Data Modeling in Excel

The main interface for working with NEAT is through a Python notebook environment. To get more granular control over the data modeling process, you can use the Excel interface of a data model either to modify an existing data model or to create a new one. The NeatSession objects provides methods for reading and writing data models to Excel files.

from cognite.neat import NeatSession, get_cognite_client

client = get_cognite_client(".env")

neat = NeatSession(client)

# Reading an existing data model
neat.read.excel("path/to/excel/file.xlsx")


# Writing a data model to an Excel file
neat.to.excel("path/to/excel/file.xlsx")

Neat data models comes in two flavors, logical (information) and physical (dms). The logical data model is the semantic data model, it is a description of how the different concepts relate to each other and what properties they have. The physical data model specifies how the data model is implemented in Cognite Data Fusion (CDF), specifically in the Domain Modeling Service (DMS).

These two data models are intended for different user personas, the logical data model is intended for domain experts/ information architects, while the physical data model is intended for DMS/CDF architects.