About the customer
This organisation is a leading financial services provider in the Netherlands, specialising in managing pension funds and investments for institutional clients.
They handle large-scale financial operations and oversee significant capital flows. With a mission to provide stable and efficient services, they rely on robust technology infrastructure to ensure smooth operations and maintain high levels of security.
The challenge
To meet new market demands and evolving compliance requirements, our client needed to launch a new big data project to remain competitive.
A key challenge was the high level of trust their end-clients placed in the system, meaning there was little room for error. Everything had to be done right the first time, as the consequences of failure were too significant. This created a need to balance rapid development with strict security and reliability standards.
Several key challenges arose:
- Developers lacked a reliable way to test the product locally, often depending on staging environments to validate stability.
- Business analysts had difficulty accessing system data, requiring developers to run manual queries.
- The development process lacked a reliable framework for running integration and end-to-end tests.
- There was no clear process for reporting and patching vulnerabilities, leaving gaps in security management.
- The infrastructure as code was so complex that no one felt comfortable making changes, leading to fragile and unstable releases.
The solution
To accelerate development while maintaining high security and reliability standards, we focused on empowering the developers by giving them the tools and confidence needed for seamless deployments.
First, we replaced the complex and monolithic AWS CDK codebase with a distributed GitOps solution, granting developers direct control over their applications. This simplified the infrastructure management process, making deployments more predictable and stable.
I now feel in control of my own application, it feels great to be able to spend the time precisely tweaking my deployment to meet my exact needs. Before I had to spend weeks waiting for an infrastructure engineer to be available... or longer.
Data Engineer
For business analysts, we exposed internal system data using AWS Athena and implemented AWS Lakehouse for added security, allowing them to query the system directly through PowerBI. This eliminated the need for developers to run manual queries, streamlining data access and analysis.
To enhance security, we integrated AWS Security Hub for continuous monitoring and reporting of security incidents, alongside Trivy for container vulnerability scanning. Automated reports were sent directly to Microsoft Teams, ensuring timely notifications and faster response times to vulnerabilities and compliance issues.
Known vulnerabilities in the project have been reduced from thousands to none; this is instrumental in allowing us to meet our operational readiness requirements.
Solution Architect
We also implemented Virtual Clusters, providing developers with actionable, testable releases that mimic production environments, ensuring the stability and reliability of their deployments.
Finally, we created a fully local Kubernetes environment, enabling developers to test changes in real-time as they code, greatly improving the speed and accuracy of development without reliance on staging environments. This shift gave developers full confidence over their deployments, fostering faster iterations with reduced risk.
Our impact
Initial Setup | With PolitePixels |
|---|---|
| Manual testing processes slowed down releases and increased risks. | Integrated automatic testing frameworks and 91% reduction in post-release bugs. |
| Business analysts relied on developers for data access, creating delays. | Business analysts gained direct secure access to system data, improving efficiency. |
| Complex infrastructure as code made changes risky and unreliable. | Simplified and reliable infrastructure changes using GitOps and Terraform. |
| Vulnerability reporting and patching were inconsistent, creating security gaps. | Enhanced security with automated vulnerability scanning and continuous monitoring. |
Key results
40% increase in deployment speed due to the shift from a monolithic codebase to a distributed GitOps solution, enabling developers to handle releases without needing an infrastructure engineer.
91% reduction in post-release bugs, with developers empowered to test in real-time and address issues earlier in the development cycle.
100% visibility for business analysts, with secure, direct access to system data via AWS Athena and PowerBI, reducing reliance on developers for queries.
Improved security posture, with automated vulnerability scanning and security monitoring through Trivy and AWS Security Hub, ensuring compliance and timely patching of vulnerabilities.
Greater developer confidence, thanks to virtual clusters and local Kubernetes environments, resulting in more stable and reliable testable releases.
