The most effective way to collaborate on real-time data processing
Nussknacker
Software, data and business teams build solutions together, seamlessly combining their technical and domain expertise. Nussknacker provides rapid time-to-value and frictionless long-term cooperation
trusted by financial and telecom companies to handle heavy data processing and decision making
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 |
software team
Engage stakeholders in data processing without compromising technological advancements
| 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 |
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 |
product
ease of use
flow diagrams for decision algorithms
powerful expression language
autocompletion and validation
testing and monitoring tools
REST (OpenAPI) and data base (JDBC) enrichments
ML models inferring enrichments → more
one-click deployment
version history
deployment flexibility
running on Flink or K8s-based lightweight engine
Kafka® and HTTP interfaces
integrates with Kafka-compatible platforms like Confluent® Cloud, Azure Event Hubs® and Aiven® for Apache Kafka®
streaming and request-response modes
customisable and extensible
open source with enterprise extensions
on premises and cloud
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.
use cases
Real-time marketing
Communications with customers in real-time, providing event-driven offers and actions
Read a customer story
Fraud management
Mitigating fraud by running detection algorithms on network or device signals
Read a customer story
Next Best Action
Assisting the Point Of Sale, displaying suggestions about what to offer and how to proceed with a customer
Read a blog post
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
See demo
ML models deployment
Infer Machine Learning models from within complex decision algorithms
Read a blog post
offer
Free
Cloud
Quick solution for straightforward yet demanding data streaming tasks without exhausting investment decisions
Premium
Cloud
Ready-to-use collection of features and integrations for advanced data environments with affordable infrastructure maintenance expenses
Enterprise
On premise
Extensible tool fitted for superior technology stacks where unique data integrity is required
blog
Zbigniew Małachowski
Jan 14, 2025
Streaming SQL alternative
Many streaming applications require significant domain knowledge and continuous updates, but SQL is not user-friendly for domain experts
Nussknacker Team
Dec 19, 2024
Real-Time Recommendations: Using Machine Learning in Clickstream Processing Pipeline
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.
Arek Burdach
Sep 27, 2024
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
next steps
see the demo in action
try it yourself
feel free to contact us if you have any questions