The Visual Flink Interface

Low-Code Apache Flink

Build on Flink without the complexity. Design real-time streaming jobs 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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550+ jobs in production
Nussknacker Designer with ML integration, featuring a low-code drag-and-drop interface for real-time stream processing and machine learning-driven automation.
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 job 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 jobs independently.
How It Works

Ship streaming workflows without Flink expertise

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

  • Visual drag-and-drop job builder
    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 workflows 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.
Visual Designer

Drag-and-drop streaming processing

Nussknacker architecture diagram
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 across channels, and geographic anomalies – without writing Flink code. Business risk teams own and update detection logic directly, without routing every change through engineering.

Deploy rule changes in minutes, not weeks. Run pattern matching across millions of transactions per second with full Flink CEP under the hood – and capture multi-step sequences that span time windows without touching a line of code.

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 – your data scientists iterate on model logic independently while the pipeline runs at full throughput.

Feature engineering and model calls become drag-and-drop nodes. No Flink expertise is required to operationalize ML at scale, and model version swaps happen without re-deploying the pipeline – inference latency stays consistent.

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 – owned and updated by business analysts, not engineers.

Audit trails, rollback, and A/B testing of decision variants are built in from day one. When a rule changes, the analyst deploys it directly – no ticket, no sprint, no waiting for the next release cycle.

Credit scoring Dynamic pricing Next-best-offer CRM integration Real-time marketing
Events Business Logic conditions · thresholds · rules Approve Alerts Route Actions 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 – no platform specialist required.

Reduce unplanned downtime and maintenance costs by acting on predictive signals, not reactive alarms. When thresholds need adjusting, the team updates them directly without re-deploying the pipeline or involving engineering.

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

Flink CEP as the nervous system for AI agents

AI agents fail at scale when connected directly to raw event streams. Platforms processing over 500,000 events per second cannot route each one to an LLM – the cost alone makes it impossible. Flink CEP and window aggregations solve this: complex event patterns are detected and signals pre-aggregated across time windows before any AI agent is involved, cutting inference volume by over 99%.

With Nussknacker, business define CEP patterns and sliding window aggregations visually. Agents receive grounded, pre-aggregated signals – not noise. The result is autonomous decisions that are fast, cost-efficient, and auditable.

Flink CEP Window Aggregation Pattern detection Event-driven agents
>500K/s Flink CEP pattern filter −99% noise window agg aggregated AI Agent decides deterministic patterns → grounded AI decisions Agentic AI
Where We Fit

Not raw Flink, not a custom delivery system – 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 RAW Own Delivery System Nussknacker
Time to first production value Months (Flink setup first) Months (platform first) Weeks
Who deploys a rule change Engineers only Business teams* Business teams
Time to ship a logic change Sprint (1–2 weeks) Days Hours
User complexity High Medium Low
Flink expertise required High (ongoing) High (build + maintain) Setup only
Deployment cycle CI/CD Pipelines Dedicated deployment engine One-click, no CI/CD

* Only after the custom solution is fully built and deployed

Production-Proven

Built for enterprise, proven at scale

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

550+
jobs 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 job in 15 minutes

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