Stripe

Stripe Payout Export Sample

A Stripe payout CSV with 80 transactions including charges, refunds, disputes, and fee breakdowns across USD and EUR. Timestamps are in raw UTC format. Use this to test the Stripe Payout Formatter and validate QuickBooks-compatible output.

This dummy dataset simulates a standard export from Stripe, containing realistic yet fully anonymized records that mirror the structure and common data quality issues found in real production environments. The CSV file includes typical problems such as inconsistent formatting, missing values, duplicate entries, and non-standardized categorical fields.

It is designed to be used as a safe sandbox for testing data cleaning workflows directly in your browser — no uploads, no server round-trips, no third-party data exposure. Whether you are validating a transformation pipeline, benchmarking a cleaning tool, or simply exploring common data quality patterns, this sample provides a representative starting point without risking any sensitive business data.

Data Schema

Column NameData TypeDescription
idstringUnique record identifier
created_attimestampRecord creation date and time
statusstringCurrent status of the record

Recommended Tools

Related Workflows

How to Anonymize Customer Data Before Sharing with ChatGPT or Developers

A step-by-step SOP for stripping PII (names, emails, phone numbers, physical addresses) from production datasets while preserving the statistical shape of the data. Includes guidance on which columns to mask, which to drop, and how to verify the output is truly de-identified.

How to Clean Apollo Exported Leads Before Importing into Cold Email Software

Apollo.io exports contain duplicate contacts, generic role-based emails (info@, support@), invalid domains, and inconsistent company name formatting. This workflow shows how to clean all of these issues in one pass to keep your sender reputation above 95% deliverability.

How to Convert Stripe Payout Reports into QuickBooks Import Format

Stripe's payout CSV has 15+ columns that don't map to QuickBooks' expected schema. This guide shows how to separate gross revenue from processing fees, reformat dates to MM/DD/YYYY, and produce a clean CSV that QuickBooks accepts without manual reconciliation.

Clean WooCommerce Exports for Financial System Import

WooCommerce's native CSV export is notoriously hostile to accounting workflows. Product descriptions in the post_content column arrive wrapped in raw HTML tags and shortcodes like [woocommerce_reviews]. Custom meta fields (e.g., _regular_price, _sku) frequently shift columns when plugins inject hidden postmeta, and the post_date column alternates between Y-m-d H:i:s and Unix timestamps depending on your WordPress version. This SOP strips HTML remnants via regex, realigns displaced meta columns, and standardizes all date fields to ISO 8601 so QuickBooks and Xero accept the import without throwing schema validation errors.

Sample Datasets

Amazon Settlement Report Sample Data

A representative Amazon Seller Central settlement report with 150 transactions covering FBA fees, commissions, reimbursements, and refunds across multiple marketplaces. Use this to test the Amazon Settlement Cleaner and see how 40+ raw columns collapse into a clean profit summary.

Facebook Ads Campaign Report Sample

A Facebook Ads Manager export with 100 rows spanning 3 campaigns, 12 ad sets, and key metrics (impressions, clicks, spend, conversions, ROAS). Includes intentionally messy rows with zero-spend days and broken attribution windows for testing the Ads ROAS Pivot tool.

Apollo.io B2B Leads Export Sample

An Apollo.io lead list export with 300 contacts including duplicate emails, role-based addresses (info@, admin@), invalid domains, and inconsistent company name casing. Perfect for testing the Apollo Leads Cleaner and CSV Deduplicator on a realistic dirty dataset.

Zendesk Tickets Export Sample Dataset

This dataset contains 5,000 synthetic Zendesk support tickets exported in standard CSV format, featuring exact production columns: Ticket ID, Subject, Description, Status, Priority, Requester, Assignee, and Created. It simulates a realistic helpdesk environment with a mix of open, pending, and resolved tickets across multiple support queues. The critical Zendesk export quirk is that Description and Comments fields contain embedded CRLF (\r\n) line breaks from customer emails and multi-paragraph replies. Naive CSV parsers that don't respect RFC 4180 quoting rules will split a single ticket row into multiple invalid records at every newline, corrupting the dataset structure. The intentionally injected dirty data inventory includes: embedded CRLF newlines in 1,200 Description cells, 340 rows with Assignee set to null (unassigned tickets), and UTF-8 characters (é, ñ, ü) in Requester names without a BOM header to test encoding fallbacks. After cleaning and proper quote-handling, this yields exactly 5,000 valid ticket records. Ideal for: ETL pipeline testing, CSV parser validation, DuckDB text-loading demos, and NLP preprocessing on support logs. Load this file into the format-cleaner tool to strip CRLF artifacts and normalize the text fields.