Competitive intelligence platforms, electronic component marketplaces, real estate aggregators, and AI training pipelines depend on continuous, clean data ingestion from hundreds of external vendor APIs and websites. Naive scripts break the moment an upstream API returns HTTP 429 rate limits, changes its JSON structure, or requires dynamic OAuth/session cookie renewal.
If you need to hire a freelance Python ETL, data pipeline, and resilient web scraping programmer, I am Aldian Fazrihady.
Production Data Ingestion & Scraping Architectures
1. High-Speed Asyncio & Polymorphic Vendor Adapters
I build modular request adapters (Polymorphic Crawler Design) powered by Python asyncio (High-Speed Async Crawlers) and dynamic multi-provider OAuth token managers (Managing Multi-Provider OAuth Tokens).
2. Adaptive 429 Rate-Limit Backoff & SQS Dead-Letter Queues
Every ingestion worker respects Retry-After headers, jittered exponential backoff (Dealing with 429 Rate Limits), and AWS SQS Dead-Letter Queues (Handling Malformed Scraping Payloads with DLQs) so zero records are silently dropped.
3. Entity Normalization, Fuzzy Matching & Elasticsearch Indexing
Raw vendor data is messy. I implement deterministic and fuzzy string normalization (Manufacturer Name Normalization in Python) before streaming clean records into PostgreSQL, DynamoDB, and Elasticsearch.