add-csv-importer

To build a Python CSV importer with validation and deduplication, you can scaffold a script that loads, normalizes, and deduplicates rows by a specific key before writing to the database. The generated script includes validation hooks and supports skipping or updating duplicates.

The best way to preview a bulk CSV import is to use a dry-run flag. Running the import script with a dry run executes the validation and deduplication logic without committing changes, generating a results CSV that reports whether rows would be created, updated, skipped, or encountered an error.

To handle errors during a bulk CSV import without stopping the process, the script uses per-row exception handling with rollback. This isolates bad rows so they fail individually while allowing the rest of the batch to process successfully, and records the specific errors in a reporting CSV.

Yes, this CSV import scaffolder supports updating existing records instead of skipping duplicates. You can use an update-existing command-line flag to trigger upsert behavior, which updates the database record matching the deduplication key with the new CSV row data.

To resolve foreign keys when importing CSV data, the importer script includes optional foreign-key resolution capabilities during the row normalization phase. This maps CSV input values to their corresponding database identifiers before safely writing the entity records to the CRM.

Generating a deterministic CSV import script for DotMac Omni CRM patterns requires providing the target entity and CSV column mapping. The scaffolder outputs a ready-to-run Python script with command-line arguments, validation rules, and per-row error isolation matching the required CRM structure.