Dataraw

Dataraw is a term used in data management to denote datasets captured directly from source systems, devices, or logs in their native, unprocessed form. It refers to data immediately after collection, before any cleaning, normalization, or aggregation has occurred.

The concept is closely related to raw data and is commonly encountered in data pipelines, data lakes, and auditing workflows. Dataraw can include structured records, semi-structured logs, or binary captures, and it carries metadata describing origin, timestamping, and collection methods.

Characteristics of dataraw include high volume and variety, and it may lack stable schemas. It frequently contains noise, outliers, and missing values, yet preserves complete provenance, enabling traceability and reproducibility of processing steps.

Formats and storage: It is typically stored in data lakes or append-only storage, using formats such as line-delimited JSON, CSV dumps, log files, or raw binary blobs, before downstream processing. Access is managed to ensure provenance and privacy.

Uses and processing: Analysts and data scientists inspect dataraw to debug pipelines, verify data lineage, or reprocess data with different cleaning rules. In practice, dataraw is the input for data wrangling and transformation processes (ETL or ELT).

Challenges: The unprocessed nature makes dataraw demanding in terms of storage, compute, and governance. Quality issues and privacy concerns require careful handling, annotation, and cataloging to enable reliable analysis.

See also: Raw data; Data lake; Data wrangling; ETL; ELT.