Collection Architecture Design

Manual research does not scale. A researcher who monitors ten sources manually checks them when they remember, misses updates during vacations, and produces inconsistent coverage depending on workload and attention. Automated collection inverts the model. The system monitors sources continuously, captures every update, and stores everything in a structured format. The researcher's job shifts from collecting data to analyzing data that has already been collected. This is the architectural difference between a person doing research and a system that produces research.

Collection architecture has four components. Source registry: a catalog of every data source the system monitors, including connection type, update frequency, data format, and reliability rating. Collection engine: the processes that actually pull data from sources on schedule or in response to triggers. Storage layer: where collected data lands — normalized, timestamped, deduplicated, and indexed for retrieval. Quality monitor: automated checks that flag missing data, format changes, stale connections, and anomalous patterns in collected data.

1. Source Registry Catalog every source with metadata: name, URL or API endpoint, connection type (RSS, API, scraper), expected update frequency, data format, and a reliability score based on historical uptime. The registry is your ground truth for what the system monitors.
2. Collection Engine A scheduler that triggers collection jobs at configured intervals. Each job connects to a source, pulls new data since the last collection, normalizes the format, and writes to the storage layer. Failed jobs retry with exponential backoff and alert after three consecutive failures.
3. Storage Layer Incoming data is timestamped, tagged with source metadata, deduplicated against existing records, and indexed for full-text search. Use append-only storage — never overwrite collected data. Historical context matters when you are tracking changes over time.
4. Quality Monitor Automated checks that run after every collection cycle. Did the expected number of sources return data? Are any sources returning significantly more or less data than their historical average? Has a source's data format changed? Quality monitoring catches silent failures that would otherwise corrupt your intelligence pipeline.