The Most Collaborative Tool to Swiftly Develop Your Real-Time Data Processing

Picture shows Nussknacker diagram with ML Flow enricher

Software, data and business teams build solutions together, seamlessly combining their technical and domain expertise

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Trusted by financial and telecom companies to handle heavy data processing and decision making

Case Studies

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Business Team

Apply Your Domain Knowledge with Zero-Delay

Express decision algorithms with self-explanatory flow diagrams
Use spreadsheet-like formulas for even the most complicated data transformations and boolean conditions
Verify your ideas instantly using one-click deployment and testing functionalities

Data Team

Productionize ML Inference for Demanding Workloads

Focus on ML insights, not ML model deployment
Use RAG, add model pre and postprocessing and ensemble multiple models
Infer models in your favourite format: Python, ONNX, or PMML

ML model inference real time

Software Team

Engage Stakeholders in Data Processing without Compromising Technological Advancements

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Set up integrations and let domain experts make use of the data
Use the power of Flink while keeping it under the hood
Adjust to your specific needs - add UDFs and specialized components

Product

Ease of Use

  • Visual flow diagrams for building decision algorithms in Nussknacker’s real-time automation platform.flow diagrams for decision algorithms
  • Simplify development with Nussknacker’s powerful expression language for low-code stream processing.less code with powerful expression language
  • Enhance accuracy with Nussknacker’s intelligent autocompletion and real-time validation for stream processing.autocompletion and validation
  • Track performance with Nussknacker’s real-time monitoring and metrics for stream processing.real-time monitoring and metrics
  • Quickly validate and debug workflows with Nussknacker’s rapid testing tools for stream processing.rapid testing tools
  • Seamlessly transfer workflows between environments with Nussknacker’s easy migration feature.easy migration across environments
  • Deploy workflows instantly with Nussknacker’s one-click deployment feature.one-click process deployment
  • Track, review, and restore workflow versions with Nussknacker’s version history management.version history management
  • Extend and tailor Nussknacker to your needs with its customizable and extensible architecture.customisable and extensible
  • Seamless REST API integration for real-time data processing and automation.exposed REST API for automation and integration

Deployment Flexibility

  • Execute workflows efficiently with Nussknacker’s lightweight engine powered by Flink or Kubernetes.Run on-premises or on K8s, with support for both deployment modes
  • Real-time streaming data processing with Nussknacker for scalable event-driven workflows.unifies both streaming & batch processing
  • Seamlessly integrate Nussknacker with Ververica for enterprise-grade Apache Flink stream processing.integration with Ververica Platform
  • Seamless data streaming with Nussknacker’s Kafka source and sink interfaces.Kafka® source and sink interfaces, integration with platforms like Confluent® Cloud, Azure Event Hubs® and Aiven®
  • Seamlessly connect Nussknacker to data lakes for efficient data processing and storage.data lakes & warehouses support by Flink's connectors
  • Connect Nussknacker to databases for efficient real-time data processing and automation.databases source & sink interfaces
  • Enhance workflows with REST (OpenAPI) and database (JDBC) enrichments in Nussknacker for seamless data integration.REST (OpenAPI) and database (JDBC) enrichments
  • Integrate and enrich workflows with real-time ML model inference in Nussknacker.ML models inferring enrichments → how to?
  • Nussknacker combines open-source flexibility with powerful enterprise extensions for scalability and advanced features.open source with enterprise extensions
  • Experience Nussknacker on-premises or in the cloud with a free plan.on premises and cloud → play with it

Nussknacker is a graphical tool to define, deploy and monitor Apache Flink jobs. Job logic is expressed by a graph, with SpEL used for data transformations and boolean conditions.

Nussknacker supports various data sources - Kafka streams, files, databases, HTTP APIs, and many others, either natively or via Flink connectors.

product

Use Cases

Real-time Marketing

Communications with customers in real-time, providing event-driven offers and actions
RTM Automation

Fraud Management

Mitigating fraud by running detection algorithms on network or device signals
Fraud Monitoring

 

Next Best Action

Assisting the Point Of Sale, displaying suggestions about what to offer and how to proceed with a customer
Recommendation System

Customer Data Processing

Decisioning on dynamic customer data in
- dynamic pricing
- order status management
- instant credit scoring

Internet of Things

Automating actionable data in
- predictive maintenance
- inventory management
- smart devices
Gaming Engagement

ML Models Deployment

Infer Machine Learning models from within complex decision algorithms
ML Inference

Offer

Freemium

Hosted by Nussknacker

Quick solution for straightforward yet demanding data streaming tasks without exhausting investment decisions

 

Pro

Hosted by Nussknacker

Ready-to-use collection of features and integrations for advanced data environments with affordable infrastructure maintenance expenses

 

Enterprise

Self-hosted / On Premise / BYOC / by Nussknacker

Extensible tool fitted for superior technology stacks where unique data integrity is required

 

Our Offer

Blog

Zbigniew Małachowski
Jul 17, 2025

Why Streaming SQL Is Not the Right Tool for Authoring Event-Driven Stream Based Algorithms

Should SQL be the go-to language for building streaming algorithms — or are we forcing it into a role it was never meant to play?

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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.

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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.

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Next Steps

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