From Painful Test Cycles to Automated Data Masking
Digitail was struggling in a painful testing cycle against generic data that missed real-world edge cases, or copying production databases and risking exposure of sensitive pet parent and patient records. Every release carried either quality risk or compliance risk. Threading Clouds designed and automated a secure data masking pipeline on AWS—as part of its managed services—that gives Digitail's engineers production-realistic test environments on demand, with zero PII exposure and zero manual effort.
About Digitail
Founded in 2018, Digitail offers an all-in-one, cloud-based practice management software uniquely designed for veterinary clinics. The platform streamlines clinic operations, enhances patient care, and facilitates seamless communication between veterinarians and pet parents through its integrated pet parent app and AI Assistant.
What needed to change
Bugs That Only Production Could Catch
Testing against generic, synthetic data consistently missed real-world edge cases that only surfaced in production. The result: bugs reaching end customers, support complaints, and erosion of trust with veterinary clinics relying on Digitail for daily operations.
Compliance Risk on Every Database Copy
The only alternative to synthetic data was copying production databases directly, exposing sensitive pet parent PII and patient records to development environments. Every manual copy was a GDPR liability, and off-the-shelf masking tools lacked the flexibility to handle Digitail's schema complexity.
Developer Velocity Bottlenecked by Data
Preparing safe test databases was a manual, time-consuming process that blocked engineering cycles. Developers waited hours for usable environments, slowing release cadence at a time when Digitail needed to ship features fast to stay competitive.
Why Threading Clouds
Digitail had already tried the obvious path: standard AWS tools for database replication and masking. They hit a wall. DMS couldn't mask with the granularity GDPR demanded, and no off-the-shelf product handled their schema complexity without breaking indexes and referential integrity. Threading Clouds was brought in specifically because they could design a custom solution where packaged tools had failed. The combination of deep AWS database expertise with hands-on automation engineering meant Digitail got a single partner who owned the problem end-to-end, from replication through masking to automated delivery.
How we solved it
Threading Clouds built a fully automated pipeline that solves all three challenges in a single orchestrated flow. Production data is replicated via AWS DMS, then passed through custom masking scripts that strip sensitive PII while preserving the data relationships and edge cases that make test environments realistic. Database indexes and schema integrity are migrated separately to ensure masked copies perform like production, not like degraded snapshots. The masked output is compressed, stored in S3 with lifecycle policies that control costs automatically, and available for on-demand restoration into a consolidated Aurora cluster in the staging account. Jenkins orchestrates every step end-to-end. No manual intervention, no compliance gaps, no developer waiting.
What changed
The bugs-in-production problem disappeared. With test environments that mirror real-world data patterns, Digitail's QA cycles now catch edge cases before they reach end customers, eliminating the complaint pattern that had been eroding clinic trust. GDPR compliance risk dropped to zero across all non-production environments, with every database copy automatically masked before it leaves the pipeline. Test database preparation went from hours of manual work to a single Jenkins trigger, removing the bottleneck that had been holding back release cadence.
Results and benefits
Bugs Caught Before Customers Feel Them
Production-realistic test data surfaces the edge cases that synthetic data misses. Digitail's QA cycles now catch real-world issues before release, protecting clinic trust and reducing support burden.
Compliance on Autopilot
Every database copy is automatically masked before it leaves the pipeline. GDPR compliance shifted from a manual responsibility to a built-in guarantee, eliminating the risk of sensitive data reaching non-production environments.
Engineering Time Back on Features
Test database preparation went from hours of manual work to a single automated trigger. The time engineers used to spend wrangling data now goes toward building the features Digitail's veterinary customers are waiting for.
Continuing the partnership
With the automated masking pipeline operational, Digitail is positioned to extend production-realistic testing across new product lines and database schemas as their platform grows. Threading Clouds continues to evolve the masking rules alongside Digitail's data model and regulatory landscape, ensuring the solution stays ahead of both business requirements and compliance demands.
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