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Sub-100ms Insights from Streaming Data
A high-performance stream analytics engine powered by Apache Flink and ClickHouse. Process complex event patterns, run OLAP queries on live streams, and detect anomalies with ML — all at sub-100ms latency. Purpose-built for teams that need speed, not just dashboards.
Architecture
A purpose-built analytics pipeline: Kafka for ingestion, Flink for stateful stream processing, ClickHouse + Elasticsearch for OLAP, and an intelligent alerting layer on top.
Capabilities
What Real-Time Stream Analytics Engine does, in the terms your engineers will evaluate it on.
Apache Flink-powered engine for complex event processing, windowed aggregations, and pattern matching on high-throughput event streams.
Run analytical queries on billions of rows of streaming data with sub-100ms response times using columnar OLAP engines.
Automatically detect outliers, trend shifts, and unusual patterns in real time — no manual threshold tuning required.
Go beyond simple thresholds with composite alerts, escalation policies, and root-cause correlation across related events.
Validate, cleanse, and mask data in-flight so only accurate, compliant data reaches your analytics layer.
Build and deploy custom analytics pipelines with SQL, Python, and REST APIs — no UI dependency.
Use Cases
Patterns our clients run in production today.
Score every transaction in under 100ms using streaming ML models. Detect fraud rings, velocity abuse, and account takeover patterns before the transaction completes.
Correlate logs, metrics, and traces from thousands of microservices in real time. Surface anomalies automatically and cut mean-time-to-resolution by 50%.
Compute user features from clickstream data in real time and serve personalized recommendations, offers, and content within the same session.
Process millions of sensor readings per second, detect equipment anomalies in real time, and trigger predictive maintenance workflows before failures occur.
Ecosystem
Plugs into your streaming infrastructure and observability stack. Purpose-built connectors for high-throughput analytics workloads.
Technology
Schedule a demo to see Real-Time Stream Analytics Engine working against your data, and talk through what deployment looks like.