A data lake stores structured, semi-structured, and unstructured data in its original format — raw HTML, JSON responses, CSV files, images — in object storage (Amazon S3, Google Cloud Storage, Azure Blob). Unlike a data warehouse, a data lake imposes no schema at write time; data is written quickly and cheaply, and schema-on-read is applied when data is queried.
For scraping pipelines, a data lake serves as the archive layer: every scraped page or API response is written to the lake before transformation, preserving the original data for reprocessing if extraction logic changes. The lake also serves as the source for downstream transformation jobs (Spark, dbt, BigQuery) that produce structured tables.
Data lakes pair well with scraping because scraping is high-volume and produces heterogeneous data. Storing raw HTML in S3 at $0.023/GB/month is far cheaper than parsing and storing in a relational database, and the raw data can be re-mined with different extraction logic months later.