Merge Date-Range-Limited Shopify Exports Into One Master File
Shopify's admin export interface caps each download to a specific date range, and for stores with high order volume, you may need to pull data in monthly or quarterly chunks. The problem: if you export March 1-31 and then April 1-30, any order placed at 11:58 PM on March 31st that gets fulfilled or updated after midnight appears in both exports with different Fulfillment Status values. Simply concatenating the files creates duplicate Name entries that inflate revenue reports. This workflow merges multiple Shopify order CSVs sequentially, identifies overlapping orders by the Name column, and keeps only the most recent version of each order based on the Updated at timestamp. It also validates that column headers match across all files before merging, flagging any schema drift caused by Shopify silently adding new fields like Payment References between your January and June exports.
Why this matters?
A DTC supplement brand doing annual revenue reconciliation exported 14 months of orders in quarterly chunks. They merged the files in Excel using copy-paste, unknowingly creating 1,847 duplicate rows from orders that were updated across period boundaries. Their total reported revenue was inflated by $287,000, which flowed into their investor deck and triggered questions during due diligence for a Series A round. The duplicates were only caught when their accountant noticed that Name values like #4821 appeared twice with different Financial Status columns — one showing paid and the other partially_refunded. Manually identifying and resolving duplicates across 280,000 rows in Excel is error-prone, and Google Sheets rejects imports exceeding 10 million cells, which a year of Shopify data easily surpasses when accounting for all 80+ default columns.
The 3-Step Solution
Follow this streamlined workflow to transform your raw data export into a clean, analysis-ready dataset. Each step leverages our browser-based tools to ensure your sensitive data never leaves your device.
By following these three steps, you eliminate manual data wrangling, reduce human error, and maintain full GDPR compliance throughout the process.
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Sample Datasets
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.
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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.