ScrapingBuddy / Project stories

Case studies.

The work behind the data.

Three projects. Different challenges. A closer look at what we built, what changed and what the results mean.

Selected work

From the brief to the outcome.

Collection at scale. Connected datasets.
A faster path to delivery.

01 / Ticket intelligenceManaged data collection

A ticket intelligence platform needed recurring, large-scale marketplace data for its own analytics.

The outcome: Approximately 20 million ticket listings delivered daily, with more than two years of production operation.

Ticket intelligence / daily delivery

~20Mticket listing records per day

Event & listing identitySeating / quantity / pricingStructured daily feed

2+ years of production operation

02 / Restaurant intelligenceManaged data collection

A restaurant intelligence workflow needed comparable data across brands, locations and changing menus.

The outcome: 42 brands covered, with restaurant and menu details organised for recurring market analysis.

Restaurant intelligence / data structure

42brands in the programme

Connected detail, not just a list of menu prices.

03 / Data engineeringPipeline optimisation

A weekly restaurant dataset had a tight delivery window, but processing alone took more than three days.

The outcome: Processing reduced from more than three days to approximately four to five hours.

Processing time / before & after

Original workflow3+ days

Redesigned workflow4-5 hours

Processing stage only. Reported project durations.

Results describe individual ScrapingBuddy projects, not guaranteed performance for every workload. Client identities are not published.

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