What's the best way to collect publicly available data efficiently?

The most efficient approach is usually API/open-data first, targeted crawling second, full web scraping last.

A practical workflow

1. Define exactly what you need

• Fields, geographic scope, date range, update frequency.
• Decide what counts as a valid record before collecting anything.

2. Look for structured sources first

• Official APIs
• Government/open-data portals
• CSV/JSON/XML downloads
• Public databases and registries
• RSS feeds
• Only then scrape HTML pages.

APIs are generally much easier to automate, paginate, validate, and keep up to date. The EU's open-data guidance specifically recommends APIs for automated, reusable access to high-value datasets. European Data Portal+1 3. Build a source inventory

Keep a small table such as:

Source	Method	Fields	Update rate	License	Reliability
Official API	API	12	Daily	Open	High
Government CSV	Download	8	Monthly	Open	High
Website	HTML	5	Weekly	Check	Medium

4. Collect incrementally Don't repeatedly download everything. Store a stable identifier and timestamp, then retrieve only new/changed records where the source permits it.
5. Use pagination, batching and caching For large APIs, request data in pages/batches and cache responses when permitted. This substantially reduces requests and makes the pipeline faster and more robust. Google for Developers+1
6. For websites, crawl selectively Start from known URLs/sitemaps rather than blindly crawling an entire domain. Respect the site's terms, robots directives, rate limits, and applicable law. robots.txt communicates crawling preferences, although it isn't a security mechanism. MDN Web Docs
7. Be a polite client

• Set a reasonable request rate.
• Use timeouts.
• Retry transient failures with exponential backoff.
• Don't fire thousands of simultaneous requests.
• Identify your client where appropriate.

Exponential backoff and throttling are standard recommendations for avoiding unnecessary server load and quota problems. Google for Developers+1 8. Normalize immediately Convert everything into a consistent schema as it enters your pipeline: source → raw data → parser → normalized records → validation → database. 9. Preserve provenance For every record, ideally retain:

• source URL/API endpoint
• collection timestamp
• source-specific ID
• original/raw value where useful
• transformation/version information

10. Automate monitoring Track HTTP errors, missing fields, record counts, schema changes, and unusually large/small updates. A scraper that silently breaks is often worse than no scraper.

A good architecture

             ┌── Official API ──────┐
             ├── Open-data files ───┤
Sources ─────┼── Public database ───┼──> Ingestion
             └── HTML pages ────────┘       │
                                             ▼
                                      Raw data store
                                             │
                                             ▼
                                      Parse / normalize
                                             │
                                             ▼
                                        Validation
                                             │
                                             ▼
                                      Database / files
                                             │
                                             ▼
                                    Analysis / application

Rule of thumb: if you can obtain the same information through a documented API or downloadable dataset, don't scrape the webpage. It will usually be faster, more stable, and easier to maintain. European Data Portal+1

If you tell me what kind of public data you're trying to collect (e.g. company data, government statistics, news, real-estate listings, academic papers, social media, etc.), I can suggest a concrete collection stack and workflow.