now supports Flink catalogs. This means you can use it with Apache Iceberg for tasks like data ingestion, transformation, aggregation, enrichment, and creating business logic. This blog post will show you how to use Nussknacker and Apache Iceberg together for a real-world example
ML models inference in fraud detection
How to simplify the integration of ML models into business applications, automate many of the technical complexities, and support advanced techniques like A/B testing and ensemble models. A fraud detection example
Next Best Action recommendation management simplified
Business scenarios can become increasingly complex, causing many problems for users and administrators. Read how we tackle the problem in the Request Response processing example
Exactly-once event processing with the new 1.16 release
With the new release Nussknacker allows End-to-End Exactly-Once event processing. Plus using Nussknacker Quickstart is now even clearer and easier.
Batch processing on Apache Flink
With the upcoming batch mode and other improvements, Nussknacker will be ready to fulfill the role of a central piece in your data processing architecture that unifies business logic in one place.
Data processing modes: Streaming, Batch, Request-Response
A comprehensive guide to the use of multiple processing modes in the modern organisation, how they complement each other and their use cases.
1.15 release highlights
Batch processing and Flink 1.18 support with many other improvements on the end-user and admin sides
Webinar video: Real-time data processing for the people
To create powerful real-time data applications, what features do these tools really need? Could these tools be as big of a change as the invention of spreadsheets? Watch the video replay, where we discuss this topic in detail.
How to use Nussknacker together with Kafka & Schema Registry
This article will explore how Nussknacker effortlessly integrates with Apache Kafka, including its seamless compatibility with Kafka's Schema Registry. We illustrate these concepts through an exciting Internet of Things example, showcasing the real-world application of Kafka integration with Nussknacker.
SMS Artificially Inflated Traffic
Using Nussknacker's robust real-time processing capabilities, we can build complex decision diagrams that analyse patterns, behaviours and anomalies that indicate SPAM SMS Fraud. This approach allows us to quickly identify potential threats and take proactive action to prevent fraud before it happens
A guide to OpenAPI components
At Nussknacker we decided to significantly ease enriching your data in scenarios with OpenAPI components. All you have to do is provide an OpenAPI based interface definition of your service and magic will happen — Nussknacker will automatically create components based on your interface definition and they will be ready to use
Telecommunications Fraud: an Introduction
This article is a starting point into the vast and shadowy realm of the Telecom Fraud World. It outlines various fraudster profiles without delving into the technical complexities. Going forward, detailed descriptions and examples of Nussknacker’s solutions for each type of fraud will be covered in separate articles.
Introduction to integrating Google Firebase with Kafka Connect and Nussknacker
Nussknacker can be used to inject additional business logic for the Firebase events and is easily integrated with Firebase subscriptions. This blog post describes how to integrate Google Firebase with Nussknacker with the help of Kafka Connect.
How to build a bridge between IT and the business teams?
With a bridge application, the business team won’t need to spend so much time thinking about optimal process design before they pass their requirements to the IT team. They can carry out experiments on live data, quickly verify assumptions and introduce adjustments that will improve prepared indicators.
MS Azure Active Directory Integration
In this blog post, I'll take you step-by-step through integrating with Microsoft Azure Active Directory (AAD), based on recent work for one of our valued customers, to show you the real-world impact and how easy it can be.
Nu chapter for TouK
We are delighted to unveil that most of the inimitable software engineers of TouK have joined Snowflake today, enhancing their capabilities. At the same time, a few of them will continue working with us on Nussknacker.
Combine streaming and historical data for real-time decisioning
The power of Snowflake is that you can build customer profiles with a 360° view. All the relevant information about your customers in one place. And Nussknacker not only makes it possible to react to current behaviour using all the prior knowledge, but it makes it possible for non-developers, shortening the path from an idea to actually taking actions.
What is wrong with low-code tools in streaming?
The success of spreadsheets is proof that non-professionals are not afraid of coding - just the constructs they use must be easier to learn than regular programming languages.
Translating business case into streaming concepts
Low-code solution for enrichments and time windows in marketing promotion. In this post we go through a specific example to highlight Nussknacker’s capabilities: data enrichments and aggregates in time window.
Take Nussknacker to the next level with configuration only
Nussknacker is a low-code tool that is also highly extensible - it allows the implementation of custom components, and custom runtime engine managers - reducing the deployment time
How to handle quickly evolving Elasticsearch documents in Nussknacker
Thanks to Nussknacker’s schema evolution support, it will evolve all incoming events to the latest schema version. If a new field will be unavailable, the default value will be used.
Speed up your OpenBanking decision scenarios with Nussknacker
Low-code and open data standards allow fast TTM and bring business experts closer to faster iterations and innovations. You can build complex decision scenarios using bricks-like components and low code
Stream Designer and Nussknacker Designer comparison
Both Stream Designer and Nussknacker Designer take similar, visual tool approaches to facilitate working with data streams. However, they diverge when it comes to target user groups. What’s most important is that both make data stream processing more ubiquitous and democratised.
Real-time sentiment analysis using Nussknacker and SpEL
Once data is on topics with defined schemas, they are ready to be used in Nussknacker. What’s more, Nussknacker has a flexible expressions language that allows for defining decision logic. The results of computations are visible in a nice form on metrics charts.
Low-code for business rules - are we there yet?
The general approach should be pragmatic: while we should aim at hiding all technical complexities of underlying systems and data, sometimes we have to accept that there will be cases where technical help for the experts (temporary or permanent) will be needed.
Building a credit rating verification service with Nussknacker
Algorithms for credit rating as well as in other domains tend to be complicated, therefore having them implemented in a visual way lets us understand them better than having them in the code.
Is SQL enough to democratise stream processing?
How can data streaming become part of the enterprise nervous system, if acting on the data in streams is the domain of savvy developers only?
Nussknacker 1.5
This version focused mainly on unifying our kafka source and sink components and some filter and choice (former switch) component improvements.
See the details
ML models inference. Non trivial processing algorithms built and deployed in an easy way
You can use Nussknacker to design and deploy ML models in use cases where complex decisioning, data transformation, and enrichment logic are needed. The decision logic you can build with Nusskkncker can be pretty sophisticated - we moved the bar of how much you can achieve with low code to pretty high.
Stretching old solutions to fit new problems doesn’t work. A genealogy of Nussknacker.
Extending SQL for the streaming world is very promising, but its main drawback is that in order to use it, you need to have all of your data in a homogenous environment. We took another approach that allows us to integrate any existing technology with stream processing.
NU has many flavours - which one is for you?
Chances are that you are already using a container’s scheduler - that is, Kubernetes. Wouldn’t it be great if we could just use it to deploy our business logic? You want to handle events from Kafka, enrich them with data from external systems, and possibly score some ML models? Let's glance at what is possible in Nu 1.3.
Running low-code scenarios with GraalVM native image
We’ve been working on making Nussknacker more cloud-ready, and it turned out we need to make the runtime container as small as possible.
It looks like GraalVM can be the answer: the resulting image and memory usage are really small, which makes running each Nussknacker scenario
in a separate container viable.
Nussknacker compared with similar open-source tools
People quite often ask us, what are the differences between Nussknacker and other similar (at first glance) tools. There are usually some overlapping areas in which many of them can be used interchangeably. But when your system grows, tools used for other purposes than they were designed for can cost you a lot of pain and can introduce a lot of technical debt.
Apache Ignite in Nussknacker environment. Lessons learned
A data cache allowing fast access by key is a must-have when dealing with a stream of thousands of records per second
Event streams always need Flink, or ...not really?
We believe there are many use cases where Nussknacker can bring significant value without operational overhead caused by Flink or other sophisticated stream processing engine
Nussknacker 1.1
We have released version 1.1 of Nussknacker 🍾
See how lowcode can help with business decisions on streaming data 💪
Feel free to ask any questions
Nussknacker can make your data processing use case more agile and easier to manage.