Changelog – Release 2026.04¶
With Release 2026.04, digna significantly enhances its capabilities in analytics and data validation.
This release introduces advanced time-series analysis, reusable validation components, and centralized value standardization.
New Features¶
Analytics Chart – Time Series Analysis Without Data Science¶
- New Analytics Chart for interactive time-series analysis
- Built-in analytical methods:
- Linear, quadratic and cubic regression
- Piecewise regression with configurable breakpoints
- Smoothing techniques
- Quantile analysis
- Automatic identification of trends, seasonality, and pattern changes
- Residual analysis for deeper insight into deviations
- Time-series are automatically calculated for every dataset
Impact: Enables users to understand complex data behavior over time without requiring data science expertise or external tools.
Enumerations – Central Definition of Allowed Values¶
- Define reusable sets of allowed values (e.g., countries, states, status codes)
- Validate column values against predefined enumerations in digna Data Validation
- Reuse enumerations across projects and data sources
- Use enumerations everywhere via
#ENUM:MY_ENUM# - All check are executed directly in the source database
Impact: Ensures consistent and standardized data values across the organization.
Validation Rule Templates – Reusable Data Quality Logic¶
- Define reusable validation rules (e.g., whitespace checks, NOT NULL, format checks)
- Apply templates across multiple datasets
- Ensure consistent rule logic across projects
- Reduce duplication and manual configuration
- All check are executed directly in the source database
Impact: Enables scalable and high-performance data validation without data movement.
Statistic-Level Relevance Conditions¶
- Define relevance conditions on column level for each statistic
- Extends the concept of anomaly relevance conditions
- Control when a statistic should be considered relevant
- Reduce noise by excluding non-critical situations
Impact: Improves signal quality by focusing only on meaningful deviations.
Data Analytics と Data Validation の拡張機能¶
With this release, digna expands both data understanding and data validation standardization:
- Advanced time-series interpretation without data science knowledge
- Centralized definition of allowed values via enumerations
- Reusable validation logic via templates
- Fine-grained control over relevance of statistics and alerts
Together, these capabilities enable organizations to not only detect issues, but also understand, standardize, and control data quality.
Who Benefits from This Release¶
- Data Engineers: Reusable validation logic and improved control over monitoring behavior
- Data Quality & Governance Teams: Standardized rules and consistent data validation across systems
- Analytics & BI Teams: Better understanding of trends and deviations
- Platform Owners: Increased adoption through simplified analytics and scalable validation
CLI Updates¶
- 変更なし