data-autocleaning

Automate data quality for BigQuery pipelines by running a deterministic workflow that handles data ingestion, profiling, cleansing, and schema mapping. This enforces consistent quality verification and reduces manual transformation errors across sources.

Data profiling with GCS and BigQuery is a process that analyzes source datasets to generate statistics and determine data quality. It uses Dataplex-based profiling to assess data structures and guide subsequent automated cleansing rules.

Yes, you can standardize schemas and normalize data sourced from Google Cloud Storage. The workflow uses deterministic transformation templates to map schemas and normalize data, ensuring consistency for downstream systems and deployment.

The best way to clean BigQuery data without manual handoffs is using a guided autocleaning workflow. It enforces deterministic transformations and end-to-end quality verification, reducing error-prone handoffs across the data lifecycle.

Dataplex is required for data profiling within this cleansing workflow. The automated pipeline relies on Dataplex-based profiling to analyze datasets and generate the guided cleaning rules needed for transformations.