20 million ticket listings. A daily data operation.
How we built and maintained a daily collection pipeline for a global ticket intelligence platform, delivering approximately 20 million listings a day over more than two years.
- Listings delivered daily
- ~20M
- Production operation
- 2+ years
- Delivery cadence
- Daily
01 / The challenge
A snapshot was not enough.
For a ticket intelligence platform, a listing only tells part of the story. Its event, seating section, quantity and price need to remain connected. A useful dataset also needs to be refreshed as marketplace inventory changes.
The client needed this at a scale of roughly 20 million listings per day. A one-off export could not support a recurring analytical workflow. Collection, validation and delivery had to become an operation the team could maintain as the source changed.
The engineering challenge was therefore broader than retrieving a page: sustain a large daily collection workload while keeping the output structured and useful to the client's downstream systems.
02 / Our approach
What we built around the brief.
- 01
Design for the recurring workload
We built the collection workflow around the required daily dataset. The delivery schedule and record structure shaped the pipeline, rather than treating each scrape as a separate export.
- 02
Keep listing context together
The output preserved the relationship between events and their listings. Seating, quantity and pricing information stayed attached to the record it described, supporting comparison without losing marketplace context.
- 03
Maintain the production operation
Collection was paired with monitoring, validation and ongoing maintenance. Changes to the source required operational attention, not simply another run of an unchanged script.
03 / Inside the work
The record behind the volume.
The important unit is a structured listing, not an HTTP request. These field groups describe the ticket data we collect; availability and naming vary by marketplace.
| Data group | What it preserves |
|---|---|
| Event & listing identity | Marketplace, event ID and listing ID connect each offer to its event. |
| Seating | Section and row information, with seat details where supplied by the source. |
| Inventory | Ticket quantity and available purchase quantities where present. |
| Pricing | Displayed price and source-provided price breakdowns, without treating a pre-checkout value as a final checkout total. |
| Collection context | Collection timestamps help distinguish observations made at different times. |
Field groups are descriptive, not a published client export or a promise that every source provides every field.
04 / The outcome
A daily feed, supported beyond launch.
2+ years of production operation
The pipeline delivered approximately 20 million ticket listings daily and operated in production for more than two years. That sustained operation is the central result: the client received recurring structured data rather than a collection of disconnected one-off files.
The work supported the client's ticket intelligence workflow while ScrapingBuddy handled the collection and maintenance layer. The result demonstrates a managed data operation at this project's scale, not a universal throughput benchmark.
How to read these results
The daily figure refers to listing records delivered, not unique tickets sold, API requests or guaranteed coverage of an entire marketplace. Volume and coverage depend on the agreed source and collection scope.
05 / The takeaway