Valitavast

Valitavast is a fictional open-source software framework designed to validate and govern data quality across distributed systems. It provides a rule-based validation engine, schema checks, and an auditable provenance trail to support data quality initiatives in complex pipelines.

The name blends val (short for validate) with vast, signaling broad validation capabilities.

Key features include a modular core, pluggable validators, a domain-specific rule language, and connectors for common data sources. It supports both batch and streaming validation, data lineage tracking, and built-in audit logging to meet governance requirements. It is designed to be deployment-agnostic and can run as a library, a microservice, or a containerized service.

The architecture centers on a core engine that orchestrates validators and rules; validators can be extended by plugins. Data connectors abstract source and sink interfaces, while configuration is provided in YAML or JSON. A reporting API exposes validation results and lineage graphs.

In typical data pipelines, Valitavast is inserted after data ingestion or during ETL to check quality. Common integrations include Kafka streams and Spark jobs. It returns structured reports and flags records that fail rules for remediation.

Originating in 2021 as a collaborative effort among data-engineering groups, the project released its first stable version in 2022 and has since evolved through several major releases with community contributions.

Reception notes modularity and auditability as strengths, while acknowledging a learning curve and potential performance overhead for very large datasets.

Related topics include data validation, data quality, data governance, and schema validation.