MDIS Data Management is an enterprise ETL (Extract, Transform, Load) and data integration solution developed by Paspara for SAS-based data environments. Its metadata-driven architecture simplifies the design, development, execution, and maintenance of complex data integration processes.
Unlike traditional ETL approaches that rely on manually written code or static workflows, MDIS allows developers to define data structures, transformation logic, validation rules, historization requirements, and process dependencies through configurable metadata. The platform uses this metadata to generate and execute data processing logic automatically.
Key capabilities include:
- Metadata-driven ETL: Define data processing requirements through reusable metadata instead of developing and maintaining complex ETL code.
- Automated process orchestration: Determine execution order automatically based on dependencies between data processing tasks.
- Data quality management: Apply configurable data validation rules to improve the reliability and consistency of integrated data.
- Data historization: Track and manage changes in data over time to support historical analysis and reporting.
- Optimized performance: Leverage generated SAS processing logic, historical execution statistics, and parallel processing to efficiently manage large data volumes.
- Operational monitoring and recovery: Track execution, validation, and change activity through centralized registers, with support for failure recovery.
- SAS platform compatibility: Operate within SAS 9, SAS Viya 3.5, and SAS Viya 4 environments.
MDIS supports enterprise data warehousing, analytical data preparation, data quality management, and integration across heterogeneous source systems.
The solution is particularly relevant to organizations managing complex, large-scale data environments, including financial institutions, insurance companies, and public-sector organizations.
By replacing repetitive manual ETL development with metadata-driven automation, MDIS helps organizations accelerate delivery, simplify maintenance, reduce operational complexity, and improve data reliability.
