How to Export Wayback Machine Snapshot Data to Spreadsheets
The Wayback Machine gives you a window into the past - but what if you want a table? Something sortable, filterable, exportable. Something you can chart, process, or load into a data pipeline.
That’s where exporting snapshot data into spreadsheets comes in.
Whether you're analyzing the timeline of a single URL, comparing thousands of archived pages, or prepping training data for machine learning, a clean CSV or Excel file can turn archive chaos into clarity.
Here’s how to extract that data - and why it might be more useful than the pages themselves.
Start with the Right Tool - Use the CDX API
To get structured data from archive.org, you’ll want to start with the CDX API. It’s a public endpoint that returns snapshot metadata: timestamp, URL, HTTP status code, MIME type, and more.
A simple query like:
gives you a downloadable CSV of all archived snapshots for a domain. It’s raw, but it opens perfectly in Excel or any spreadsheet editor.
You can filter by year, collapse duplicates, or limit by MIME type. It’s a fast way to turn Wayback history into a dataset you can explore.
This is the same foundational approach we recommend when building smarter OSINT workflows - as we explored in our article on AI-enhanced open-source intelligence. Structured data is the bridge between browsing and analysis.
Need Bulk? Use Smartial WScanner
For larger domains or bulk analysis, Smartial’s WScanner tool is your friend. Enter a domain, pick a year (or not), and hit scan. You’ll get a full list of archived URLs, grouped by year and sorted cleanly.
The best part? You can copy and paste the entire result table into a spreadsheet with proper formatting. Every row becomes a URL, every cell a point of reference. Use it to:
Audit a domain’s growth over time
Track when specific pages were added or removed
Estimate how much of a site was crawled each year
It’s the kind of structured timeline that makes training datasets or forensic timelines much easier to build - especially when you’re prepping archived content for labeling or NLP work, like we detailed in this article on using Wayback data for machine learning.
Add Your Own Fields for Analysis
Once you have the raw data in a spreadsheet, it becomes a canvas.
You can add:
A “status” column to flag broken or redirected links
A “content type” column based on MIME
A “category” tag for manual classification
Timestamps converted to human-readable dates
This structure is perfect for team audits, research reports, or input to other systems like keyword trackers, language models, or OSINT dashboards.
Don’t Forget Encoding and Format Cleanliness
When working with CSV files from the CDX API or WScanner output, make sure your spreadsheet software preserves UTF-8 encoding. Archive.org data occasionally contains special characters or percent-encoded URLs that can break when opened in older Excel versions.
If in doubt, import as plain text and manually define columns.
What This Enables Long-Term
Exported snapshot data isn’t just for record-keeping. It supports:
Longitudinal content analysis
Content decay studies
Domain flip tracking
Broken link audits
Automated retraining of language models on time-based slices
We’ve seen researchers build entire ML corpora from archive.org exports. Others use them to reconstruct deleted forums or monitor misinformation narratives over time.
It all starts with a spreadsheet.
Final Thoughts: The Past Works Better in Rows and Columns
Snapshots are great to look at. But rows, dates, and URLs are what let you work with the past.
Whether you’re auditing a site’s evolution, feeding a model, or building a forensic case, exporting snapshot data to CSV gives you structure. And structure is where meaning starts.
The pages may be old. But the patterns? Those are brand new - once you see them in the grid.