IoT Data Platform on AWS | Scalable & Secure Cloud Architecture

Client

Our client is an Industry 4.0 / IoT technology company developing connected solutions for industrial environments. Their platform collects telemetry data from distributed IoT devices and sensors deployed across multiple locations.

The company required a cloud platform that could scale reliably with increasing device counts, handle high-throughput data ingestion, and meet strict security expectations common in industrial and regulated environments. In addition to the initial build, they needed long-term operational support to ensure stability, observability, and controlled cost growth.

Challenge

The project involved designing and operating a cloud-native IoT data platform under real-world production constraints.

Key challenges included:

1. Scalability
   The platform needed to ingest and process large volumes of telemetry data from MQTT-enabled devices while remaining responsive and cost-efficient as the number of connected devices increased.

2. Security
   The AWS environment required strict identity and access management, encrypted communication, and isolation between development and production environments to reduce blast radius and operational risk.

3. Observability
   The client needed centralized monitoring that provided visibility into device connectivity, backend services, and infrastructure health — without building a custom monitoring system from scratch.

4. Flexibility & Cost Control
   The architecture had to support rapid iteration during early development while remaining modular enough to evolve into a multi-tenant SaaS platform in later phases.

Solution Overview

We designed and implemented a modular AWS-based IoT data platform with a strong emphasis on security, scalability, and operational clarity.

1. Account Structure
   Separate AWS accounts were provisioned for development and production to enforce environment isolation, reduce risk, and simplify access control.

2. Networking
   A dedicated VPC was configured with public and private subnets, NAT gateways, and route tables. Services handling sensitive workloads were deployed in private subnets, while controlled ingress was exposed via secured endpoints.

3. Compute Layer
   Containerized backend services were deployed on Amazon EC2 instances running the latest Amazon Linux AMIs. Spot Instances were used in non-production environments to reduce costs during development and testing.

4. Backend & IoT Integration
   The IoT backend was developed in Go, handling MQTT-based communication with devices, message processing, and API-based integration with downstream systems.

5. Observability & Monitoring
   Amazon CloudWatch was used for infrastructure-level metrics and logging, while Grafana provided dashboards for application and device-level visibility.

6. Security Controls
   AWS WAF was deployed to protect exposed endpoints, and IAM roles followed the principle of least privilege across services and environments.

7. Infrastructure as Code
   All infrastructure was defined using Terraform, enabling repeatable deployments, version control, and safe environment changes.

Architecture & Operational Decisions

Several design decisions were made to balance performance, security, and long-term maintainability.

Containerization allowed backend services to be updated independently and simplified future migration to managed container platforms. The use of Infrastructure as Code reduced configuration drift and enabled fast environment replication. Observability tooling was selected to give both engineers and stakeholders a clear view of system behavior without excessive operational overhead.

Results & Value Delivered

The engagement resulted in a stable, production-ready IoT data platform on AWS.

• Delivered a secure, scalable cloud architecture capable of handling large volumes of IoT telemetry data.
• Implemented a reliable MQTT-based backend in Go for device communication and control.
• Improved operational visibility through centralized monitoring and dashboards.
• Reduced infrastructure costs during development using spot instances and modular design.
• Provided ongoing support under SLA, including monitoring, troubleshooting, and operational guidance.

The platform is now positioned to scale further and evolve into a full SaaS offering.

Key Takeaways

• AWS provides a strong foundation for scalable IoT data platforms when combined with proper account structure and networking.
• Security and observability must be designed in from the start, especially for industrial IoT workloads.
• Infrastructure as Code and containerization significantly reduce operational risk and enable faster iteration.

Technologies Used

1. Cloud Platform: AWS (EC2, VPC, IAM, CloudWatch, WAF, AWS Site-to-Site VPN, Amazon Linux)
2. Infrastructure as Code: Terraform
3. Monitoring: Grafana, CloudWatch, Telegraf, InfluxDB, PostgreSQL Timescale
4. Development Language: Go (Golang)
5. Protocol: MQTT for IoT communication, REST
6. Containerization: Docker