The Challenge
Synchronizing spatial data across three enterprise platforms is not a standard ETL problem. Spatial data has geometry, projections, and topology rules that must be preserved across transformations.
Schema Design First
Before writing any pipeline code, design the intermediate schema. This schema is your contract between source and target systems.
Audit Tables
Every pipeline step writes to an audit table. The audit table records:
- Source record ID
- Target record ID
- Transformation applied
- Timestamp
- Status (success, failure, skipped)
- Error message (if any)
This audit trail is your debugging tool. When data discrepancies arise, the audit table tells you exactly what happened.
Data Integrity Validation
After each transformation step, validate the data. For spatial data, this means:
- Geometry is valid (no self-intersections)
- Projection is correct
- Attributes match expected schema
- No null values in required fields
def validate_geometry(record):
geom = record['geometry']
if geom is None:
raise ValidationError(f"Null geometry for record {record['id']}")
if not geom.is_valid:
raise ValidationError(f"Invalid geometry for record {record['id']}")
return True
Branched Versioning
For multi-user editing, branched versioning is essential. Oracle SDE supports this natively. Each user works on a branch, and changes are reconciled to the default version.
This prevents edit conflicts and provides a rollback mechanism. If a user introduces bad data, you can revert their branch without affecting others.
Performance
Spatial queries are expensive. Index your geometry columns. Use spatial indexes (GIST in PostgreSQL, spatial indexes in Oracle). Batch your transformations to avoid per-record overhead.
Conclusion
Spatial ETL is harder than standard ETL, but the principles are the same: design schemas first, audit everything, validate at every step, and index for performance.