We extract group profiles, event schedules, RSVP counts, venue details, and topic tags from Meetup. 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 Group Profiles objects from meetup.com. All fields typed and schema-versioned.
"group_id": "3128492", "name": "Bengaluru Python Users Group", "city": "Bengaluru", "member_count": 14291, "organiser_name": "Karthik R.", "creation_date": "2015-08-12T10:00:00Z"
| # | group_id | name | urlname | city | country | member_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Upcoming Events objects from meetup.com. All fields typed and schema-versioned.
"event_id": "291837461", "title": "Advanced Asyncio in Python 3.12", "start_time": "2026-06-15T18:30:00Z", "rsvp_count": 145, "is_online": false, "event_url": "https://meetup.com/blr-python/events/291837461"
| # | event_id | title | group_id | start_time | end_time | timezone |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Venue Data objects from meetup.com. All fields typed and schema-versioned.
"venue_id": "847192", "name": "WeWork Galaxy", "city": "Bengaluru", "lat": 12.9738, "lon": 77.6119, "is_hidden": false
| # | venue_id | name | address | city | state | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Past Event History objects from meetup.com. All fields typed and schema-versioned.
"event_id": "281746291", "title": "Introduction to FastAPI", "final_rsvp_count": 210, "rating": 4.8, "review_count": 42, "duration": 7200
| # | event_id | title | group_id | date | final_rsvp_count | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Topic & Category Data objects from meetup.com. All fields typed and schema-versioned.
"topic_id": "1843", "name": "Machine Learning", "parent_category": "Tech", "group_count": 8492, "member_count": 1204938, "description": "Groups focused on ML algorithms and data science."
| # | topic_id | name | urlkey | parent_category | group_count | member_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Meetup scraper handles the underlying GraphQL APIs, extracting group metadata, event schedules, RSVP velocity, and venue details with proper pagination and anti-bot circumvention built in.
Extract member counts, creation dates, descriptions, and organiser details across thousands of groups globally.
Monitor upcoming events, start times, RSVP limits, and waitlist counts to gauge community interest.
Track how fast events fill up by capturing RSVP counts at scheduled intervals before the event date.
Extract exact latitude, longitude, and physical addresses for events to map community density.
Capture the standard Meetup topics assigned to groups and events to build categorised datasets.
Retrieve historical attendance data, ratings, and comment counts for events that have already occurred.
Identify whether an event is hosted physically or via a virtual meeting link.
Track who runs multiple groups and extract publicly available contact links for community leaders.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide group URLs, target cities, or specific topic tags. We design the extraction schema together.
We configure Scrapy crawlers, GraphQL query interception, and proxy rotation for meetup.com.
Schema validation, null-rate checks, and pagination testing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Meetup relies heavily on complex GraphQL APIs and rate limiting. Here is how we maintain stable extraction pipelines.
Meetup functions as a Single Page Application powered by Apollo GraphQL. Instead of parsing DOM elements, our crawlers intercept and replicate the exact GraphQL queries used by the frontend, yielding perfectly structured JSON responses.
Extracting historical events or large member lists requires navigating deep cursor-based pagination. Our pipeline handles cursor state automatically, resuming exactly where it left off if a connection drops.
Meetup employs rate limits and IP bans for excessive query volumes. We distribute requests across residential ISP proxies, ensuring our extraction volume stays within normal user behaviour thresholds.
For large group catalogues, we maintain a hash index of last-seen values per field. 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, schema drift, and coverage drops, responding before you notice.
Developer tools companies track tech meetups to sponsor events and identify local community leaders.
Coworking spaces and event venues map physical event clusters to identify high-demand neighbourhoods.
Analysts track the growth of specific tech stacks or hobbies by monitoring group creation rates and RSVP velocity.
Brands monitor rival companies to see which communities they sponsor or host events for.
Agencies target specific zip codes by understanding the demographic interests of local active groups.
Hedge funds track community engagement metrics for open source tools to gauge developer adoption rates.
"Meetup represents the largest global graph of professional and hobbyist communities, but extracting the underlying event schedules requires significant GraphQL reverse engineering."
Most teams underestimate the investment required: reliable Meetup scraping requires handling complex GraphQL pagination, managing rate limits, bypassing bot protection, and maintaining queries. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our meetup.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.
We bypass brittle DOM parsing by intercepting the exact GraphQL queries used by the Meetup frontend, resulting in highly structured and stable JSON extraction.
We maintain pools of residential ISP proxies to distribute query volume organically, preventing IP bans and rate-limit blocks during large historical backfills.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About meetup.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Meetup is generally permissible. DataFlirt targets only public group profiles, event schedules, and venue data. We do not extract personal data from private groups or circumvent authentication walls. Clients should review Meetup's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies and distribute GraphQL queries across thousands of IPs. Our request timing is modelled on human behaviour to avoid triggering automated blocks.
No. We only extract data that is publicly visible without requiring a user to join the group or log in to an approved account.
Pipelines can be configured to run daily or hourly. Group metadata and upcoming event schedules are typically refreshed every 24 hours to capture new RSVPs.
Yes. We can paginate through a group's past events to extract historical attendance numbers, ratings, and event frequencies.
Our smallest packages start at a defined list of 1,000 groups or a specific city's tech category with weekly delivery. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dump of tech groups in London or a continuous feed of global event schedules, we scope, build, and operate the pipeline. Tell us what you need.
