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Streaming Analytics at Enterprise Scale

Traditional data warehouses often fall short when it comes to addressing fast-moving, time-sensitive challenges. By the time data is ingested, aggregated, and made available, the opportunity to act has often passed—especially in dynamic environments such as telecommunications, gaming, and fraud detection.

At Millersoft, we specialize in building systems that operate on live data streams, delivering insights and actions in real time—not minutes or hours later.

We leverage cutting-edge tools like Apache Kafka, Kafka Streams (KSL), Apache Druid, MongoDB, and RabbitMQ to architect solutions that handle high-velocity data at scale. Whether it’s monitoring fraud patterns as they emerge or supporting thousands of users with up-to-the-second operational dashboards, our systems are built to perform.

Need to update 40,000 insurance agent dashboards every second with the latest call data? With our technology stack, we make it possible—efficiently, reliably, and at scale.

Millersoft Streaming Analytics Services​

Real-Time Data Ingestion & Processing
  • Ingest streaming data from IoT devices, applications, APIs, and logs
  • Tools: Apache Kafka, Apache Pulsar, Amazon Kinesis, Azure Event Hubs, Google Pub/Sub
  • Support for schema management and message enrichment
Real-Time Data Ingestion & ProcessingStream Processing Pipelines

Real-time transformation and analytics using:

  • Apache Flink
  • Kafka Streams
  • Spark Structured Streaming

Complex event processing (CEP), filtering, joins, and aggregations on the fly

Operational Monitoring & Alerting
  • Integration with Grafana, Prometheus, and Loki for real-time observability
  • Anomaly detection and auto-alerting based on streaming metrics and log events
  • Support for custom dashboards and automated incident triggers
Data Lakehouse Integration

Streaming ingestion into lakehouses for continuous data freshness Tools: Apache Hudi, Delta Lake, Iceberg Supports both raw and curated layers for downstream analytics

Streaming ETL/ELT
  • Real-time data movement from sources (e.g., databases, CRM, ERP) to analytics platforms
  • Stream joins, windowed aggregations, and stateful transformations
  • Output to cloud storage, data warehouses, or message queues
IoT & Sensor Data Analytics
  • Ingest and analyze telemetry from edge devices and sensors
  • Real-time KPI tracking, alerts, and edge-to-cloud synchronization
Fraud Detection & Risk Monitoring

Real-time behavioral tracking and anomaly detection for high-risk systems (e.g., finance, logistics) AI integration for stream-based scoring and decisioning

API & Webhook Stream Processing

Real-time data capture from APIs and webhook events Instant analysis for CRM activity, transaction events, or e-commerce behavior

Streaming AI/ML Model Serving
  • Integration of models into stream processors for online predictions
  • Use cases: churn prediction, dynamic pricing, recommendation systems
Streaming Architecture Design & Optimization
  • Architecture design for high-throughput, low-latency streaming workloads
  • Scalability, fault-tolerance, and cost optimization
  • Cloud-native and hybrid deployments