⚡ A schema-based data mapper

A handy study of Mongoose Schema including schema and model creation, using virtuals and instance methods, relationship types, embedded models (subdocuments), and normalization implementation.

A privacy-first data integration and schema normalisation pipeline for fragmented identity datasets. Features heuristic parsing to resolve inconsistent CSV/vCard headers, bitwise fuzzy search for fault-tolerant querying, chunked memory management for massive record lists, and a zero-dependency local architecture.

A privacy-first ETL and data processing engine for massive, unstructured communication archives. Features custom Regex parsing pipelines, index-based memory chunking for instant dataset traversal, context-aware metadata extraction, and a zero-dependency local architecture optimised for strict data sovereignty.

Python pipeline that normalizes heterogeneous log sources (Sysmon, CloudTrail) into a unified OCSF schema — reducing cross-source query complexity by 40%.