WipUpRev — High-Throughput Blueprint Revision Migration Service
2025A parallelized data transformation pipeline that migrated hundreds of thousands of aerospace inspection records from one drawing revision to another using a graph-based specification mapping algorithm. The project protected millions of dollars of at-risk inventory and a critical customer relationship.
Motivation
A top aerospace customer mandated that all parts conform to a specific drawing revision by a hard deadline. The business unit had millions of dollars of stock inspected to older revisions. Failure to meet the requirement would have seriously damaged the company's most important customer relationship.
Challenge
The naive approach — a lookup table mapping old features to new ones — broke down almost immediately. Bubble numbers changed between revisions, measurement types changed, some features had no equivalent in the new revision, some previously in-tolerance parts became out-of-tolerance on the new rev, and some new features had no historical data at all. No single heuristic could cover the space. Throughput was also a hard constraint: hundreds of thousands of parts needed to be processed under a tight deadline.
Solution
A graph-based specification map was designed in close collaboration with a senior Quality Engineer: each feature at each revision is a node, and edges encode the transformation rules between them. A breadth-first walk of the graph from any starting feature determines the exact sequence of changes needed to reach every corresponding feature on the target revision, including cases where features split, merge, or disappear. The processing pipeline was built on C# TPL Dataflow for parallel execution, with IAsyncEnumerable feeding parts in asynchronously. Workers hit only production APIs with no direct database access, which meant the process could be scaled horizontally by spinning up additional instances — critical for hitting throughput targets on the initial run of over a hundred thousand parts.
Outcome
Launched on time. Millions of dollars of at-risk inventory was protected and the customer relationship was maintained. The scalable architecture supported the project's next phase with minimal rework.
- C#
- Graph algorithms (BFS)
- TPL Dataflow
- IAsyncEnumerable
- ASP.NET Core
- OpenTelemetry
- PowerBI integration
- CSV sharding
- Horizontal scaling
- Parallel pipeline architecture