When milliseconds matter, batch processing is not enough
Some business decisions cannot wait for overnight batch runs. Fraud detection must happen before a transaction completes. Anomaly detection must trigger alerts while there is still time to act. Real-time personalisation must respond before a customer moves on. Apache Flink processes event streams continuously with true low-latency, stateful computation. Node deploys Flink for automation that operates at the speed of your business.
What Flink does and why it matters
Apache Flink is a distributed stream processing engine that treats real-time data as a first-class citizen rather than an afterthought. While many processing frameworks started as batch engines and bolted on streaming later, Flink was designed from the ground up for continuous, stateful computation over unbounded data streams.
Flink maintains application state internally with consistent checkpointing, meaning it can recover from failures and resume processing from exactly where it left off without losing or duplicating events. This makes it suitable for use cases where correctness is as important as speed - financial calculations, compliance monitoring and operational alerting.
Flink is used in production at companies including Alibaba (processing billions of events per second during Singles' Day), Uber, Netflix, Ericsson, ING Bank and Booking.com. It handles some of the most demanding real-time workloads on the planet.
How we deploy Flink for business automation
We deploy Flink for automation scenarios that require continuous, real-time processing. Where Airflow orchestrates scheduled batch workflows, Flink handles the always-on stream processing that runs between those scheduled jobs.
In an AI-driven automation stack, Flink processes the real-time event streams from Kafka, applies feature engineering and windowed aggregations, feeds enriched data to ML models for real-time inference and routes the results to downstream systems. A recommendation engine, for example, continuously processes user behaviour events, updates feature stores and serves fresh predictions, all with sub-second latency.
Key capabilities we implement
True stream processing - process events individually as they arrive, not in micro-batches. Flink's event-time processing handles out-of-order events correctly, ensuring accurate results even when data arrives late or from distributed sources with clock skew.
Stateful computation - maintain running state across billions of events with exactly-once consistency guarantees. Flink manages state internally with efficient serialisation, incremental checkpointing and rescalable state backends. Applications can maintain complex data structures - maps, lists, aggregations - that evolve continuously as events flow through.
Complex event processing (CEP) - detect patterns across event streams in real time. Define pattern sequences like "three failed login attempts from different locations within five minutes" and trigger automated responses instantly. CEP is essential for fraud detection, anomaly alerting and operational monitoring.
Flink SQL - express stream processing logic using standard SQL, making it accessible to analysts and engineers who already know SQL. Create materialised views, streaming joins and windowed aggregations without writing Java or Python code.
Unified batch and streaming - use the same Flink engine for both real-time streaming and batch processing. This eliminates the complexity of maintaining separate systems and ensures consistent results regardless of processing mode.
Savepoints and operational control - take consistent snapshots of running applications, allowing zero-downtime upgrades, A/B testing of processing logic and schema evolution without data loss.
Flink in your automation stack
Flink and Kafka form a natural pair - Kafka captures and distributes events while Flink processes them in real time. Airflow manages the broader workflow orchestration, scheduling batch Spark jobs that complement Flink's continuous processing. Superset visualises both real-time and historical metrics. Node architects this complete real-time automation layer for mission-critical use cases.
Trusted in production worldwide - Apache Flink processes event streams where latency is measured in milliseconds. Alibaba handles billions of transactions through it during Singles' Day, Uber calculates real-time pricing with it, and Pinterest delivers real-time ad targeting. Spotify uses Flink for live music recommendations and ING Bank runs fraud detection on streaming transaction data. Node deploys and operates Flink with the same production standards these organisations demand.
Talk to us about real-time stream processing.
Drop us a line, and our team will discuss how Flink can power your real-time automation requirements.