We extract company profiles, technographic stacks, revenue estimates, and employee directories from Datanyze. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Company Profiles objects from datanyze.com. All fields typed and schema-versioned.
"domain": "acmecorp.com", "company_name": "Acme Corporation", "industry": "Enterprise Software", "employee_count": "1000-5000", "revenue_estimate": "$100M-$500M", "hq_location": "San Francisco, CA"
| # | domain | company_name | industry | employee_count | revenue_estimate | year_founded |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technographics objects from datanyze.com. All fields typed and schema-versioned.
"domain": "acmecorp.com", "technology_name": "Marketo", "category": "Marketing Automation", "first_detected": "2021-03-15", "last_detected": "2026-05-10", "is_active": true
| # | domain | technology_name | category | sub_category | first_detected | last_detected |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Employee Directory objects from datanyze.com. All fields typed and schema-versioned.
"domain": "acmecorp.com", "employee_name": "Jane Doe", "job_title": "VP of Engineering", "department": "Engineering", "seniority_level": "VP", "location": "Seattle, WA"
| # | domain | employee_name | job_title | department | seniority_level | linkedin_profile |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Contact Data objects from datanyze.com. All fields typed and schema-versioned.
"domain": "acmecorp.com", "contact_name": "John Smith", "title": "Director of IT", "corporate_phone": "+1-555-019-8372", "hq_address": "123 Tech Lane", "city": "San Francisco"
| # | domain | contact_name | title | public_email | corporate_phone | hq_address |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Competitor Network objects from datanyze.com. All fields typed and schema-versioned.
"source_domain": "acmecorp.com", "competitor_domain": "globex.com", "competitor_name": "Globex Inc", "similarity_score": 88.5, "ranking_position": 2, "category_overlap": 14
| # | source_domain | competitor_domain | competitor_name | similarity_score | shared_technologies | ranking_position |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Datanyze scraper bypasses strict rate limits to extract comprehensive technographic profiles, company demographics, and employee directories. We manage the proxies, sessions, and schema parsing.
Extract revenue brackets, employee counts, industry classifications, and founding years for millions of domains.
Capture the full list of web technologies, SaaS tools, and infrastructure providers detected on a target domain.
Scrape public employee lists, job titles, seniority levels, and departmental classifications.
Track estimated revenue figures and employee growth trajectories over time.
Map market alternatives and competitor domains based on Datanyze similarity scores and category overlap.
Collect associated LinkedIn, Twitter, and Facebook corporate profiles for cross-referencing.
Extract full physical addresses, operating regions, and distributed office locations.
Navigate deep employee directories and technology category pages without triggering IP bans.
Monitor domains for newly added or dropped technologies. Receive only the differential data per run.
Provide a CSV of target domains. We return the enriched Datanyze profile for every match.
Brief in. Clean data out.
Provide target domains, technology categories, or industry filters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for datanyze.com.
Schema validation, null-rate checks, and sample profile extraction before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Datanyze protects its proprietary technographic database with aggressive rate limiting and bot detection. Here is how we maintain reliable extraction.
Datanyze employs strict request limits and TLS fingerprinting. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to blend in with legitimate traffic.
Technographic timelines and deep employee directories often require DOM hydration. We run full Playwright browser sessions with JavaScript execution to capture data that simple HTTP requests miss.
Datanyze updates its frontend structure periodically. Our selector strategy uses multiple fallback chains per field, including CSS selectors, XPath, and text-pattern matching to ensure continuous data flow.
For tracking technology adoption across millions of domains, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, proxy pool exhaustion, and schema drift, responding before your downstream systems are affected.
Strategy teams analyse technographic adoption rates across industries to calculate Total Addressable Market for new integrations.
Product marketers track which companies are dropping competitor tools and migrating to alternative platforms.
Sales operations teams enrich inbound leads with technographic data to route prospects and personalise outreach.
Growth teams build target account lists based on the presence of complementary software stacks.
Private equity analysts evaluate SaaS company health by tracking the net-new adoption of their tools across the web.
Revenue operations teams automatically update stale Salesforce records with fresh employee counts and revenue estimates.
"Datanyze holds the definitive map of B2B technology adoption, but querying it at scale requires bypassing sophisticated anti-scraping perimeters."
Extracting millions of company profiles and technology stacks requires constant rotation of residential IPs and headless browser fingerprinting. DataFlirt manages the extraction infrastructure so your data engineering team can focus on integrating the signals, not fighting rate limits.
Everything supported by our datanyze.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About datanyze.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available business information is generally permissible. DataFlirt targets only public, non-authenticated company profiles and public technographic data. We do not circumvent authentication walls to steal proprietary credit-based data. Clients should review terms of service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 429/CAPTCHA rate spikes in real time and trigger pool rotation automatically.
No. Datanyze gates premium contact data (unmasked emails and direct dials) behind a credit-based login system. We only extract publicly visible employee names, titles, and corporate contact formats.
Our pipelines extract the most recent data displayed on Datanyze at the time of the crawl. Continuous monitoring pipelines can be configured to run weekly or monthly to capture technology additions and drops.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per domain for technology adoption, allowing you to track when a tool was first detected and last detected.
Our smallest packages start at a defined list of 10,000 domains with monthly delivery. For larger scale monitoring across millions of domains, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 500 domains as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of 50,000 SaaS companies or continuous technographic monitoring across millions of domains. Tell us what you need.
