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How DevOps Launchpad Transformed Aquatic Informatics' Environment with Fully Automated CI/CD Pipeline
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Aquatic Informatics
Services
DevOps Launchpad
Machine Learning OPs
Security and DevSecOpstics
Industry
Energy
Technologies
Cloud: AWS
Infrastructure as Code and Launchpad: Docker | Kubernetes | Python | .NET Framework | API Gateway
A mission-driven software company that organizes the world’s water data to make it accessible and useful. As the world’s largest water data management company with more than 1,500 customers in 60 countries, the client is unique in that it provides information technology solutions for all water.
The Project
Providing Multi-Techstack Solutions. This containerization project assisted the migration and struct of a single tennant enviroment to a multi- tenant enviroment.
Project Duration
The project duration was
six months.
Deliverables
Fully Automated end to end CI/CD (DevOps Launchpad with DevSecOps and ML Ops methodologies
The Challenge
The client had a mixed container in both Windows and Linux, which resulted in their multi-tenant environment not actually being mature enough to be multi-tenant.
Our Solution
Cloud Architect created a unique solution design to implement an a end to end CI/CD, which included security operations and fully automated quality assurance. This required us to have the application up and running in an EKS environment, securing the application using different methods of security, and deploying using Cloud Architects DevOps Launchpad.
This allowed the application to work in a mixed environment (Windows and Linux), and properly authenticate the users using Active Directory on a windows, and then a Mac.
DevOps
- A fully automated solution to compile, test and deploy your application to any cloud, anywhere.
SecOps/DevSecOps
- Security is first and foremost for any company or educational institution. We have the best SecOps Platform to start or mature your Security Operations and DevSecOps
Machine Learning
- A programming model that simplifies the process of defining and launching distributed machine learning and data pipeline
Value Delivered
Our model saved the client an additional 6 months of development time, resulting in a large labour and operations cost reduction.
In addition, by correcting the compatibility of their multi-tenant environment, it reduced their overhead cost of migration, provided optimized security and improved visibility into their application.
The flexibility of multi-tenant environments
Simplified Total Cost of Ownership
Enhanced security and optimized performance
Reduced application down time
The Result
1.Their Multi-Tenant Model is optimized
2. Project costs were reduced by 6 months
3. Improved visibility and security to the application
Technology Stack
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