FeedPrep for Pimcore
Pimcore's data model is powerful. Getting supplier data into it shouldn't require hours of manual reformatting. FeedPrep cleans and structures product data so it's ready for Pimcore import.
The Problem
Pimcore's flexible data model lets you define exactly the object structure your business needs. But suppliers don't send data that matches your class definitions. Getting data INTO Pimcore in the right format — with correct object structures, consistent values, and proper field mapping — is manual, repetitive work.
Mismatched Structures
You've designed clean Pimcore class definitions with specific field types, relations, and validation rules. Suppliers send flat CSVs with freeform text columns that don't match any of it.
Inconsistent Values
Your Pimcore selects and multi-selects expect exact enumeration values. Suppliers send "Red", "red", "RED", "Crimson" — none of which match the "red" option in your class definition.
Wrong Formats Everywhere
Quantity values arrive as "10 kg", "10kg", "10000g", or just "10" with the unit in a separate column. Pimcore's quantity value fields need a specific numeric format and unit reference.
What Pimcore Expects
To import cleanly into Pimcore, your data needs to meet specific requirements.
Structured Data Matching Your Class Definitions
Every field in your import file must correspond to a field in your Pimcore data object class. Field names, types, and nesting must align with your schema.
Consistent Enumeration Values
Select fields, multi-selects, and relation fields need exact value matches. One typo or casing difference and the import silently drops the value or fails entirely.
Correct Measurement Formats
Pimcore's quantity value fields require a numeric value paired with a defined unit. Freeform text like "5.2 kilograms" won't work — it needs to be structured as value and unit separately.
How FeedPrep Prepares Your Data
FeedPrep sits between your suppliers and Pimcore. It learns your Pimcore schema and transforms every supplier feed to match.
Define Your Pimcore Attribute Schema
Set up your target attributes in FeedPrep to mirror your Pimcore class definitions. Define which fields are required, what types they expect, and which values are valid.
Set External Codes Matching Pimcore Field Names
Each FeedPrep attribute gets an external code that maps directly to your Pimcore field name. When you export, the output uses these codes as column headers — ready for direct import.
Normalize Values to Match Pimcore Enumerations
Define approved values for each attribute. FeedPrep's transform rules automatically map supplier variants ("Rood", "RED", "Crimson") to the exact values your Pimcore selects expect ("red").
Measurement Normalization for Quantity Values
FeedPrep detects and converts measurement values. "5.2 kilograms", "5200g", "5,2 kg" all become a clean numeric value in your target unit — ready for Pimcore's quantity value fields.
Export CSV/JSON/XML Matching Pimcore Import Format
Generate export files in the exact format Pimcore's import expects. CSV for bulk data objects, JSON for API-based imports, or XML for structured hierarchies. Column names match your field names.
The Workflow
Upload supplier data in any format — CSV, XLSX, XML, JSON. FeedPrep parses it and shows you the raw data immediately.
Map supplier columns to your Pimcore attributes. FeedPrep suggests mappings based on column names. Set transform rules to normalize values to your enumerations.
Review the normalized data. FeedPrep highlights values that don't match your approved lists, missing required fields, and measurement conversion results.
Export in your chosen format. The file uses your Pimcore field names as headers and contains only clean, validated data. Import directly into Pimcore.
Same supplier, new file? Upload it and the saved adapter auto-applies all your mappings and rules. Review and export in minutes.
Key Features for Pimcore Users
External Codes
Map each FeedPrep attribute to the exact Pimcore field name using external codes. Your exports use these codes as column headers, so Pimcore's import recognizes every field without manual mapping on the Pimcore side.
Measurement Handling
Automatically parse freeform measurement values from suppliers and convert them to structured numeric values with standardized units. Output matches what Pimcore's quantity value fields expect.
JSON Export
Export clean product data as structured JSON for use with Pimcore's REST API or custom import scripts. Nested structures and typed values are preserved in the output.
Approved Values
Define valid enumeration values per attribute to mirror your Pimcore select options. FeedPrep flags any supplier value that doesn't match and helps you create transform rules to fix them.
Scheduled Exports
Set up recurring exports that run automatically. Combine with supplier feed monitoring to process new supplier data and push Pimcore-ready files on a schedule — no manual intervention needed.
Supplier Adapters
Save all mappings, transforms, and normalization rules per supplier. When they send their next file, everything applies automatically. Build your adapter library over time.
Clean Product Data for Pimcore — Without the Manual Work
Upload a supplier file. Map it to your Pimcore schema. Export a Pimcore-ready file in minutes.
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