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
easy real-time data processing
with a simple drag-and-drop interface:
detect fraud, device malfunction, respond with marketing messages, analyze clickstream, compute ML features or run ML models on real-time data
perform any data transformation and enrichment - analyze data in time-windows - experience rapid iterations with a one-minute experimentation cycle
integrates with Kafka-compatible platforms
existing Kafka deployments gain the ability to execute business logic in real time
user-friendly Flink processing
a deployed decision scenario becomes a Flink job in data streaming use cases
multipurpose
Nussknacker adapts to a wide range of applications, from common to cutting-edge
real-time data
event streams can be enriched with information from multiple sources: private and public APIs, databases, and Machine Learning models
low-code interface
the data is made readily available to the user via the drag-and-drop, one-click deployment interface that allows them to iterate and experiment
real-time apps
raw events become real-time business applications - without spending developer time
users innovate in response to changing business conditions creating event-driven solutions in faster cycles
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
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
performance
as a showcase, we present here two deployments that demonstrate the power of the solution in the case of the telecom company PLAY (part of the Iliad Group)
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
Nussknacker Team
Nov 26, 2024
1.18 release
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.
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
Łukasz Jędrzejewski
Sep 6, 2024
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 steps
follow the project on GitHub
try it yourself
feel free to contact us if you have any questions