I built V.E.N.O.M – A Python CLI tool that uses NetworkX graphs to triage SQL data anomalies (Looking for Beta Testers!)
Hey guys, I’m a solo developer working on an open-source Python CLI tool called V.E.N.O.M designed to audit and triage data integrity anomalies in SQL dumps and data files. Many database engineers, analysts, and backend developers run into structural bottlenecks like deep circular dependencies or extreme data skew. I wanted a fast way to visually and structurally audit these relationships without writing a million custom validation queries
How it works:
Graph Compilation: It parses your .sql or data dump file.
Node Mapping: It maps rows/keys to a networkx graph structure.
Anomaly Analysis: It runs graph traversal algorithms to flag: • Circular Patterns: Tracing row-level cyclic dependencies. • Extreme Fan-In / Fan-Out: Spotting highly concentrated nodes or bottleneck points. • Orphaned Keys: Detecting broken pointers and disconnected subgraphs.
Markdown Reporting: Outputs a clean .md report detailing the exact evidence and flagged nodes.
Where I need help:
I’m looking for anyone dealing with messy data or complex database schemas to run it against their test environments and try to break it. I’d love your feedback on CLI usability, performance bottlenecks, and accuracy. • Code/Repository: https://github.com/shadow-fintech/VENOM Let me know what you think or if you run into any performance walls with massive datasets!.
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