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ISEATEC
Scalable IoT Sensor Data Platform on AWS with Real-Time Analytics
Sensor events
billions/day
Query time
baseline
→
−40%
Processing
batch only
→
near real-time
Iseatec is focused on lightweight and bridge construction, structural dynamics, and structural monitoring.
What changed
Before
Ingest
Sensor feeds
single site
Process
Batch jobs
Store
Relational store
Consume
Static reports
After
Ingest
Sensor gateway
billions of events/day
Process
Apache Spark
stream + replay
Custom transforms
Store
InfluxDB
time-series
Consume
Grafana
Client dashboards
A single-site batch pipeline became a streaming platform where clients define their own transformations and history can be reprocessed on demand.
What we built
The client needed a scalable, secure, and efficient data platform to handle large volumes of IoT sensor data from multiple sources. Their existing system lacked real-time processing, robust analytics, and centralized monitoring.
Key Features
Designed a pipeline to upload sensor data from multiple sources into AWS S3 for secure and durable storage.
Enabled ingestion of both real-time streams and historical batch data.
Implemented Apache Spark for near real-time processing and batch workloads.
Built functionality to reprocess historical data from S3 for advanced analytics and deeper insights.
Added support for custom transformations and aggregations, giving clients flexibility before persisting data.
Configured InfluxDB for high-performance time-series storage.
Enabled real-time writes of sensor data into InfluxDB.
Deployed Grafana dashboards for real-time monitoring, alerts, and business insights.
Implemented role-based access control to ensure secure, client-specific data access.
Designed and deployed scalable AWS infrastructure using Terraform.
Dockerized applications for consistent environments across development, staging, and production.
Deployed Kubernetes to support auto-scaling and efficient resource allocation.
Established a CI/CD pipeline with GitHub Actions and Terraform for automated, reliable infrastructure updates.
Implemented Prometheus and Grafana monitoring to track system health and resource utilization.
Results
Processed billions of sensor events daily with near real-time performance.
Enabled historical data reprocessing for advanced analytics.
Allowed clients to define custom data transformations and aggregations.
Reduced data query time by over 40% using InfluxDB time-series optimization.
Enforced role-based access control, ensuring data privacy and compliance for each client.
Achieved 99.9% uptime with scalable AWS infrastructure to handle peak workloads.
Cut deployment times from hours to minutes with Terraform automation.
CI/CD pipeline enabled zero-downtime deployments and faster release cycles.
Increased system reliability with proactive monitoring using Prometheus and Grafana.
Stack
Engagement: Ongoing · Real-time & Industrial Data Platforms, Product Engineering, Platform & Cloud Engineering