Data Lifeguard Tool

The Data Lifeguard Tool validates and monitors data quality across analytics datasets. It ingests a dataset reference, optional schema, and a set of quality rules, then runs structural, content, and relational checks. It returns a composite quality score and a detailed violation log, enabling teams to act before analytics or ML pipelines.
Conceptually, it treats data quality as a gate: ensure schema conformity, proper data types, and complete columns; verify value patterns and allowed sets; check duplicates and referential integrity. It supports CSV, Parquet, and SQL sources, and can operate at ingestion time or on scheduled batch checks. The output is designed to be consumed by data catalogs, orchestration tools, and dashboards.
Unique value and differentiators: programmable rule templates, configurable weights, drift detection against baselines, and scalable sampling for large datasets. It produces machine-readable artifacts (scores, violation logs, and remediation guidance) that fit into ETL pipelines. Use cases include pre-analytics data hygiene, data lake governance, and audit-ready reporting.
Core features and workflow are intentionally separation of responsibilities: separation of schema checks from content checks, per-column scoring, and exportable reports. Localization and privacy considerations are supported, with ISO-aligned concepts and privacy-aware processing. The tool emphasizes reproducibility and traceability for data teams.