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Import and Export

Omnismith allows you to import and export entity datasets using CSV (Comma-Separated Values) files. This makes it easy to migrate data from spreadsheets (Excel, Google Sheets) or export records for external analysis.

1. Exporting Entities

Section titled “1. Exporting Entities”

Exporting exports the current entity dataset based on your active template, search text, and filter chips.

Step-by-Step Export:

Section titled “Step-by-Step Export:”
  1. Navigate to Entity Operations → Entities and choose your Template.
  2. Apply any search or filter conditions if you only want a subset of data.
  3. Click the Export CSV button in the table toolbar.
  4. The downloaded CSV file contains:
    • Column headers matching the exact attribute names.
    • All matching entity rows.

[!TIP] Pro Tip: Even if a template has 0 entities, export a CSV file first! The exported file gives you an exact pre-formatted template header file ready for bulk data entry.

2. Importing Entities

Section titled “2. Importing Entities”
  1. Open your CSV file in Excel, Google Sheets, or a text editor.
  2. Ensure column headers match your template attribute names.
  3. On the Template Entity screen in Omnismith, click Import CSV.
  4. Drag and drop your file into the import modal.
  5. Review field mappings and click Run Import.

3. Import Results & Validation Summary

Section titled “3. Import Results & Validation Summary”

Once the import completes, Omnismith provides a detailed execution summary:

  • Created: Number of new entities created.
  • Updated: Existing entities matched by key and updated.
  • Skipped / Failed: Count of invalid rows.
  • Row-Level Error Breakdown: Lists exact row numbers and validation errors (e.g. Row 14: Invalid List option ‘Pending’ for Status or Row 22: Expected number for CPU Usage).

Import result summary dialog with row-level error breakdown

💡 Developer Architecture Note

Section titled “💡 Developer Architecture Note”

[!NOTE] Asynchronous Bulk Processing: Imports run through non-blocking asynchronous queue workers. Large datasets (thousands of rows) are processed in micro-batches with transaction isolation, guaranteeing that invalid rows do not corrupt existing records.

Next Steps

Section titled “Next Steps”

[!TIP]