Data warehouses (Snowflake, BigQuery, Redshift, ClickHouse) are designed for OLAP (Online Analytical Processing) — complex aggregations over billions of rows — rather than OLTP (transactional reads and writes). They use columnar storage, which compresses repeated values efficiently and enables fast full-column scans for aggregation queries.
For scraping pipelines, the data warehouse is the analytical destination: cleaned, normalised, and deduplicated scraped records are loaded via batch ETL or streaming (via Kafka/BigQuery Streaming Inserts) into fact and dimension tables. Analysts then query the warehouse with SQL to answer business questions — price trend analysis, competitor monitoring, market share estimation.
BigQuery and Snowflake both support external tables over data lake storage (S3, GCS), allowing semi-structured JSON scraped data to be queried without pre-defining a schema — combining the flexibility of a data lake with the query capability of a warehouse.