Data Science

Real-Time Analytics — Act on Data in the Moment

Batch analytics tells you what happened yesterday. Real-time analytics tells you what is happening now — enabling operational responses, live dashboards, and streaming AI acting on events as they occur.

Capabilities

Real-Time Analytics — deep expertise

Stream Processing Architecture

Kafka, Flink, and Spark Streaming architectures for exactly-once processing, low latency, and high throughput across any event volume.

Apache KafkaApache FlinkSpark Structured StreamingKinesis

Real-Time Dashboards

Live operational dashboards refreshing in sub-second intervals — powered by streaming aggregations on Apache Druid, ClickHouse, or Apache Pinot.

Apache DruidClickHouseApache PinotGrafana

Time-Series Databases

InfluxDB, TimescaleDB, and QuestDB for high-frequency IoT sensor data, financial tick data, and operational metrics at scale.

InfluxDBTimescaleDBQuestDBOpenTSDB

Event-Driven Microservices

Kafka-based event-driven architectures decoupling services — scalable, resilient systems reacting to business events in real time.

KafkaSchema RegistryEvent SourcingCQRS

Streaming ML Inference

ML model inference on streaming events — real-time fraud scoring, personalization, and anomaly detection at millisecond latency.

Online InferenceFeature ServingStreaming PredictionsLatency SLAs

Data Freshness SLAs

Pipeline SLA monitoring ensuring real-time data meets freshness commitments — automated remediation and business impact quantification for delays.

Data SLAsFreshness MonitoringAutomated AlertingImpact Analysis
Real-Time Results

Real-Time Analytics Milliseconds Matter

1B+
Events processed per day
<100ms
End-to-end processing latency
99.99%
Stream processing availability
10×
Faster operational response
Our Approach

From Yesterday's Reports to Real-Time Decisions

01
Use Case Scoping
Define the decisions requiring real-time data and the acceptable latency and freshness thresholds.
02
Pipeline Architecture
Design the streaming pipeline from event sources through stream processing to analytics serving layer.
03
Build
Implement stream processing jobs, real-time dashboards, and alerting rules with end-to-end latency validation.
04
Operate
Deploy with auto-scaling, lag monitoring, and SLA-driven alerting to ensure continuous low-latency operation.

Ready to explore Real-Time Analytics?

Our specialists will design a tailored solution for your organization.