We extract public activities, segment leaderboards, club statistics, and route geometries from Strava. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Segments objects from strava.com. All fields typed and schema-versioned.
"segment_id": "229781", "name": "Hawk Hill", "distance_meters": 2684.8, "average_grade": 5.7, "climb_category": 2, "kom_time": "00:05:44", "total_efforts": 184920, "total_athletes": 28310
| # | segment_id | name | distance_meters | average_grade | maximum_grade | elevation_difference |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Activities objects from strava.com. All fields typed and schema-versioned.
"activity_id": "847291048", "athlete_id": "19482", "name": "Morning Ride", "activity_type": "Ride", "distance_meters": 42195.0, "moving_time": 5420, "total_elevation_gain": 450.2, "kudos_count": 42
| # | activity_id | athlete_id | name | activity_type | distance_meters | moving_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Athletes objects from strava.com. All fields typed and schema-versioned.
"athlete_id": "19482", "username": "jdoe_runner", "firstname": "John", "city": "London", "country": "United Kingdom", "follower_count": 412, "primary_shoes": "Nike Vaporfly 3", "club_count": 4
| # | athlete_id | username | firstname | lastname | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Clubs objects from strava.com. All fields typed and schema-versioned.
"club_id": "93821", "name": "London Cycling Club", "sport_type": "cycling", "city": "London", "country": "United Kingdom", "member_count": 1420, "is_private": false, "url": "https://www.strava.com/clubs/london-cycling"
| # | club_id | name | sport_type | city | state | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Leaderboards objects from strava.com. All fields typed and schema-versioned.
"segment_id": "229781", "rank": 1, "athlete_name": "Jane Doe", "athlete_id": "94821", "elapsed_time": "00:05:44", "average_speed": 28.1, "average_power": 310, "start_date": "2025-04-12T08:14:00Z"
| # | segment_id | rank | athlete_name | athlete_id | elapsed_time | moving_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Strava scraper handles segment paginations, leaderboard depth, activity feeds, and club registries with full anti-bot circumvention and session management built in.
Extract KOMs, QOMs, and full top 100 leaderboards for any segment, including historical times and athlete details.
Capture distance, elevation, pace, moving time, and social metrics from public activities across targeted regions.
Extract follower counts, club memberships, primary gear, and recent activity summaries from public athlete profiles.
Track member counts, weekly leaderboards, and recent activity feeds for public clubs and brand pages.
Extract coordinate polylines and elevation profiles from public routes for geospatial analysis.
Monitor 90-day effort counts and current Local Legend status across key segments.
Extract declared shoes and bikes used in public activities to track brand adoption and mileage.
Capture kudos counts and comment threads on public posts to measure engagement.
Run one-off bulk exports or configure continuous pipelines at daily cadences for segment monitoring.
Brief in. Clean data out.
Provide segment IDs, club URLs, or geographic bounding boxes. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and rate-limit handling for strava.com.
Schema validation, null-rate checks, and coordinate parsing verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or BigQuery dataset on agreed cadence.
Strava heavily rate-limits and protects its endpoints. Here is how we maintain stable extraction.
Strava protects endpoints with strict rate limits and Cloudflare. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to avoid blocks.
Extracting full segment leaderboards requires navigating complex pagination logic. Our pipeline manages state across thousands of pages to ensure complete data capture without duplicates.
Route data is often encoded in complex polyline formats. We decode these geometries on the fly, delivering clean GeoJSON or coordinate arrays ready for mapping.
For large segment catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Athletes frequently change privacy settings. Our observability stack alerts on null-rate spikes, ensuring pipeline health and accurate data representation.
Track gear usage, shoe mileage, and brand adoption across specific demographics and regions.
Analyse popular cycling and running routes to inform infrastructure investments and safety improvements.
Create virtual race leaderboards and monitor segment challenges outside of official API limitations.
Train machine learning models on pace, elevation, and distance correlations using public telemetry.
Monitor brand club engagement, member growth, and activity levels across rival sports brands.
Analyse popular trails and outdoor activity density to optimise marketing and resource allocation.
"Strava holds the largest structured dataset of human endurance on the internet, but accessing it beyond the restrictive API requires purpose-built infrastructure."
Relying on the official API means dealing with strict rate limits and restricted endpoints. DataFlirt bypasses these limitations by extracting public web data directly, using residential proxies and headless browsers to deliver clean, warehouse-ready telemetry without API quotas.
Everything supported by our strava.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 and deduplication. Playwright handles JavaScript rendering and interaction flows for dynamic map tiles.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent rate limits.
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 strava.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated segment, activity, and profile data. We do not extract private activities or violate GDPR. Clients should review Terms of Service and consult legal counsel.
We use residential ISP proxies and request timing modelled on human behaviour. We monitor for 429 rate limit spikes in real time and trigger pool rotation automatically.
No. We only extract data that users have explicitly chosen to make public on the web interface.
Pipelines achieve daily refreshes for segment leaderboards. High-priority segments can be monitored at hourly cadences.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per segment from the date your pipeline starts.
Our smallest packages start at a defined segment list with weekly delivery. Contact us with your use case for a scoped quote.
Yes. We decode route geometries into standard coordinate arrays suitable for mapping and geospatial analysis.
Yes. We provide a sample run of up to 100 segments or activities to validate schema fit 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 segment dump or a continuous activity feed across regions, we scope, build, and operate the pipeline. Tell us what you need.
