Canada
Puerto Rico
USA
Belgium
France
UK
India
Client Background
The client operates a satellite system for environmental monitoring.
Client Need
Lack of necessary internal cloud expertise and experience to modernize and refactor their large on-premises solution stack to a cloud-native architecture
Lack of training and hands-on experience in cloud-native environments
Difficulty deploying and managing legacy C code processing kernels leading to operational inefficiencies
Solution
We implemented the following solutions:
Cloud-Based Data Processing Platform: Implemented a fully public cloud-hosted solution, transforming raw satellite signals into finished TIFF images and enabling global access to critical environmental data
Modernized Application Deployment: Migrated image processing software to Docker containers and refactored code
Accessible Cloud Data Lake: Migrated 11+ million scene and data artifacts from on-premises storage to a cloud-based data lake
Data Processing & Delivery: Processed 117,000 satellite scenes from raw data to finished images on the cloud-native solution
Hybrid Cloud Compatibility: Demonstrated the ability to run code containers on both cloud-native instances and on-site container clusters
Data Accessibility: Migrated and refactored an 80TB OLTP database to a combination of data lake, NoSQL, SQLite, data warehouse, and OLTP, optimizing data storage
Improved Release Velocity: Implemented a cloud-native CI/CD pipeline for all build, test, and deployment tasks
Realized Benefits
The solution implemented yielded the following benefits:
Improved image processing performance by 100x compared to on-premise
Reduced image processing costs by 2,000X, from $100.00 per scene on-premises to $0.05 per scene in the cloud
Delivered a 200,000:1 price-performance advantage over on-premises processing with the cloud-hosted solution
Tools & Technologies
AWS Batch Lambda
Amazon SQS & SNS
ElastiCache
AWS Athena
CloudTrail
DynamoDB
Cloudwatch
Oracle
ECS
Ansible
Amazon S3
Github
Amazon S3
Jenkins
Redshift
Docker
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