We extract watch reference numbers, caliber specifications, case dimensions, and family histories from Watchbase. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Watch Models objects from watchbase.com. All fields typed and schema-versioned.
"reference_number": "116610LN", "brand": "Rolex", "family": "Submariner", "case_material": "Stainless Steel", "diameter_mm": 40.0, "water_resistance_m": 300, "dial_colour": "Black"
| # | reference_number | brand | family | name | limited_edition | produced_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Calibers objects from watchbase.com. All fields typed and schema-versioned.
"caliber_reference": "3135", "brand": "Rolex", "movement_type": "Automatic", "jewels": 31, "power_reserve_h": 48, "frequency_vph": 28800, "date_complication": true
| # | caliber_reference | brand | base_movement | movement_type | display | date_complication |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brands objects from watchbase.com. All fields typed and schema-versioned.
"brand_name": "Omega", "founded_year": 1848, "founder": "Louis Brandt", "headquarters": "Biel/Bienne, Switzerland", "parent_company": "Swatch Group", "watch_count": 3482, "caliber_count": 184
| # | brand_name | founded_year | founder | headquarters | website | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Families objects from watchbase.com. All fields typed and schema-versioned.
"brand": "Patek Philippe", "family_name": "Nautilus", "watch_count": 142, "earliest_model_year": 1976, "signature_feature": "Porthole case shape", "sub_families_count": 4
| # | brand | family_name | description | watch_count | earliest_model_year | latest_model_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Market & Pricing objects from watchbase.com. All fields typed and schema-versioned.
"reference_number": "15202ST.OO.1240ST.01", "currency": "USD", "retail_price": 33200.0, "price_date": "2023-10-14", "discontinued": true, "availability_status": "Out of Production", "market_segment": "Luxury Sports"
| # | reference_number | currency | retail_price | price_date | availability_status | discontinued |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Watchbase scraper captures the highly relational nature of watch data, mapping base movements to specific calibers and tying them to thousands of individual reference numbers.
Capture case dimensions, materials, dial colours, and water resistance for every watch reference listed.
Extract jewel counts, power reserves, operating frequencies, and complication lists for every mapped movement.
Preserve the links between parent brands, watch families, specific references, and the calibers that power them.
Extract founding histories, parent company hierarchies, and total model counts per manufacturer.
Track production years and identify discontinued models for secondary market valuation models.
Capture direct URLs for case, dial, and movement imagery for visual authentication and catalogue building.
Normalise complex complication strings into structured boolean fields (e.g., perpetual calendar, tourbillon, moonphase).
Extract listed retail prices and currency data where available to establish baseline valuation metrics.
Run continuous pipelines to detect new reference releases and caliber updates without re-scraping the entire database.
Brief in. Clean data out.
Provide target brands, specific families, or request the entire Watchbase catalogue. We map the required schema.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for watchbase.com.
Schema validation, null-rate checks, and relational integrity testing between watches and calibers.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting Watchbase requires maintaining relational integrity across thousands of pages while navigating rate limits.
Watch data is inherently relational. A single base movement might be modified into ten different calibers, powering fifty different watches. Our pipeline maintains these foreign-key relationships during extraction, delivering normalised tables that join perfectly in your warehouse.
Watchbase enforces strict request limits to protect their database. We utilise distributed proxy pools and configure low-concurrency, high-delay request profiles to ensure continuous extraction without triggering IP bans or degrading site performance.
Older watch references often have missing fields or non-standard specification formats compared to modern releases. Our parsers use fallback regex patterns and fuzzy matching to extract dimensions and materials even when the DOM structure deviates.
Case materials and dial colours are often described inconsistently (e.g., 'Pink Gold' vs 'Rose Gold'). We can apply post-extraction normalisation dictionaries to standardise these attributes for easier filtering and analysis.
We extract the highest resolution image URLs available for each reference and caliber, avoiding thumbnails. These URLs are delivered alongside the metadata, ready for ingestion into your DAM or CDN.
Grey market dealers use structured reference data and retail baselines to train pricing algorithms and detect arbitrage opportunities.
Authentication platforms cross-reference case dimensions, caliber specs, and jewel counts to identify counterfeit watches.
Retailers auto-populate their e-commerce catalogues with precise specifications by matching incoming stock to Watchbase references.
Analysts track brand output, complication trends, and material usage over time to understand horological industry shifts.
Underwriters use historical production dates and reference specifications to assess replacement values for scheduled property.
Machine learning teams use the image URLs and associated metadata to train visual recognition models for watch identification.
"Watchbase is the definitive digital encyclopaedia for horology, but extracting its highly relational caliber-to-reference data requires a precise extraction schema."
Watch data is inherently relational. A single caliber might power forty different references across multiple brands, each with distinct case materials and dial configurations. DataFlirt reconstructs this relational graph, handling rate limits and DOM variations so your engineers can focus on valuation models, not scraping infrastructure.
Everything supported by our watchbase.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 the deep crawling required to traverse brand taxonomies down to individual reference pages, ensuring complete catalogue coverage.
PostgreSQL maintains the mapping between discovered calibers and watches during the crawl, ensuring foreign keys are correctly assigned before export.
Airflow schedules regular diff-runs to detect new releases, pushing updated Parquet files directly to your S3 buckets.
Data delivered to where your team already works — no new tooling required.
About watchbase.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We can configure the pipeline to target specific brands (e.g., Rolex, Patek Philippe, Audemars Piguet) or specific families rather than crawling the entire Watchbase catalogue.
Our extraction schema treats watches and calibers as separate entities linked by a foreign key. This allows you to ingest the data into a relational database without duplicating caliber specifications across hundreds of watch models.
We extract the direct URLs for all images associated with a reference or caliber. We do not host the images, but provide the URLs for your systems to download and ingest.
Given the relatively slow release cycle of new watch models, we typically recommend a weekly or monthly pipeline cadence to detect new references and calibers, though faster cadences are available.
By default, we extract the exact text displayed on Watchbase. However, we can implement custom post-processing scripts to normalise terms according to your internal taxonomy.
Yes. If Watchbase updates a model's status or lists a final production year, our change-detection pipeline will capture this update and flag the reference as discontinued in your next data delivery.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete dump of all mechanical calibers or continuous tracking of new reference releases — we scope, build, and operate the pipeline. Tell us what you need.
