What Changed
A previously fragile synchronization process became a reliable and controlled system, with accurate data transfer and significantly reduced deployment risk.
Where They Started
Super Star Car Wash relied on a scheduled synchronization service to transfer store and fundraising data into an external platform. This data was essential for accurate reporting of fundraising performance across locations.
What Was Breaking
The system faced two key challenges. First, inconsistencies in the payload structure being sent to the external API resulted in inaccurate or incomplete data capture. Second, deployments were being made directly to production, introducing the risk of disrupting scheduled synchronization jobs and impacting daily operations.
How The Zig Fixed It
The Zig began by analyzing the integration layer and correcting the payload structure to ensure compatibility with the external API, restoring data accuracy at the source.
To address deployment risk, The Zig implemented a structured CI/CD pipeline using Azure DevOps. This included the introduction of staging and production deployment slots, allowing new builds to be deployed and validated in a staging environment before being promoted to production through controlled slot swaps.
This approach ensured that any issues could be identified and resolved prior to impacting live operations, while also enabling rollback if necessary.
What It Unlocked
The synchronization process became stable and predictable, ensuring that fundraising data was consistently and accurately reflected in the external platform.
At the same time, deployment workflows became significantly safer, allowing the team to release updates with confidence while minimizing the risk of operational disruption.
Where the Investment Went
Investment was focused on integration debugging, pipeline design, and infrastructure configuration to support staged deployments. Additional effort was allocated to validating synchronization behavior across environments.
What This Taught Us
Reliable integrations depend as much on deployment strategy as they do on correct data handling. Even well-functioning systems can introduce risk if changes are not deployed safely.
This project reinforced the importance of separating validation from production execution in systems that run on scheduled workflows.