The Visual Flink Interface

Low-Code Apache Flink

Build on Flink without the complexity. Design real-time streaming scenarios in a visual editor, deploy with one click, and let your team iterate without touching Flink code.

Engineers set up integrations. Business teams own the logic.

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Apache Flink Apache Kafka AI Agentic AI
Trusted by
550+ scenarios in production
Nussknacker visual scenario designer — drag-and-drop streaming logic on Apache Flink
Why Teams Switch

Why teams stop building on raw Flink

Flink is a powerful engine, but building production decision logic on it requires too much code, too many specialists, and too long to ship

Business teams wait on developers for every logic change

Every rule update, threshold adjustment, or new condition requires a developer, a code review, a deployment. A fraud analyst wants to change a threshold from 500 to 800 – that's a Jira ticket, a sprint, three people involved.

With Nussknacker: The analyst changes the value in the visual editor, tests on historical data, and deploys. Time: 15 minutes.
The real cost of streaming adoption

The average Flink greenfield takes weeks and 40 – 115 engineer-days before the first business logic runs. Write the job in Java. Configure serialization. Build the JAR. Set up CI/CD. Every new use case restarts the cycle.

With Nussknacker: New scenario from first node to production – in hours, not months.
Engineers spend most of their time on ops, not business logic

Checkpoints, RocksDB tuning, connector reliability, Kubernetes orchestration, observability - Flink infrastructure consumes engineering capacity that should go toward delivering value.

With Nussknacker: Zero manual savepoints, zero 3 AM restarts. Deploy and rollback in seconds.
Flink expertise is your bottleneck

Self-managed Apache Flink costs $1.5 – 2.5M per year – up to 78% is salaries, not servers. Streaming data engineers are among the hardest roles to fill.

With Nussknacker: One Flink engineer sets up the platform. Business users build and modify scenarios independently.
How It Works

Visual platform that makes Flink accessible

Flink's missing interface – focused on delivering value with powerful real-time processing, not on coding

  • Visual drag-and-drop scenario designer
    Build on Flink. Use the visual interface, not code syntax. Wire connectors by dragging nodes on a canvas.
  • SpEL expressions – spreadsheet-level accessibility
    Domain experts write conditions as readable expressions. No Java, no boilerplate, no compile step.
  • One-click deployment to production Flink
    From design to running Flink job: testing, locking, and rollback built in. No CI/CD config required.
  • Full Flink capabilities, exposed visually
    Flink CEP, Flink SQL, stateful aggregations, time windows - all through the visual interface.
  • Built-in testing, version history, and one-click rollback
    Test scenarios on real historical data before deploying. Roll back any version in seconds - no CI/CD intervention needed.
  • Full observability out of the box
    Per-node event counts, behavioral metrics, dashboards - no custom instrumentation needed.
Nussknacker Overview: Real-Time Processing Applications Simplified
Nussknacker Overview
Use Cases

Start with your problem

Decision-makers think in problems they own – find yours below

Real-time fraud detection at scale

Define complex event patterns in a visual editor: card testing sequences, velocity rules, geographic anomalies – without writing Flink code. Business risk teams update detection logic directly.

Deploy rule changes in minutes, not weeks. Run pattern matching across millions of events per second with full Flink CEP under the hood.

Flink CEP Pattern matching Dynamic rules Low-latency Fintech
$47k $12k ALERT 2.1ms Fraud Detection

Stream ML predictions at scale

Plug any ML model into your streaming pipeline as a native enrichment step. Nussknacker handles batching, latency management, and model versioning while your data scientists iterate on model logic independently. Feature engineering and model calls become drag-and-drop nodes - no Flink expertise required to operationalize ML at scale.

ONNX PyTorch scikit-learn Model serving Feature engineering
Features ML Model v2.3.1 0.94 score ML Inference

Business logic at stream speed

Replace batch decision engines with streaming rules that react in real time. Credit decisions, pricing adjustments, and personalization logic run as stateful Flink computations – but are owned and updated by business analysts, not engineers. Audit trails, rollback, and A/B testing of decision variants are built in from day one.

Credit scoring Dynamic pricing Next-best-offer CRM integration Real-time marketing
Rule Approve Review Real-Time Decisioning

Predict failures before they happen

Ingest high-frequency sensor streams and detect anomaly patterns using Flink CEP without writing Java. Maintenance teams configure alert thresholds and complex temporal patterns through Nussknacker's visual interface. Reduce unplanned downtime and maintenance costs by acting on predictive signals, not reactive alarms.

Sensor streams Anomaly detection Sliding windows MQTT / Kafka Industrial IoT
ANOMALY sensor stream — 10k events/sec IoT & Predictive
Where We Fit

Not a rule engine, not raw Flink – something different

If your decision logic changes faster than engineers can ship it and your data moves faster than batch jobs can handle it – that's where Nussknacker belongs

Flink Custom Code Flink Rules Engine Nussknacker
Who owns the logic Engineers only Business teams* Business teams
ML inference Custom code Dedicated integration Built-in
Time to ship a logic change Sprint (1-2 weeks) Days Hours
Flink expertise required High Medium Setup only
Deployment cycle Dedicated CI/CD Dedicated CI/CD One-click, no CI/CD
See how we compare →
Production-Proven

Built for enterprise, proven at scale

Over 8 years in production across telecom, fintech, and enterprise environments

550+
scenarios in production
1M+
events / sec
8+
years in production
iliad PLAY
Rule deployment: 2 weeks → 2 hours
“Before Nussknacker, every change to our fraud detection rules required an engineering sprint. Today, our analysts deploy updated rules in hours, without writing a line of code.”
- Fraud Detection Team, iliad Group
Domain experts iterate on logic daily - no code required
“The visual designer changed how our teams collaborate. Engineers set up the integrations once. Business teams own the logic from that point on.”
- IT Department, international telecom provider
Built by TouK - 20+ years in JVM and data engineering for enterprise clients

Deploy your first real-time scenario in 15 minutes

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