ISEATEC

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.