Insights
Understanding systems from evidence.
Practical, first-party writing on the problems Coharyn is built for — unknown databases, cross-system consistency, and migration that preserves meaning.
Evidence-Backed Discovery and Deterministic Data Assurance
A four-stage model for trustworthy data intelligence: learn structure from evidence, prove it, compile the validated knowledge into deterministic checks, and execute them without continuous model inference, relearning only when drift demands it.
8 min readTransformationSource-to-Target Migration Mapping That Survives Contact With Reality
A durable migration is built on explicit semantic mappings: enum and lifecycle translation, one-to-many and many-to-one restructuring, derived fields, dependency-aware load order, honest blockers, and rehearsal before the real run.
8 min readTransformationWhy Migration Row Counts Are Not Enough
Matching row counts between source and target is the weakest possible migration check. Real assurance means reconciling states, enums, derived fields, and balances, where the counts pass but the meaning quietly fails.
7 min readCross-system consistencyWhen Your CRM and Billing Disagree: Pending or Broken?
The CRM says premium, billing says standard. Most of the time that gap is a change still in flight. Here is how to tell legitimate pending states from genuine breakage using learned propagation windows.
6 min readCross-system consistencyCross-System Data Reconciliation Without False Alarms
Reconciling data across systems means resolving identity across boundaries, telling eventual consistency apart from real faults using learned propagation windows, and localising the boundary where truth actually diverges.
8 min readUnknown systemsDiscovering Real Relationships When Foreign Keys Are Missing
Enterprise schemas are full of relationships the database never declared. Here is how to find the real ones from value containment and cardinality, why naming similarity is not enough, and how orphans tell you the truth.
7 min readUnknown systemsHow to Understand an Undocumented Enterprise Database Safely
A metadata-first method for making sense of a legacy database you inherited, without heavy scans or guesswork: read the catalog, use existing statistics, sample within bounds, and let evidence drive the model.
7 min read