Hermes Logistics
Built an end-to-end Azure data platform for sensor data analytics processing billions of JSON files with Delta Lake, achieving 99.9% data accuracy and 30% Azure cost reduction.
Hermes needed to process billions of IoT sensor readings from their logistics network. The data arrived as JSON files in massive volumes, with inconsistent schemas and quality issues. Existing processing was batch-only with 24-hour delays, making real-time operational decisions impossible.
Designed a Delta Lakehouse architecture with Landing, Curated, and Serving zones. Built real-time ingestion pipelines using Azure Databricks, Redis caching, and Azure Functions. Implemented automated schema evolution, data quality checks, and anomaly detection at every layer.
Built real-time ingestion pipelines for billions of JSON files using Azure Event Hubs and Databricks structured streaming.
Designed Landing → Curated → Serving architecture with ACID-compliant Delta Lake and automated schema evolution.
Implemented validation rules, anomaly detection, and data profiling at every pipeline stage with automated alerting.
Optimized queries by 40% through Z-Order indexing, partitioning, and SQL pool tuning. Reduced Azure costs by 30%.
Delivered Power BI and Tableau dashboards for sensor health monitoring, anomaly detection, and operational insights.
Built real-time ingestion pipelines for billions of JSON files using Azure Event Hubs and Databricks structured streaming.
Designed Landing → Curated → Serving architecture with ACID-compliant Delta Lake and automated schema evolution.
Implemented validation rules, anomaly detection, and data profiling at every pipeline stage with automated alerting.
Optimized queries by 40% through Z-Order indexing, partitioning, and SQL pool tuning. Reduced Azure costs by 30%.
Delivered Power BI and Tableau dashboards for sensor health monitoring, anomaly detection, and operational insights.
Automated validation across billions of sensor records
Optimized Azure infrastructure and compute patterns
From 24-hour batch to near real-time analytics
Handling massive IoT data volumes with ACID compliance
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