Geospatial Data Modernization Solution​

Client


An American leading provider of Corridor Safety Intelligence, specializing in strengthening energy network resilience through innovative AI and multi-source data integration..​​​

Business Challenge​


  • Outdated infrastructure/ scalability and performance for real-time satellite data processing.​​
  • Fragmented storage systems create inefficiencies in data management.​​
  • Lack of real-time data integration restricts analytics capabilities.​​
  • Manual workflows reduce throughput and increase complexity.​​
  • Security and compliance risks arise from a fragmented architecture.​​

Business Objective​​


  • Reduce power outages using advanced geospatial analytics.​​
  • Mitigate fire and vegetation risks with satellite data.​​
  • Create a real-time risk management solution.​
  • Automate infrastructure risk classification for efficiency.​​
  • Innovate continuously through a geospatial ML lab.​​​

Approach


  • Analyzed the geospatial data platform with AWS and developed modernization blueprints.​​
  • Migrated data from satellite imagery providers and on-prem systems to AWS analytics.​​
  • Integrated cloud applications for improved performance and scalability.​​
  • Automated real-time and scheduled data ingestion using serverless architecture.​​
  • Used AWS Lambda and Fargate for event-driven processing and integration.​​
  • Enabled advanced analytics with GeoSpatial Machine Learning in SageMaker.​​
  • Stored spatial data in RDS PostgreSQL for analysis.​​
  • Created an integrated architecture for scalable image processing and ETL pipelines.​

Technology Components


  • Compute: AWS Lambda, AWS Fargate, AWS Glue​​
  • Storage: Amazon S3, RDS PostgreSQL​​
  • Data Processing & Analytics: Amazon SageMaker, Jupyter Notebooks​​
  • Data Management: MongoDB Atlas, Amazon Redshift​​

Transformational Effects​


  • Achieved over 25% cost savings AWS Savings Plans and Reserved Instances for EC2 & Fargate workloads ​​​
  • Saved $150K+ in license costs by migrating workloads from Cloud ETL to AWS Glue and Kinesis​​
  • Reduced issue resolution time by 15% through automated monitoring of workloads​​
  • Enabled 10+ geospatial data products like Vegetation Risk, Fire Potential, and Asset Audit​​

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