DataTerminology.comNEW
Specifying correspondences between source and target schemas to enable transformation, integration, and query translation.
DESCRIPTION
Schema mapping formalizes how elements in one data model relate to another for integration. Mappings drive ETL pipelines, API mediation, and cross-system interoperability. Techniques range from manual mapping to automated generation via schema matching. Standards like R2RML express mappings from relational to RDF. Clear mappings reduce ambiguity and support governance and lineage.
ALTERNATIVE DEFINITIONS
Wikipedia
Data mapping is the process of creating data element mappings between two distinct data models.
Concise, general mapping definition across models.
W3C
R2RML is a language for expressing customized mappings from relational databases to RDF datasets.
Formal standard for relational-to-RDF mappings.
Talend
Data mapping defines how fields in source structures correspond to fields in target structures, enabling transformation and integration.
Vendor tooling perspective on field-level mappings.
RELATED TERMS
Data Integration Extract-Transform-Load Data Transformation Vocabulary Alignment Interoperability Entity Resolution Master Data Management R2RML
COMPETING TERMS
Schema Matching Data Wrangling
KNOWLEDGE AREAS
Data Integration & Interoperability Metadata Management Data Management Semantic & Knowledge Modeling
TARGET AUDIENCE
Data Engineer Data Architect Data Steward Information Analyst
EXTERNAL SOURCES
- Wikipedia - Data mapping
- W3C - R2RML mapping standard
- IBM - Overview of data mapping
- Talend - Definition and examples
TOP QUESTIONS
Why is Schema Mapping essential in integration?
It ensures data fields and semantics are aligned across systems, enabling accurate transformation, interoperability, and governance.
Category: Relevancy
How does Schema Mapping differ from Schema Matching?
Mapping specifies correspondences; matching discovers them—often automatically—based on structure, names, and data.
Category: Misunderstanding
What is a common mistake in Schema Mapping?
Ignoring semantic meaning and relying solely on field names can produce incorrect transformations.
Category: Mistakes
What risks exist in Schema Mapping projects?
Ambiguous mappings lead to data loss, duplicates, or misinterpretation, harming downstream analytics and compliance.
Category: Risk
How do you document Schema Mapping effectively?
Use mapping specifications, examples, edge cases, and lineage, and store them in a catalog for reuse.
Category: BestPractice
Which standards help with Schema Mapping?
R2RML for relational-to-RDF, JSON Schema for validation, and DCAT for dataset metadata alignment assist mapping.
Category: Requirements
What tools support Schema Mapping?
ETL platforms, integration suites, and semantic mapping tools provide graphical and programmatic mapping capabilities.
Category: Implementation
How do you test a Schema Mapping?
Create test datasets, verify transformed outputs, and compare semantics against target models and business rules.
Category: Testing
Where should I start learning Schema Mapping?
Begin with data modeling basics, then study mapping patterns and standards like R2RML.
Category: Learning