Real-Time Stream Analytics Engine
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.
10M+
Events / Second
<100ms
P99 Latency
99.99%
Data Reliability
ML-Native
Anomaly Detection
10M+
Events / Second
<100ms
P99 Latency
99.99%
Data Reliability
ML-Native
Anomaly Detection
Architecture
Stream Analytics 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.
Event Ingestion
- Apache Kafka
- Kafka Streams
- CDC Connectors
- REST / gRPC APIs
Stream Processing
- Apache Flink (Stateful)
- Windowed Aggregations
- Complex Event Processing
- Exactly-once Semantics
OLAP & Search
- ClickHouse (Columnar)
- Elasticsearch (Full-text)
- Apache Iceberg (Lakehouse)
- Pre-aggregated Rollups
Intelligence & Action
- ML Anomaly Detection
- Composite Alert Engine
- Embedded Analytics API
- Incident Correlation
Event Ingestion
Stream Processing
OLAP & Search
Intelligence & Action
Features
Key Features
Stateful Stream Processing
Apache Flink-powered engine for complex event processing, windowed aggregations, and pattern matching on high-throughput event streams.
- Flink-native stateful processing with checkpointing
- Tumbling, sliding & session window aggregations
- Complex event pattern matching (CEP)
- Dynamic auto-scaling based on backpressure
Sub-Second OLAP Queries
Run analytical queries on billions of rows of streaming data with sub-100ms response times using columnar OLAP engines.
- ClickHouse for high-cardinality columnar analytics
- Elasticsearch for full-text search & log analytics
- Pre-aggregated materialized views
- SQL-native query interface with joins & CTEs
ML-Powered Anomaly Detection
Automatically detect outliers, trend shifts, and unusual patterns in real time — no manual threshold tuning required.
- Online ML models trained on live streams
- Seasonal & trend-aware anomaly scoring
- Multi-dimensional outlier detection
- Automatic baseline learning & drift detection
Intelligent Alert Engine
Go beyond simple thresholds with composite alerts, escalation policies, and root-cause correlation across related events.
- Multi-condition composite alert rules
- Escalation policies with PagerDuty & Slack
- Incident timeline & root-cause grouping
- Alert deduplication & suppression windows
Data Quality Enforcement
Validate, cleanse, and mask data in-flight so only accurate, compliant data reaches your analytics layer.
- In-stream schema validation & enforcement
- PII detection & automatic masking
- Dead-letter queues for malformed events
- Data freshness & completeness SLAs
Developer SDK & APIs
Build and deploy custom analytics pipelines with SQL, Python, and REST APIs — no UI dependency.
- Python SDK for Flink job authoring
- SQL interface for ad-hoc & scheduled queries
- Embedded analytics REST API
- Terraform & Helm for infra-as-code deployment
Use Cases
How Teams Use Real-Time Stream Analytics Engine
Real-Time Fraud Scoring
Score every transaction in under 100ms using streaming ML models. Detect fraud rings, velocity abuse, and account takeover patterns before the transaction completes.
- Sub-100ms per-transaction scoring
- Streaming ML with online model updates
- Pattern-based fraud ring detection
- 75% reduction in fraud losses
Infrastructure Observability
Correlate logs, metrics, and traces from thousands of microservices in real time. Surface anomalies automatically and cut mean-time-to-resolution by 50%.
- Unified log, metric & trace correlation
- ML-powered incident detection
- 50% faster mean-time-to-resolution
- Full-text search across petabytes of logs
Real-Time Personalization
Compute user features from clickstream data in real time and serve personalized recommendations, offers, and content within the same session.
- In-session feature computation via Flink
- Real-time recommendation scoring
- A/B test metrics in seconds, not hours
- 40% uplift in user engagement
IoT Telemetry Analytics
Process millions of sensor readings per second, detect equipment anomalies in real time, and trigger predictive maintenance workflows before failures occur.
- Millions of sensor events per second
- Streaming anomaly detection on telemetry
- Predictive maintenance alerting
- Edge-to-cloud aggregation pipeline
Integrations
Analytics Ecosystem
Plugs into your streaming infrastructure and observability stack. Purpose-built connectors for high-throughput analytics workloads.
Stream Processing
OLAP & Search Engines
Observability
Cloud & Deployment
Technology
Built With Modern Tech Stack
Ready to get started with Real-Time Stream Analytics Engine?
See how Real-Time Stream Analytics Engine can transform your business. Schedule a personalized demo with our team today.