Case study · Data platform
Building a high-throughput sports-data ingestion platform
Automated collection and processing across multiple sports, designed for throughput, data integrity and reliable downstream reporting.
- Engagement
- Consulting CTO / Principal engineer
- Category
- High-throughput data platform
- Stack
- Rails, PostgreSQL, Sidekiq
- Throughput
- 12,000–40,000 webpages processed daily
The problem
Sports-data collection looks simple until the sources change, schedules overlap, formats vary and the downstream product depends on complete, timely data. The platform needed to collect large volumes continuously while keeping processing predictable and the stored data flexible enough to accommodate source variation.
What was built
A Rails and PostgreSQL ingestion and processing platform with automated collection, asynchronous scheduling, flexible storage and reporting outputs for downstream recommendation and decision systems.
Pipeline
- SchedulingRecurring jobs decide what to collect and when.
- Collection workersIsolated per source, so one failure does not stall the rest.
- Parsing & normalisationRaw payloads kept alongside a normalised model.
- PersistencePostgreSQL with JSONB where structures vary.
- ReportingOutputs for downstream recommendation and decision systems.
Engineering decisions
Automated collection used Ruby browser-automation and HTTP tooling, scheduled continuously through Sidekiq and cron.
- Collectors isolated by source, with retry and error handling that quarantines bad data instead of blocking the pipeline.
- Flexible storage for source variation, with normalised tables for the data downstream systems rely on.
- Sidekiq and scheduled jobs for predictable asynchronous throughput.
- Monitoring of throughput and completeness so gaps are visible before users notice them.
Outcome
A durable ingestion platform processing 12,000–40,000 webpages a day while keeping collection, storage and reporting operationally manageable.
What this demonstrates
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