Shopify Standard Order Export CSV Sample
A realistic Shopify order export with 200 rows including multi-line items, refunded orders, and discount code fields. Includes both a 'dirty' version (raw export with duplicates and nested line items) and a 'clean' version showing the expected output after normalization. Use this to test the Shopify Normalizer and CSV Merger tools.
This dummy dataset simulates a standard export from Shopify, 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 Name | Data Type | Description |
|---|---|---|
| id | string | Unique record identifier |
| created_at | timestamp | Record creation date and time |
| status | string | Current status of the record |
Recommended Tools
CSV Merger
Drag in 50 CSV files with mismatched columns and merge them into one unified table.
CSV Deduplicator
Remove duplicate rows based on single or multiple columns with fuzzy matching.
Shopify Order Normalizer
Collapse Shopify's multi-line order export into one row per order with aggregated totals.
Related Workflows
How to Merge 50 Shopify Order CSVs Without Crashing Excel
Shopify limits order exports to specific date ranges, so sellers with years of data end up with dozens of separate CSV files. This guide walks through merging them into a single master file while handling duplicate order IDs and inconsistent column orders across exports.
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.
Reconcile Facebook Ads Spend with Shopify Revenue
Attributing Facebook Ads spend to actual Shopify revenue is a nightmare because Shopify exports Name (e.g., #1042) and Order ID, but completely strips UTM parameters or Campaign IDs from the standard order CSV. Meanwhile, your Facebook Ads export groups spend by Campaign ID and Ad Set Name. You cannot directly VLOOKUP these two datasets. This workflow walks you through extracting UTM tags from Shopify's Note or Tags fields, parsing them with regex, and executing a memory-safe local VLOOKUP to bridge ad spend and realized revenue without touching a cloud server.
Sanitize Email Lists Before Klaviyo Import
Importing a dirty contact list into Klaviyo is the fastest way to torch your sending domain's reputation. Exported suppression lists from legacy ESPs are riddled with invisible zero-width spaces, malformed addresses like user@@domain.com, and role-based emails (info@, admin@) that trigger spam traps. If your hard bounce rate exceeds 2% on a single campaign, Klaviyo will throttle your account and your transactional emails start landing in Gmail's Promotions tab. This workflow cross-references your prospect list against historical bounce logs, strips non-printable Unicode characters, and validates RFC 5322 email formatting entirely in your browser.
Sample Datasets
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.
HubSpot Contacts Export Sample Dataset
This dataset contains 8,500 synthetic HubSpot contact records with exact production export columns: First Name, Last Name, Email, Lifecycle Stage, Associated Company, Lead Status, and HubSpot Score. It represents a mid-market B2B SaaS database with contacts spanning multiple lifecycle stages from subscriber to customer. The HubSpot-specific quirk is that multi-checkbox custom properties and system fields like Lead Status are exported as semicolon-separated strings (e.g., New;Qualified;SQL) rather than comma-separated values. Data engineers unfamiliar with this behavior often split these fields incorrectly during ETL, destroying the multi-select taxonomy. The dirty data inventory includes: semicolon-delimited strings in 2,100 Lead Status cells, trailing whitespace in 890 Email addresses that would cause duplicate contact creation on re-import, and 420 rows where Lifecycle Stage is completely blank due to API-synced contacts bypassing the form submission flow. After deduplication and whitespace trimming, this yields 8,340 unique, import-ready contacts. Ideal for: CRM migration testing, HubSpot import validation, data warehouse schema design, and reverse-ETL dry runs. Run this dataset through the csv-deduplicator tool to identify and merge the whitespace-polluted email duplicates.