👤 For process owners and people preparing data for import
🔐 Importer App available on all plans, subject to pipe permissions
🎯 To bring existing data into a process with clear preparation, validation, and retry criteria
A Procurement spreadsheet may contain suppliers, open purchase requests, and requests that are already closed. Treating them as a single set makes it harder to decide what belongs in the workflow and whether the team has the information it needs to start working.
By the end of this article, you will be able to choose the data and its destination, validate a sample, and prepare another import without repeating entries you have already confirmed. Start by defining what the information means before uploading the file.
What you will learn:
Define what each row represents
Before choosing a file, complete this sentence: “Each row in this dataset represents...”. A purchase request is work that needs to be reviewed and completed. A supplier record may provide reference information for several requests.
That distinction helps you choose a destination. Use a pipe to organize work that moves through stages. Consider a database for stable reference information, taking its maintenance needs into account. A supplier registration that requires review, approval, and follow up may also need its own process.
To revisit that choice, read How to structure your first process in Pipefy, in Portuguese. Define what the team will track before turning spreadsheet rows into process entries.
The Importer App creates a new card or record for each row and lets you match columns to destination fields. To import into a database through this route, the database must already exist and be connected to the pipe.
Review the approach before scaling up. For large datasets or processes with connection fields, agree on the approach with the process administrator and technical team. These situations call for planning around record relationships, monitoring, and error handling. Volume alone does not establish a universal threshold for that decision.
A useful question: does this information need to move through stages, or will people consult it while working? In the Procurement example, separating a purchase request from the supplier record helps establish what belongs in each destination.
If you are still defining the role of cards and fields, take Fundamentos do Pipefy, in Portuguese, on Pipefy Academy, before preparing the import. Access to lessons requires an Academy account.
Prepare the data the team needs to work
Agree on the scope with the person responsible for the source data. Does the team need to start with open requests? Are older records needed for a specific reference purpose? These answers help determine what to bring over and what to retain as source history.
Keep an original copy and prepare a working file. Corrections and exclusions for this import will then leave the reference used for validation intact.
For each column, record its meaning, its destination field, and how you will check the result. A column named “Date” could mean issue date, due date, or delivery date. A valid format cannot resolve a mismatch in meaning.
If the next person needs a piece of information to review the request, resolve any missing data with the source owner before importing. Avoid filling gaps with invented values or removing process requirements just to complete the upload.
The input file must be XLSX, without merged cells, filters, or conditional formatting. Review field formats too: dates follow the language configured in Pipefy; currency values need numeric formatting without currency symbols or thousands separators.
Plan for attachments separately. The Importer App does not transfer file attachments from the spreadsheet. If documents are needed to review a request, establish how they will be available before releasing the work to the team.
See Preparing spreadsheets for the Importer App for formatting guidance. Once you have defined the scope and mapping, How to import data from Excel into Pipefy covers execution.
Validate a sample before importing more
Choose a small set of rows that covers meaningful variations in the source data. A sample containing only similar records may leave out the values that need the closest attention.
For a Procurement example, include requests with different dates and amounts. A date such as November 23, 2026 helps reveal how day and month are interpreted; an amount with cents helps you check that the value retains its precision.
After importing the sample, compare the source data with the new records and verify that:
- Each entry represents the expected request or reference record.
- Each piece of information is in the field that preserves its meaning.
- Dates, amounts, and codes retain the source values, including zeros that belong to identifiers.
- The person receiving the work can find what they need to continue.
Upload completion and operational validation are separate checks. Beyond reviewing the data, ask someone involved in the routine to examine a sample request. This helps identify information that still needs attention before the larger import.
Record what you checked and which sample rows already exist in the destination. Account for those entries when preparing the next import. A sample helps you decide whether to proceed, but it does not replace validation of the full dataset.
Investigate the result before trying again
Start with what you can observe. A rejected file, an unexpected field value, and an import that is still running require different checks.
The file was not accepted
Check the XLSX format and the preparation requirements in the Help Center. If the source was a CSV, prepare and save it in the required format before uploading. Use the message you received to guide your investigation rather than assuming every error has the same cause.
The imported data differs from the source
Compare a source row with its corresponding record. Check the column matched to the field first, then the data format. If a due date was matched to an issue date field, changing how the cell looks will not resolve the mismatch.
Some entries already appear in the destination
Reconcile the submitted data with the records that already exist. Separate confirmed entries from those still requiring attention before preparing another import. Do not treat resubmitting the entire spreadsheet as a way to update existing records.
You cannot yet confirm that the import has finished
Check the Importer App history and completion email before trying again. The guide How to import data from a spreadsheet, in Portuguese covers monitoring. If the status remains unclear, gather the pipe details, submission time, and message you observed before asking Support for help.
For further diagnosis, see Common CSV and Excel import errors and how to investigate them, in Portuguese. Although that article's title mentions CSV, the Importer App input file is XLSX.
Before moving on, confirm that you understand:
☐ What each row represents and why you chose its destination.
☐ Which source information belongs in each field needed for the work.
☐ How to validate a sample and recognize data that is ready to use.
☐ Why you should compare source and destination before preparing another import.
Ready to bring a validated sample into your process? Continue with How to import data from Excel into Pipefy and apply your preparation decisions during execution.
