Akeneo Shared Catalogs Documentation

The Product Grid is the central workspace in Akeneo PIM for browsing, filtering, and managing the product catalog. It provides a unified view of products and product models across different channels and locales. Key capabilities include:

• Searching and filtering by attributes, categories, labels, or identifiers.
• Navigating category trees to browse catalog subsets.
• Configuring table columns and sorting.
• Performing bulk actions on selected products.
• Inline editing of Boolean attribute values.
• Switching channel and locale contexts to view localized data.
• Working within saved Views or Projects.
• Creating new products or product models.

Dependencies are rules that establish relationships between product attributes and properties to ensure data quality and governance. They allow administrators to control the product enrichment process by:

• Controlling the visibility and display of attributes based on conditional logic.
• Restricting or allowing the enrichment of specific attributes, which in turn affects the product completeness score.
• Restricting the list of allowed values for an attribute based on specific conditions.

The Akeneo Event Platform enables 'Push' technology for real-time synchronization between Akeneo PIM and SAP Commerce Cloud. Unlike standard 'Pull' (Batch/Delta) synchronization, which cannot effectively process deletions because deleted products simply disappear from the data flow, the Event Platform sends instantaneous notifications to SAP when a deletion occurs.

Supported Out-of-the-Box Events: The SAP Commerce Cloud Accelerator is pre-configured to handle 'Hard Delete' actions for:

• Products
• Product Models
• Categories

Standard updates (creation/modification) are still handled via Full or Delta synchronization jobs because they require more complex contextual data and composite keys than the Event Platform payloads provide.

Akeneo PIM allows users to extract product information and catalog entities (Families, Categories, and Attributes) for third-party platforms or internal use. Data is converted into specific formats, supporting CSV and XLSX files. There are two primary delivery methods:

1. Manual download: The user executes the job via the PIM UI and manually downloads the resulting file.
2. Automatic delivery: A file path is configured in the export profile, and the PIM automatically drops the file upon job completion.

Tailored Import is a profile available to SaaS users (Enterprise and Growth Edition) that allows the import of product information from XLSX or CSV files with structures that differ from standard PIM requirements. It eliminates the need to manually modify spreadsheets to match PIM attribute codes or labels before importing.

Current Limitations:

• Unsupported attribute types: Table, File, and Image.
• Unsupported system properties: Parent, Family variant, Groups, and Associations.
• Media files import is not currently supported.

The SAP ERP integration with Akeneo PIM package automates the bulk (Batch) export of product data from SAP S/4HANA to Akeneo PIM using OData for extraction and the Akeneo REST API for loading. The process is split into three primary iFlows:

1. Import in Batch Data from SAP ERP to Akeneo PIM: The entry point where data collection logic is implemented to retrieve product master data from SAP.
2. Mapping SAP ERP fields to Akeneo PIM attributes: The transformation layer where source fields from SAP are mapped to the target Akeneo PIM structure.
3. Orchestrate Data in Batch: The process manager that handles PIM token management, determines if a product requires creation or update, and executes the final push to the PIM API.

The Match History module in Supplier Data Manager (SDM) allows users to manage products using a persistent Reference Base. It supports three primary tasks: filtering products (automatically or manually), enriching products by joining them with a Reference Base to add or override attributes, and importing products into a Reference Base for future use.

Key technical details:

• Filtered-out products are stored in a dataframe called matched, which can be targeted by an Output Format to export skipped products.
• Matching only occurs on non-empty cells; two empty values are treated as different.