How do I build a public data collection system for dozens of target domains?

To build a public-data collection system for dozens of target domains, design it like a small ingestion platform rather than a set of one-off scrapers.

1) Start with scope and rules

For each domain, define:

• What to collect: pages, PDFs, RSS feeds, APIs, file downloads, metadata, etc.
• How often: hourly, daily, weekly, or event-driven.
• Allowed access: check robots.txt, terms of service, rate limits, and any API terms.
• Data sensitivity: only collect publicly accessible data and avoid anything behind authentication or access controls unless you have explicit permission.

2) Use a modular architecture

A good pattern is:

• Scheduler

  • Triggers collection jobs per domain.
  • Examples: cron, Airflow, Prefect, Celery beat, GitHub Actions for small scale.

• Domain connectors

  • One connector per site or site family.
  • Each connector handles:
    • discovery of URLs
    • fetching
    • parsing/extraction
    • normalization

• Queue / job system

  • Stores crawl tasks and retries.
  • Helps you scale across dozens of domains safely.
  • Examples: Redis queue, RabbitMQ, SQS, Kafka.

• Fetcher layer

  • Responsible only for HTTP retrieval.
  • Handles:
    • user-agent identification
    • throttling
    • retries with backoff
    • caching / ETags / Last-Modified
    • respect for robots and crawl delays

• Parser / extractor layer

  • Converts HTML/PDF/JSON into structured records.
  • Keep this separate from fetching.

• Storage

  • Raw archive: object storage like S3/GCS/Azure Blob.
  • Structured data: PostgreSQL, BigQuery, Snowflake, Elasticsearch, etc.
  • Keep raw and processed data separate.

• Monitoring

  • Track failures, response codes, parse errors, content changes, and data freshness.

3) Standardize your data model

Create a common schema across domains, for example:

• source_domain
• source_url
• fetch_time
• title
• published_at
• content_text
• content_html or raw_blob_ref
• hash
• language
• entity_type
• status

This makes downstream search, analytics, and QA much easier.

4) Discover URLs efficiently

Depending on the domain:

• Use sitemaps
• Use RSS/Atom feeds
• Use public APIs
• Crawl only known sections, not the entire web
• Use link discovery from seed pages
• Track canonical URLs to avoid duplicates

For dozens of domains, maintain a per-domain config:

• start URLs
• allowed path patterns
• excluded patterns
• crawl frequency
• parsing rules
• rate limits

5) Be polite and robust

Implement:

• per-domain request throttling
• random jitter between requests
• exponential backoff on errors
• conditional requests (If-Modified-Since, ETag)
• caching to avoid refetching unchanged content
• deduplication by URL + content hash

This keeps your system stable and reduces load on target servers.

6) Separate raw capture from extraction

Store the raw response first, then parse it asynchronously. Benefits:

• easier debugging
• reproducibility
• ability to re-parse when extraction rules change
• better auditability

7) Build observability in from the start

Track:

• requests per domain
• success/error rates
• median latency
• parse success rate
• new records per day
• content drift / template changes
• last successful fetch per source

Set alerts for:

• spikes in 403/429/5xx
• zero new items
• parse failures after site redesigns

8) Handle site changes gracefully

Do not hardcode fragile selectors everywhere. Instead:

• use site-specific extractor modules
• keep parsing rules versioned
• use fallback extraction methods for text
• write tests with saved HTML samples
• add a manual review queue for broken sources

9) Suggested tech stack

A common stack for dozens of domains:

• Orchestration: Prefect / Airflow
• Queue: Redis / SQS
• Fetch: Python requests / httpx, plus playwright only when JS rendering is truly required
• Parsing: BeautifulSoup, lxml, trafilatura, pandas, pdfplumber
• Storage: S3 + PostgreSQL
• Monitoring: Prometheus + Grafana, or cloud-native logs/metrics

10) Recommended workflow

1. Inventory target domains and data types.
2. Define a per-domain config file.
3. Build a generic fetcher with rate limiting and logging.
4. Add one connector at a time.
5. Save raw responses before parsing.
6. Add automated tests using known sample pages.
7. Monitor and iterate.

11) A simple config example

domain: example.com
start_urls:
  - https://example.com/news/
crawl_delay_seconds: 3
allowed_paths:
  - /news/
  - /articles/
exclude_patterns:
  - /tag/
  - /author/
  - /login
content_type: html
parser: example_news_parser

If you want, I can also help you with:

• a reference architecture diagram
• a Python project structure
• a sample crawler template
• or a domain configuration schema for dozens of sites.