Changelog – Release 2026.01¶

With Release 2026.01, digna introduces major improvements to datasource modeling, connection management, and inspection usability.
This release enhances flexibility across all modules and significantly extends data quality and validation coverage.

🚀 New Features¶

Global Database Connections¶

• Database connections are now configured on a global level.
• Global connections can be reused across all projects, simplifying configuration and maintenance.
• Impact: Reduces operational overhead and ensures consistent connectivity across environments.

Multiple Source Connections per Project¶

• Projects can now reference multiple source connection configurations.
• Enables more flexible setups for complex data landscapes project.
• Impact: Supports realistic enterprise architectures with heterogeneous data sources.

Logical Datasources¶

• Datasources now represent a logical layer within a project.
• Each datasource can be backed by:
• a database table
• a database view
• a custom SQL statement
• This separation improves reuse, clarity, and inspection modeling across modules.
• Impact: Decouples inspections and data quality rules from physical storage, improving maintainability and reuse.

Anomaly Relevance Condition¶

• An Anomaly Relevance Condition can now be defined to control anomaly status evaluation on level of dataset.
• Statistics are calculated independently of whether the condition is set or met.
• If the condition is not met, digna Data Anomalies does not provide anomaly status (green / yellow / red).
• Example: Exclude the dataset from anomaly evaluation when the record count is below 10.
• Impact: Ensures anomalies are evaluated only in relevant business contexts.

Per-Module Notification Configuration¶

• Notifications can now be configured per module directly in digna.
• Allows independent control of alerting behavior for digna Data Anomalies, digna Data Timeliness, digna Data Validation, and other modules.
• Impact: Enables precise alerting strategies aligned with team responsibilities and criticality.

Inspection Results Export (CSV)¶

• Users can now download inspection results as CSV files.
• Enables offline analysis, reporting, and integration with external tools.
• Impact: Simplifies audits, reporting, and downstream data quality analysis.

🧪 Extended Data Validation Capabilities¶

With this release, digna Data Validation now supports a comprehensive set of data quality rules:

• Row-level validation rules
• Multi-column uniqueness checks
• Referential integrity validation across datasources

Together, these checks enable enforcement of structural and relational data quality rules across complex data landscapes.

Uniqueness Checks for Multiple Columns¶

• Introduced Uniqueness Checks for a configurable set of columns.
• Enables validation of compound keys and business-level uniqueness constraints.
• Impact: Detects duplicate business entities that cannot be identified with single-column checks.

Referential Integrity Checks¶

• Introduced Referential Integrity Checks to validate relationships between datasources.
• Ensures that foreign key values in a source datasource exist in a referenced target datasource.
• Supports validation across:
• different tables or views
• different schemas
• different database connections within the same project
• Helps detect orphaned records, broken relationships, and data consistency issues early.
• Designed to work with logical datasources, including views and custom SQL.
• Use cases: data warehouse integrity, regulatory reporting, master data consistency, and reliable downstream analytics.

🎯 Who Benefits from This Release¶

• Data Engineers: More flexible datasource modeling and reusable database connections
• Data Quality & Governance Teams: Expanded validation coverage including relational integrity rules
• Analytics & BI Teams: Cleaner inputs and exportable inspection results
• Platform Owners: Reduced configuration complexity and improved operational maintainability

🛠 CLI Updates¶

• No changes