Real-Time Telemetry and Analytics

At 10:01, everything looks fine. By 10:03, it’s not. The issue isn’t that something suddenly broke — it’s that the business realized it too late.

Real-time telemetry and analytics are not about reports. They are about seeing what is happening now, not yesterday. This is where businesses either lose money — or prevent losses.

Without real-time analytics:

  • incidents are detected too late;
  • losses grow unnoticed;
  • decisions rely on outdated data;
  • there is no operational control.

What the System Looks Like Inside

Telemetry is not a single service. It is a continuous data pipeline flowing through multiple layers:

  • data sources (devices, services, applications);
  • transport layer (streaming, message queues);
  • processing layer (real-time analytics);
  • storage;
  • visualization.

A weakness in any layer breaks the entire chain.

Where Data Gets Lost

The most common issue is not analytics — it’s delivery.

  • missing events;
  • duplicates;
  • delays;
  • inconsistency.

If your data is unreliable, your analytics is useless.

Why “Almost Real-Time” Is a Problem

Many systems operate with minute-level delays. That may be acceptable for reporting — but not for operations.

In high-load systems, even small delays lead to:

  • loss of control;
  • accumulated errors;
  • slow response.

Stream Processing

The key is not just collecting data — but processing it on the fly.

  • filtering;
  • aggregation;
  • anomaly detection;
  • trigger-based actions.

This turns data into immediate action.

Fault Tolerance Is Mandatory

A telemetry system cannot afford to fail.

  • redundant data streams;
  • message retries;
  • horizontal scaling;
  • failure handling.

If you lose data, you lose visibility.

Architecture That Works

  • event-driven architecture;
  • message brokers (Kafka, MQTT);
  • stream processing;
  • layered design;
  • microservices.

This approach supports millions of events per second.

Technology Stack

  • Node.js (NestJS) — ingestion layer;
  • Kafka — data streaming;
  • Redis — fast processing;
  • PostgreSQL — storage;
  • ClickHouse — analytics;
  • Docker / Kubernetes — scaling.

Business Impact

  • instant issue detection;
  • reduced financial losses;
  • real-time control;
  • faster decision-making.

Real-time analytics is not about data. It’s about business reaction speed.

Need a Real-Time Telemetry System?

We design systems that process events in real time and provide full operational control.

What is telemetry?
It is the collection and transmission of system data.
Why is real-time important?
It allows immediate response.
Which technology is best?
Kafka and event-driven architecture.
Can it scale?
Yes, with the right architecture.