Cybersecurity
Build a review queue from new domain lists using transparent keyword rules. Export matched domains for correlation with independent security signals.
Input: New domain lists
Explore the Cybersecurity Python workflow →Domain Data Use Cases & Python Examples
Explore domain data workflows for security, brand protection, SEO, investing, research, and AI agents. Follow practical Python examples using downloadable datasets.
All datasets are updated daily by 9:00 AM UTC.
Find a domain data workflow for your team
Every guide includes a dataset recommendation, runnable Python code, expected output, and interpretation limits.
Brand Protection
Search daily domain additions for exact brand mentions and single-character spelling variants. Create a transparent review list with Python.
Input: New domain lists
Explore the Brand Protection Python workflow →Domain Investors
Filter expired and removed domain lists for short names in a chosen extension. Export candidates for registration-status and history checks.
Input: Expired / removed domain lists
Explore the Domain Investors Python workflow →SEO
Build a domain discovery list from daily additions using your topic keywords. Export candidates for manual website and relevance checks.
Input: New domain lists
Explore the SEO Python workflow →Market Intelligence
Count topic-term matches in new domain lists. Compare a transparent naming signal across dated files without treating it as a count of new companies.
Input: New domain lists
Explore the Market Intelligence Python workflow →Data Engineering
Stream a compressed domain list into a local SQLite table using bounded batches and duplicate-safe inserts. Start a reproducible domain-data import.
Input: Current domain lists
Explore the Data Engineering Python workflow →AI Agents
Create a small JSON evidence bundle from domain data for an AI-assisted research workflow. Retain observation dates and sources for traceable answers.
Input: New domain lists
Explore the AI Agents Python workflow →Research
Compare two compressed domain snapshots using SQLite set operations. Measure observed additions and removals while recording input checksums.
Input: Zone snapshots and available history
Explore the Research Python workflow →Match the dataset to the question
| Question | Dataset | Interpretation |
|---|---|---|
| What names appeared in the collected data? | New domain lists | Observed additions, not confirmed registration timestamps. |
| What names disappeared? | Expired / removed lists | Observed removals, not guaranteed availability to register. |
| What names were present in a zone? | Current and historical zone lists | Dated snapshots of available coverage, not active-website inventories. |
Getting started with domain data
Are the Python examples free?
The example scripts are available to read and download here. Full input datasets require appropriate account access; compare plans and limits.
Do I need external Python packages?
No. These examples use Python 3.9 or later and its standard library. They process downloaded files locally without crawling websites or calling AI models.
How do I get historical input files?
Open a zone through the domain directory to see available snapshots. Pro includes historical access; archive depth varies by zone.
Put domain data to work
Inspect the datasets, choose access, or discuss requirements for your team.