How E-commerce Teams Process Millions of Rows of Sales Data Locally
6/12/2026
The E-commerce Data Problem
An e-commerce brand selling on Amazon, Shopify, and their own D2C site generates data across every platform. Each channel exports CSV files on its own schedule, in its own format, with its own naming conventions.
Amazon export might be 800K rows. Shopify export 400K rows. Google Ads daily export 50K rows. Excel cannot open the combined file. A cloud data warehouse takes days to set up. Python scripts break when column names change.
What a Typical E-commerce Export Looks Like
Amazon Seller Central exports inventory and sales reports. Shopify exports Orders with customer data. Google Ads exports Campaign Performance with impressions, clicks, and cost by date.
These three files have no common key column by default. Merging them requires column mapping, date normalization, and product ID reconciliation before any actual analysis can begin.
The Local Processing Workflow
DataOlllo handles the merge workflow without code:
- Open all three files in separate tabs simultaneously
- Map the common columns using the visual merge interface
- Identify the product key across platforms
- Merge all three into one master sales view by date and product
- Filter to the quarter or campaign you are analyzing
- Export the merged result for use in your reporting tool
This workflow runs entirely on your workstation. No data warehouse. No ETL pipeline. No cloud upload.
What You Can Analyze Without a Data Warehouse
With the merged view: revenue by channel and product, ROAS by campaign, inventory alignment between Amazon and Shopify, and customer overlap across channels.
Getting Started
Download DataOlllo and try opening your largest channel export file. The workflow for combining multi-channel e-commerce data takes under 10 minutes to set up and runs instantly on subsequent updates.
DataOlllo is free for personal use.