About LEDS

Prev Next

The Logical Entity Data Service (LEDS) lets you work with data stored in Zilliant logical entities. Think of a logical entity as a database table, where each record is a row and each field is a column.

Use LEDS when you need to read, add, update, or remove entity records through an API, for example, to integrate Zilliant with another system, automate data maintenance, or validate data at scale.


Key concepts

  • Entity—Zilliant data source that behaves like a table. You address it by entitySystemName.

  • Field system name—Developer-friendly field name used in requests and responses, for example, UOMId, ConversionFactor.

  • Record—Single row in an entity. LEDS represents records as JSON objects in the format fieldSystemName: value.

  • Primary key—Fields that uniquely identify a record in an entity. You must include primary key fields when you update or delete records.

Tip

Your Zilliant representative can provide the full list of entities and fields available in your environment.


What you can do with LEDS

Read entity data

Use GET List records in a logical entity to retrieve records from an entity. You can control what you get back by using query parameters, including OData system query options such as $select or $filter.

Typical scenarios:

  • Fetch product or customer data for analytics, validation, or integration

  • Find records matching a condition, for example, records with missing cost

  • Retrieve a top-N list, for example, products with the largest quantities

Insert records

Use POST Insert or delete entity records (without the delete parameter) to insert records.

Typical scenarios:

  • Add new UOMs, products, or attribute values

  • Seed master data during implementation

  • Load a small batch of maintenance records

Update records

Use PATCH Update entity records to modify existing records.

Typical scenarios:

  • Correct conversion factors

  • Toggle flags such as hidden or active

Delete records

Use POST Insert or delete entity records (with thr delete parameter) to remove records from an entity.

Typical scenarios:

  • Remove invalid or obsolete records

  • Clean up test data after validation