Upload Frequently Asked Questions

Upload Basics:

1. How do I create new records via upload?

   • To create a new record, reviewAllovue Upload Options and download the appropriate CSV. All uploads will download a copy of the existing data in Allovue. New records can be added by adding a new row to your CSV spreadsheet. Ensure that your CSV is saved as a CSV UTF-8 and that all required fields are included. Each upload will require you to map the fields in your spreadsheet to the corresponding fields in Allovue. Once completed, a flash message will display indicating how many new records were added.
     

2. How do I modify existing records via upload?

   • To modify a record, download the appropriate upload template. Allovue’s upload templates will download with a copy of the existing data in Allovue. All uploads are matched by a unique constraint, which can be found for all upload types here. Modify the rows that you wish to change and proceed with the upload process. Each upload will require you to map the fields in your spreadsheet to the corresponding fields in Allovue. A flash message will display indicating how many record were added or modified.
     

3. Can I delete records via uploads?

   • The uploads in Allovue do not support a mass deletion process. If you need data deleted from your account in mass, please reach out to support@powerschool.com, and our team will follow up with you.

Common Troubleshooting

If you attempt to upload data and some rows are ignored and can't be successfully uploaded, but you can't figure out why, first check to see what they have in common.

1. Do they share a name or code?

   1. The code/name needs to be unique. Review the unique constraints section of our uploads. Even if you don't already have a "supplies" bucket, you may have repeated the "supplies" bucket in the spreadsheet, and they will conflict.
      

2. How are your date values formatted?

   1. Date values should always be in YYYY-MM-DD format.
      

3. Check for Blanks!

   1. Blank column content will throw errors. For example, if one part of the scope (segment type, operator, segment code) for a bucket or budget upload is blank, the upload cannot be processed. Ensure that you have all the required data elements and review the field descriptions found on each upload page.
      

4. How is your file saved?

   1. When saving from Excel, make sure to save the file as CSV UTF-8 format. Files saved as an xls format will not process.
      

Additional Validation Errors:

The system performs validation in layers, starting with basic file structure and progressing to content validation. Here are some common validations that we use in our upload process. Review each upload template and the unique constraints.

1. File Structure Validation

• Empty File Check: Ensures the uploaded file contains data.

  • Error: "You uploaded an empty file."

• CSV Format Validation: Verifies the file is properly formatted as CSV.

  • Error: "Your file is invalid, please try a different one" (for corrupted files)

• Structural Errors: Catches CSV parsing issues like unmatched quotes

  • Error: Shows specific parsing errors like "Errant quote"

2. Column Structure Validation

• Minimum Column Count: Ensures enough columns exist for required fields

  • Example: If uploading Job Types requires 3 fields (name, code, sort_order), the CSV must have at least 3 columns

  • Error: "Not enough columns"

• Text Encoding: Validates all text is properly UTF-8 encoded

  • Error: "Could not read row #X. When saving from Excel, make sure to save the file as CSV UTF-8 format."

3. Header Validation

During the "Map Fields" step, additional validation occurs:

• Duplicate Field Mapping: Prevents the same database field from being mapped to multiple CSV columns

  • Error: "Duplicate fields selected"

• Required Fields Present: Ensures all required fields are mapped to CSV columns

  • Example: For employees, code, first_name, last_name, and start_date are required

  • Error: "Missing required fields: first_name, start_date"

4. Unique Constraint Validation

The system identifies potential duplicate records based on unique business rules:

Single Field Constraints:

• Employee Code: Each employee must have a unique identifier

  • Example: Two rows with employee code "EMP001" would be flagged

  • Constraint field: [:code]

Multiple Field Constraints:

• Job Type: Both name and code must be unique independently

  • Example: Two job types can't share the same name, even with different codes

  • Constraint fields: [[:name], [:code]] (two separate constraints)

Composite Constraints:

• Employee Benefits: The combination of employee code, benefit type, and start date must be unique

  • Example: An employee can't have two identical health benefits starting on the same date

  • Constraint fields: [:employee_code, :benefit_type, :start_date]

5. Field-Level Data Validation Examples

The system validates each field based on its data type and business rules:

Text Fields:

• Required vs Optional: Some fields like first_name are required, others like middle_name are optional

• Character Limits: Names are typically limited to reasonable lengths.

• Safe Characters: Prevents potentially harmful characters in text fields.

Date Fields:

• Format Validation: Dates must be in YYYY-MM-DD format

  • Valid: "2023-01-15"

  • Invalid: "1/15/2023" or "January 15, 2023"

• Logic Validation: Start dates must be before end dates

  • Error: "Start date must be earlier than end date"

Numeric Fields:

• Decimal Fields: Amounts can have decimal places

  • Valid: "1000.50" or "0.15"

  • Invalid: "1,000.50" (commas not allowed)

Enumerated Fields:

• Pay Basis: Must be one of the specific allowed values

  • Valid: "percent" or "flat"

  • Invalid: "percentage" or "fixed"