validated-data-migration

Migrating mixed-format data (CSV, Excel, JSON, PDFs, VCF) into a clean SQLite database requires automated schema discovery, per-file validation, and deduplication. The migration process enforces strict provenance and auditability to ensure data quality during client onboarding.

Data provenance tracks the origin lineage of every record during data migration. It matters because it provides a full audit trail for source files, ensuring you can trace any row in the clean SQLite database back to its original legacy file for compliance and verification.

Handling data deduplication and conflicts when consolidating Excel and CSV files involves automated duplicate detection and merge rules. The system flags conflicting records and routes them to a human-review queue, ensuring accurate row-count reconciliation before finalizing the database.

Yes, you can migrate mixed-format data like PDFs and VCF files into SQLite automatically. The intake process detects file types and estimated rows, applies schema discovery, and validates each file individually to enforce a unified target schema with rejection tracking.

The best way to audit data quality when onboarding clients with inconsistent source files is to use a migration process that generates phase-driven reports and a human-review queue. This ensures strict auditability, source provenance, and row-count reconciliation before shipping.

Records are rejected during a data migration to SQLite when they fail per-file validation or violate the unified target schema. These rejections are tracked explicitly alongside merge rules and source provenance, allowing you to review data quality issues in a dedicated queue.