Mahaaya Data Mapper
by Mahaaya LLC
source to target data mappings, schema mapping and validation, data transformation and migration
Bring any data into any target schema with faster onboarding and validated, reliable mappings.
Overview
Bringing data from different source systems into a consistent target schema can take significant manual effort, especially when organizations need to understand source structures, map fields, define transformation rules, and validate mappings.
Mahaaya Data Mapper simplifies this process by detecting source structures, comparing them with a target schema, and recommending source to target mappings with confidence scores. Our experts can review, refine, and validate mappings before generating reusable pipeline ready code.
Built for modern data onboarding and integration, the accelerator helps reduce repetitive mapping effort, improve consistency, and move faster from source data to a governed target schema.
Key Capabilities
- Source structure detection
- Source to target schema mapping
- Support for structured and semi structured data sources
- Target schema definition and import
- Confidence scored mapping recommendations
- Exact and inferred field matching
- Accept, reject, and refine mapping controls
- Default and missing value handling
- Transformation rule definition
- Mapping validation
- Parquet output support
- Python and notebook code generation
- Reusable mapping configurations
- Save and activate mapping configurations
- Version history and traceability
From Source to Target Schema
Mahaaya Data Mapper brings the mapping flow together in one accelerator:
Detect → Define → Map → Review → Transform → Generate → Activate
This helps organizations move from diverse source data to validated, reusable target mappings in a more consistent and repeatable way.
Built for Faster Data Onboarding
Mahaaya Data Mapper is suited for organizations looking to:
- Bring data from multiple sources into standardized target schemas
- Accelerate onboarding of new data sources
- Reduce manual source to target mapping effort
- Standardize mappings across data feeds
- Handle differences between source and target structures
- Apply consistent transformation and default value rules
- Validate mappings before pipeline development
- Generate reusable transformation logic
- Support data integration and modernization initiatives
- Maintain mapping history and traceability
Why Mahaaya for Data Mapper?
Mahaaya Data Mapper combines intelligent recommendations with expert review, reusable configurations, and code generation to support faster and more reliable data onboarding.Its advantages include:
· Validated, Reliable Mappings
Mahaaya Data Mapper makes mapping decisions visible through confidence scores and review controls.
Our experts can review suggested mappings, refine ambiguous fields, validate transformation rules, and help ensure that approved mappings are ready for downstream pipeline development.
· Reusable and Versioned Configurations
Approved mapping configurations can be saved, activated, reused, and versioned as source systems, business rules, or target schemas evolve.
This helps organizations maintain a more consistent and traceable data onboarding process over time.
· Pipeline Ready Outputs
Once mappings and transformation rules are approved, Mahaaya Data Mapper can generate reusable transformation logic, including Python and notebook code.
Get Started