The gap between raw web data and actionable business insights is vast. Bridging it requires a robust data pipeline that can handle messy, unstructured data at scale while maintaining quality and freshness. Here's how we built ours.
Our pipeline has five stages: ingestion, parsing, normalization, enrichment, and delivery. Each stage is independently scalable and monitored, with dead-letter queues for failed records and automatic retry logic. This architecture ensures that problems in one stage don't cascade through the entire pipeline.
Ingestion handles the raw HTML and API responses from our scrapers. We store the complete raw response alongside metadata (source URL, timestamp, HTTP headers) in S3. This immutable raw layer lets us re-process historical data when we improve our parsing logic, without re-scraping.
Parsing extracts structured data from raw HTML. We use a combination of CSS selectors, XPath expressions, and custom parsers for each data source. For new or changing websites, we've built an ML-powered parser that can identify common patterns (product names, prices, descriptions) with minimal manual configuration.
Normalization is where we make data from different sources comparable. A price listed as '$29.99' on one site and '29,99 EUR' on another needs to be converted to a common format. We normalize currencies, units, date formats, and category taxonomies so that downstream analysis can compare apples to apples.
Enrichment adds context that wasn't in the original data. We geocode addresses, resolve company names to canonical entities, calculate derived metrics (price-per-unit, sentiment scores), and link related records across sources. This stage transforms data from merely structured to genuinely useful.
Delivery pushes the processed data to where it's needed: our real-time dashboards, client APIs, ML training pipelines, and data warehouses. We use event-driven architecture with message queues, so each consumer gets updates as soon as data is ready, without polling or delays.
