IoT Platforms & Real-Time Data

When developing an IoT ecosystem, we analyze data flows: devices → broker → analytics → control modules. Each stage must operate with minimal latency and guaranteed message delivery.

The architecture is chosen based on business goals and operational specifics. A monolith may suit pilot solutions, while microservices fit scalable platforms, and nanoservices are ideal for systems with extremely high data density. These autonomous modules perform specific functions and allow system updates without downtime.

One of the key principles behind such solutions is the event-driven model. A well-designed IoT platform remains resilient to failures, easily scalable, and straightforward to maintain. Clear module interaction logic reduces costs and accelerates development, ensuring reliable performance even under millions of events per second.

Event-driven architecture is the foundation of IoT. Every sensor or gateway generates a stream of events, and the system responds to them instantly. We use Kafka, NATS, and Redis Streams to ensure guaranteed message delivery and efficient distribution. If a service becomes temporarily unavailable, data isn’t lost — storage queues and retry mechanisms maintain reliability. This structure gives businesses confidence that the entire ecosystem reacts promptly and remains stable. The event-driven model makes solutions adaptive: it scales with growing load, supports the addition of new scenarios, and stays reliable even when individual components fail. For the client, this means stability, fast response, and easy functional expansion.

In high-throughput data projects, separating read and write operations (CQRS) is essential — it reduces load and improves response times. Event Sourcing complements this by storing every change in equipment state as an event. This allows the system to reproduce any situation, trace operational logic, and analyze device behavior over time. The approach is particularly effective for transport systems, industrial automation, and smart city infrastructures. CQRS and Event Sourcing together provide transparency, scalability, and data accuracy. Clients gain a stable foundation that evolves without performance loss and remains manageable as the number of connected devices grows.

Different protocols serve different purposes. MQTT is efficient under unstable connections and ideal for IoT devices. WebSocket enables real-time updates for operator dashboards, while HTTP is used for APIs and administrative operations. A typical flow looks like this: device → MQTT → Kafka → WebSocket → operator interface. This pipeline ensures instant response and minimal data latency. Properly designed transport architecture guarantees stability and performance even with thousands of active connections. For the client, this means real-time updates and reliable interaction with the infrastructure.

Many clients already use cloud services such as AWS IoT, Azure IoT Hub, or Google IoT Core. We build an adapter layer on top of these platforms that adds error handling, logging, and retry control. This makes the ecosystem independent of a single vendor and resilient to external service outages. As a result, infrastructure can evolve gradually without complex migrations. The client receives a flexible solution that’s ready for integrations and future changes — without compromising security or stability.

For large-scale projects, we use Kafka; for lightweight and ultra-fast ones — NATS; and for real-time data streams — Redis Streams. All services are connected through a unified event bus that manages data flow, distributes load, and ensures transparent communication. This structure becomes the core of the entire ecosystem. It increases fault tolerance, simplifies scaling, and prevents data loss. As a result, businesses achieve stable performance under load and predictable, well-coordinated processes.

Go is ideal for high-load solutions: it’s compact, fast, and stable under thousands of concurrent connections. With concise syntax and built-in concurrency, the language ensures reliability while consuming minimal resources. Combined with Docker and Kubernetes, Go provides predictable performance and effortless microservice scalability. This simplifies maintenance and keeps the system fully controllable under any load.

We use proven, production-grade tools:

• MQTT: eclipse/paho.mqtt.golang, fhmq/hmq
• Kafka: segmentio/kafka-go, confluent-kafka-go
• gRPC: google.golang.org/grpc
• WebSocket: gorilla/websocket
• REST: chi, gin, fasthttp
• Worker Pools: ants, tunny, goworker

These libraries deliver stable performance and clean, maintainable code. The team can focus on business logic, while the client receives a reliable and scalable solution.

• Telemetry Service — handles telemetry ingestion and validation.
• Aggregator — calculates metrics and aggregates analytics.
• Alert Service — generates notifications and alerts.
• Device State Service — determines equipment status and activity.

This modular structure increases system resilience: even if one component fails, the others continue to operate. For the client, it means a stable architecture that can be expanded and scaled safely.

We use different databases for different IoT tasks:

• PostgreSQL — metadata, users, and commands.
• MongoDB — logs and events.
• ClickHouse — analytics and aggregations.
• InfluxDB — time series and telemetry data.

This combination provides a balance between speed, reliability, and storage cost. The client gets a robust solution where data is always available and processed quickly.

IoT platforms generate terabytes of data. We design partitioning, sharding, and retention policies to distribute data evenly and archive outdated records.

This keeps storage fast and the system cost-efficient. The approach enables years of stable operation without performance degradation — maintaining both speed and reliability over time.

Each device is identified using JWT, certificates, and access keys. The infrastructure verifies connection authenticity and enforces permissions. This eliminates the risk of unauthorized interference and protects transmitted data.

We limit the number of messages from a single source to prevent overloads and attacks. Controlling data transmission frequency keeps the system stable and predictable under peak loads.

Every command and operation is logged with timestamp, source, and result details. This auditing simplifies event analysis and ensures transparency. For the client, it provides trust and full control over the entire ecosystem.

Monitoring is an essential part of the architecture. Each service exposes metrics to Prometheus, while Grafana visualizes key indicators such as latency, errors, and event volume.

This enables rapid detection of bottlenecks and prevents failures before they affect users.

We use Loki, ELK, and Jaeger for logging and tracing. With contextual tracking (context.Context), we trace the full request path — from the device to the database. This accelerates diagnostics and improves service quality.

The monitoring system not only collects data but also reacts: it notifies engineers, restarts services, and sends alerts to Slack. Automation reduces downtime and increases resilience.

As a result, support becomes proactive, and the entire ecosystem remains predictable and manageable.

The frontend receives updates via WebSocket and instantly displays equipment status, alerts, and commands. This gives operators a complete real-time view of the system and increases operational efficiency.

We use REST for integrations and mobile applications, and GraphQL for administrative panels. All requests are secured with tokens and versioned. Such an API accelerates client-side development and simplifies the integration of new services.

For mobile clients, we use binary formats (Protobuf) and compression to speed up data transfer and reduce network overhead. This ensures stable performance even under weak connection conditions.