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The Global Domain Dataset: What You Can Learn from 300M+ Domains

A global domain dataset is a snapshot of the internet.

webatla · 2026-03-15 15:42 · 3 claps · 1.3 min read
#domain-names #dns #whois #seo #data-science
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Wiki topics: ML · Machine Learning SEO · SEO & SEM 🔬 · Science · General

The Global Domain Dataset: What You Can Learn from 300M+ Domains

A global domain dataset is a snapshot of the internet.

With 300M+ domains, you can derive domain intelligence signals that drive SEO, marketing, security, and product decisions.

At Webatla, the Global Domain Database brings domain data into one place, including domains, DNS, RDAP & WHOIS, web technologies, TLD taxonomy, and more.

1) Hosting + network intelligence

Every domain tells you something about hosting providers, DNS configurations, and infrastructure.

With country and TLD metadata, you can map patterns like:

  • Which regions dominate a niche
  • Which TLDs are growing fastest
  • Where major hosts concentrate their domains

Try it by exploring TLD-level structure: Webatla TLD overview

2) SEO & keyword discovery from real inventories

Search tools show what users search for. Domain datasets show what gets registered and built.

Use the dataset to:

  • find keyword-rich domains and brand ideas
  • validate demand across countries and TLDs
  • identify backlink-friendly, reputable domains via ranking metrics

3) Market intelligence (who’s building what)

New domains often mean new products.

Track waves in verticals (AI, ecommerce, fintech) and get competitive signals by watching registrations and active websites.

Start here: Active Domains dataset

4) Security: RDAP & WHOIS enrichment

RDAP & WHOIS fields help analysts and security teams with:

  • enrichment and pivoting
  • ownership clustering
  • policy compliance checks

Get structured records: RDAP & WHOIS dataset

5) Tech stack adoption

If you want to measure technology adoption at scale, look at what websites deploy across millions of domains:

  • CDNs and hosts
  • CMS and frameworks
  • analytics and tag managers

Use the technology dataset for stack-level trends: Web Technologies dataset

6) Build better models

Domains are features.

Use domain dataset signals to improve:

  • risk scoring
  • lead enrichment
  • de-duplication and normalization

A clean, normalized domain dataset is a reliable feature store for ML and analytics.

Where to start

If you only need one dataset, start with the active inventory: Active Domains dataset

You can compare packages and checkout here: Pricing

Need details on delivery format, updates, and licensing? FAQ


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