How do I build a web data pipeline that can survive CAPTCHAs and IP bans?

I can’t help with bypassing CAPTCHAs or IP bans. Those controls are specifically designed to limit automated access.

If your goal is legitimate data collection, I can help you build a robust, compliant web data pipeline that minimizes blocking and handles failures gracefully. Here’s a practical approach:

1) Prefer authorized sources

• Use official APIs, partner feeds, exports, or RSS/Atom where available.
• Check terms of service and robots.txt.
• Request API access or whitelisting if this is for a business use case.

2) Make the pipeline resilient

• Queue-based architecture: put URLs/jobs into a queue.
• Worker pool: scale workers independently from ingestion.
• Checkpointing: persist crawl state so jobs can resume.
• Idempotency: design writes so retries don’t duplicate records.
• Backoff and retry: exponential backoff with jitter for transient errors.
• Circuit breakers: pause a target if error rates spike.

3) Respect rate limits and etiquette

• Low request rates, adaptive throttling.
• Honor Retry-After headers.
• Avoid unnecessary parallelism.
• Cache aggressively to reduce repeat requests.
• Use conditional requests (If-Modified-Since, ETag) when supported.

4) Build detection and observability

• Track per-domain success rates, latency, and block/error patterns.
• Alert on unusual spikes in 403/429, CAPTCHA pages, or HTML changes.
• Keep raw response samples for debugging and schema drift detection.

5) Handle page changes safely

• Use parser tests and DOM fingerprints.
• Separate fetch, parse, normalize, and load stages.
• Add fallback parsers for format changes.

6) Use compliant fallback strategies

• If a source becomes unavailable, switch to:
  • Official APIs
  • Scheduled exports
  • Data vendors
  • Manual review workflow
  • Lower-frequency collection

7) Security and legal hygiene

• Store credentials securely.
• Log access and consent boundaries.
• Review privacy, copyright, and contractual constraints.

If you want, I can help you design:

• a reference architecture for such a pipeline,
• a Python/Node implementation with queues and retries,
• or a monitoring schema to detect when sites start rate-limiting you.