Automated Schema Adaptation
We built a metadata-driven pipeline that dynamically detected and adapted to changing XML structures without manual code rewrites.
Case Study
Automated Legacy-to-ERP Data Migration
An organization moving to a new ERP needed to migrate a large volume of complex, frequently changing XML data from its legacy systems into the ERP's database, within a strict deadline and with a small team.
We replaced hand-written migration scripts with an automated, metadata-driven ETL pipeline that adapted to changing source structures, validated every record before loading, and could be re-run whenever needed.
The Challenge
Changing data, a fixed deadline
Scripts written by hand for each source format could not keep up with the volume and variety of the legacy data.
Unscalable Custom Scripts
Hand-coded developer scripts could not keep pace with constantly changing XML schemas.
Resource Constraints
The organization faced tight head-count limits and a rigid delivery window.
Data Quality Risks
Inconsistent structures and missing fields threatened to break the target ERP ingestion process.
The Solution
A pipeline that adapts instead of breaking
We designed the migration as a configurable pipeline, so changes in the source data meant updating metadata rather than rewriting code.
Metadata-Driven Field Mapping
Source-to-target mappings were defined as configurable metadata rather than code, so complex field mappings across many target tables could be reviewed and changed quickly.
Built-in Data Validation
Automated data quality checks flag anomalies, validate formats, and route bad data to error-handling feedback loops prior to target loading.
Reconciliation and Audit Trail
Every load is reconciled against its source, with record counts, rejected rows and transformation logs kept for review and sign-off.
Reusable Framework
A modular architecture allows the team to re-run and adjust iterative migrations easily, including after go-live.
Results
Delivered on time, ready to run again
On-Time Delivery
The migration met its strict deadline without schedule overruns.
Reduced Labor
The complex multi-table mapping was completed with a smaller-than-planned project team.
Trusted Data in the ERP
Validation and reconciliation before loading meant the new ERP went live with clean, consistent data.
Long-Term Reusability
A one-off migration script became a repeatable, configurable migration template for future rollouts.
Conclusion
By treating the migration as a reusable, metadata-driven pipeline rather than a one-off script, the organization moved its legacy data into the new ERP on schedule, with validated and reconciled data and a smaller team than planned. The same framework now supports future rollouts and iterative data loads, turning a high-risk, one-time project into a repeatable capability.
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