Raw scraped data is rarely client-ready. Encoding issues, HTML entities, inconsistent date formats, duplicate records, and null placeholders ("N/A", "-", "") pollute datasets and break downstream analytics.
Our standard pipeline: Extract → Normalize → Validate → Dedupe → Deliver. Each stage has automated checks and human review triggers for anomalies.
Normalization examples: prices stripped to float with currency code, dates parsed to ISO 8601, phone numbers to E.164, addresses geocoded via API. Validation uses JSON Schema or Great Expectations rules — fail the batch if >2% records violate schema.
Deduplication: exact hash on primary key where available; fuzzy match on title+location for listings; time-window dedup for news (same story, multiple sources).
Delivery formats: CSV, JSON, JSONL, Parquet, direct PostgreSQL/MongoDB insert, REST API, webhooks on change. Multiple formats simultaneously is common during client validation phases.
Scrapy Ninja guarantees clean data — not just raw HTML dumps. Ask for a sample dataset when requesting a quote.
