START WITH ONE USEFUL FILE
Make your first sample export.
Inspect the same fictional customer dataset in four formats, verify what each file contains, and outline the contract for your own export workflow.
1. Choose the receiving workflow
Begin with the person or application that will use the output. A support analyst may need an editable workbook, while a background process needs predictable structured values. A customer reviewing a finished statement may prefer a PDF. Write down the next action before selecting a file extension.
For this walkthrough, the dataset is a tiny customer overview: three fictional customers with an identifier, name, plan label, and monthly amount in USD. These values are examples for inspection, not available ExportAPI.com subscription plans.
2. Open a real sample
Visit the examples and downloads collection and choose a format. Open the CSV in a text editor and, separately, in a spreadsheet. Inspect the JSON as structured text. Open the XLSX workbook in a compatible spreadsheet and the PDF in a document viewer. Every download is an actual file that you can save and inspect locally.
Check the three customer identifiers, the header names, and the monthly amounts. Compare how the same values appear in a plain table, a structured document, a workbook, and a finished report. The file presentation changes; the intended dataset stays the same.
3. Describe the data contract
List the selected fields in their intended order. Explain each identifier, the currency, the treatment of missing values, and which values represent text rather than quantities. For your own application, add the reporting window, selected account, schema version, and source cutoff where they matter.
Decide what the caller should see when no records match. An empty but correctly structured file may be useful; a failed job communicates a different outcome. Avoid making downstream users infer that distinction from a blank screen or a missing download.
4. Validate the handoff
Read the finished artifact back with the receiving tool and compare it with the expected dataset. Confirm record counts, required fields, representative values, and any totals used by the recipient. Test long text and values with punctuation before treating a clean-looking sample as proof that every input is handled.
Then write down access rules and the file lifetime. Public fictional samples can be delivered directly. Real customer records require an access and retention policy appropriate to their purpose. Use the security guide to examine those decisions.
5. Plan the production workflow
For a small bounded dataset, generation may fit within one request. For larger work, use a durable job with a clear status and a separately retrievable artifact. Keep the request identity stable across retries and distinguish generation from delivery.
Continue to illustrative API patterns to study a request and status contract. The asynchronous bulk exports guide explains the wider lifecycle, including consistency, worker failures, and completion checks.
A good place to start
Make your first export.
Four real sample files. One fictional dataset. A clear next step for your project.