Data Mapping is the process of linking or matching data fields from one source to another. It ensures that data from different systems, databases, or formats can be accurately transferred, integrated, or transformed.
Key Aspects: Source identification, target definition, field mapping, transformation rules, validation, and execution — all working together to ensure data lands exactly where it should.
Business Use Cases
Data Migration
Transferring data from an old system to a new one seamlessly.
Data Integration
Merging data from multiple sources into a single unified system.
ETL / ELT Processes
Preparing and transforming data for analysis pipelines.
Data Synchronization
Keeping records updated and consistent across different platforms.
Data Migration is the process of transferring data from one system, storage, or format to another. It ensures that data remains accurate, consistent, and usable during the transition.
Types: Storage migration, database migration, application migration, cloud migration, and business process migration — each requiring careful planning and validation.
Key Steps in Data Migration
- Planning & Assessment — Understanding source and target systems, defining goals.
- Data Extraction — Pulling data from the source system.
- Data Cleansing & Transformation — Removing errors, standardizing formats.
- Data Mapping — Aligning data fields between source and target systems.
- Script Migration — Adapting scripts and code for the target database.
- Testing & Validation — Ensuring accuracy and integrity after migration.
- Deployment & Monitoring — Implementing migration with minimal disruption.