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Skysens Industrial IoT Automation

Industrial IoT Platform

Context & Challenge

Slow multi-cloud provisioning and unoptimized time-series processing delayed deployment of scalable IoT analytics across millions of daily data points.

Deploying industrial IoT solutions across diverse client facilities—ranging from food production plants to airports and automotive sites—demands robust, repeatable infrastructure. When cloud environments must be manually configured for each new deployment, onboarding timelines stall, and operating costs escalate rapidly. Skysens needed a standardized, high-throughput IoT analytics foundation. Their legacy setup struggled with provisioning speed across multi-cloud environments (Azure and GCP) and faced database bottlenecks when handling millions of concurrent daily time-series telemetry events.

Solution & Architecture

Engineered reusable Infrastructure-as-Code (IaC) modules on Azure and GCP, while building a time-series ML engine for predictive equipment failure detection.

Datapand re-architected the platform foundation by introducing a modular Infrastructure-as-Code (IaC) framework built on Terraform. This allowed entire production environments—including Kubernetes clusters, database instances, and queue systems—to be bootstrapped automatically in hours rather than days. To eliminate database write contention, transactional (OLTP) and analytical (OLAP) processing loads were split across PostgreSQL clusters managed by automated Airflow DAGs and RabbitMQ message queues. Additionally, a time-series machine learning engine was developed in Python to analyze sensor streams for abnormal operational signatures. By applying predictive anomaly detection models, the system catches equipment degradation—such as air pressure leaks or thermal spikes—before physical failure occurs.

Architecture & Technical Highlights

  • Designed a multi-cloud IaC provisioning layer in Terraform across Azure and GCP environments.
  • Separated transactional and analytical processing loads across PostgreSQL clusters to optimize real-time throughput.
  • Built a time-series ML anomaly detection engine to catch equipment degradation in food, airport, and automotive facilities.

Key Engineering Deliverables

  • Modular Terraform and Docker/Kubernetes deployment scripts for client environment bootstrapping.
  • Automated Airflow DAGs for time-series sensor data orchestration and RabbitMQ queue ingestion.
  • Predictive analytics modules for industrial pressure, tracking, and energy consumption telemetry.

Long-Term Operational Impact

The transformed platform reduced client environment setup times from 3 days down to just 4 hours (a 95% reduction). The decoupled architecture empowered Skysens to scale effortlessly across enterprise industrial IoT deployments while maintaining low operational latency.
Key Outcome & Impact

Cut environment provisioning time by 95% (3 days down to 4 hours)

Drastically accelerated client onboarding and reduced operational overhead by turning cloud infrastructure into automated, reusable software components.