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Fully Automated DevOps CI/CD pipeline for a software company

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Automation at its Best

How DevOps Launchpad Transformed Aquatic Informatics' Environment with Fully Automated CI/CD Pipeline

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Aquatic Informatics


DevOps Launchpad
Machine Learning OPs
Security and DevSecOpstics




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.


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.


  • A fully automated solution to compile, test and deploy your application to any cloud, anywhere.


  • 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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