How to run a dry-run import#

Audience: admin — imports are an administrative data-migration surface; the run takes commit-level intent.

Moving clients, contacts, tickets, or hardware assets from a legacy PSA or spreadsheet shouldn't be a blind leap of faith. In Marula, every import runs a dry-run preview first so you can inspect exactly what will be created, skipped, or merged before a single row is written to your database. Column mappings save per source system so your next import from the same tool maps itself automatically.

Prerequisites#

• Admin access to Imports (/imports).
• A CSV or JSON export from your previous tool. CSV files must include a header row so you can map columns in step 2.
• Your desired conflict rule (the default is skip-existing so existing records are not overwritten).

The three-step import habit#

"dry-run first (nothing is written), then commit as a batch. Field mappings save per source system so the next import maps itself."

1. Dry-run first: Preview every row and validation warning before committing.
2. Save your column mapping: Name the source system (for example, ConnectWise or Syncro) once and reuse the saved template on future batches.
3. Trace imported rows anytime: Every imported record carries an import actor badge in Provenance so you can always filter by what came from a batch.

1. Choose the record type and source system#

On Imports (/imports), complete step 1 of the guided flow:

• Pick the entity you are importing (clients, contacts, devices, tickets, Knowledge Base articles, etc.).
• Choose the conflict policy (skip-existing by default).
• Enter the source system name and pick an existing mapping template if you have imported from this tool before.

2. Upload your file and map columns#

Upload or paste your CSV/JSON file (including the header row). Match each column from your file to the corresponding Marula field. When you save, Marula stores this mapping under the source system name you chose in step 1.

3. Run the dry-run preview, then commit#

1. Click Dry-run. Marula validates every row and shows a summary of records to create, records to skip, and any formatting errors—without writing anything to the database.
2. Once the preview looks clean, click Commit to write the batch in one transaction. The run history panel keeps a permanent record of every batch.

4. Where imported records appear#

• Provenance (/provenance): Every field populated by an import is tagged with the import actor chip (alongside human and agent), making it easy to audit when and how a record arrived.
• Inventory (/devices): Imported hardware passes through the identity matcher and merge queue so overlapping serial numbers or MAC addresses don't create duplicate devices (see Your first inventory sync).

What you learned#

• Nothing is written to the database until you review the dry-run preview and click Commit.
• Column mappings are saved per source system so repeat imports take seconds.
• Every imported record keeps its batch provenance and import actor tag.