I want to share our successful implementation of bulk supplier data migration to Aras 13.0 using custom ETL scripts. We needed to onboard 2,500 suppliers from multiple legacy ERP systems with inconsistent data formats.
Challenge: Different source systems had varying data quality-some had complete supplier profiles with certifications and contact hierarchies, others just had basic name and address info. Manual data entry would have taken months and introduced errors.
Solution: We built a Python-based ETL pipeline using pandas for data transformation and the Aras REST API for bulk import. The key was implementing comprehensive data validation rules before attempting any imports.
Our ETL script handled:
- Data normalization (standardizing country codes, phone formats, etc.)
- Duplicate detection across source systems
- Validation against business rules (required fields, format checks)
- Automatic classification based on supplier type
- Bulk import with error logging
Results: Successfully migrated all 2,500 suppliers in 3 days (including validation and error resolution). The automation reduced onboarding time from an estimated 4 months of manual work to under a week. Data quality improved significantly with automated validation catching issues that manual entry would have missed.
The ETL approach also gave us reusable scripts for ongoing supplier onboarding, which has been a huge win for the procurement team.
Using pandas for data transformation is smart-it handles large datasets efficiently. Did you run into any memory issues with 2,500 suppliers, or was that small enough to process in-memory? Also interested in how you structured the error logging. When bulk imports fail, having detailed logs is essential for troubleshooting, but I’ve seen logging implementations that generate too much noise and make it hard to identify real issues.
The Aras REST API is definitely the right choice for bulk imports in 13.0-much better performance than the SOAP API from earlier versions. One thing to watch out for: rate limiting and connection pooling. If you’re importing 2,500 suppliers with complex relationships, you might hit API throttling. Did you implement any batching strategy or retry logic in your ETL script?
The business rule validation aspect is critical. We tried a similar bulk import without proper validation and ended up with incomplete supplier records that caused issues downstream in RFQ processes. Can you share more details about what validation rules you implemented? Specifically, how did you handle optional vs. required fields given that your source systems had varying data completeness?
The time savings are impressive-4 months to 1 week is a massive improvement. From a change management perspective, how did you handle the data review and approval process? Even with automated validation, I assume procurement stakeholders wanted to review the migrated data before it went live in Aras. Did you build any preview/staging functionality into your ETL pipeline?