Canada
Puerto Rico
USA
Belgium
France
UK
India
Client Background
The client operates a satellite system designed to make multi-spectral observations, enabling land managers and policymakers to make data-driven decisions about natural resources and environmental management.
Client Need
The client encountered significant challenges while transitioning their satellite image processing operations to a cloud-native environment, including:
Struggled to migrate 50 years of satellite image data and processing workflows to a modern cloud infrastructure
Faced difficulties in modernizing and refactoring a large-scale on-premises solution stack into a cloud-native architecture
Needed to transform a legacy 80TB OLTP database into a distributed data model capable of supporting cloud-based operations
Encountered limitations in deploying legacy C-code processing kernels, requiring containerization
Lacked hands-on expertise to navigate deep cloud-native environments
Solution
We implemented a cutting-edge, cloud-native approach to transform the client’s operations.
Public Cloud Hosting: Developed a fully cloud-hosted solution to process Landsat data from raw radio signals to finished TIFF images, accessible worldwide
Containerization & Code Refactoring: Migrated image processing software into Docker containers, refactoring the code to use uniform libraries, RPMs, and build options
Data Lake Migration: Transferred 11+ million scenes and data artifacts from on-premises storage to a cloud-based data lake
End-to-End Processing: Processed 117,000 satellite scenes from raw data to finished images on the cloud
Hybrid Deployment: Demonstrated the same containerized code running in both cloud-native instances and on-site container clusters
Database Modernization: Refactored the 80TB OLTP database into a combination of data lake, NoSQL, SQLite, data warehouse, and OLTP models
CI/CD Pipeline: Built a cloud-native CI/CD pipeline for seamless build, test, and deployment tasks
Realized Benefits
The solution delivered transformative results, including:
Improved image processing performance by 100x compared to on-premises systems
Reduced image processing costs by 2,000x, lowering expenses from $100 per scene on-premises to just $0.05 per scene in the cloud
Achieved a staggering 200,000:1 price-performance advantage
Tools & Technologies
AWS Batch Lambda
Amazon ElastiCache
CloudTrail
CloudWatch
ECS
Amazon S3
Redshift
Amazon SQS & SNS
AWS Athena
DynamoDB
Oracle
Ansible
GitHub
Jenkins
Docker
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