Where to Start

pygen has multiple use cases spanning different user skill levels. This section will help you find the right place to start.

The minimum requirements for using pygen is to have a Cognite Data Fusion (CDF) project available. Note that pygen is made for people with a very basic understanding of Python, but instead have domain knowledge of the data they are exploring and analyzing.

For the more advanced users, pygen is a great tool for building solutions quickly by tailoring the generated code to your data model. Thus making it easy to get data in a format that is close to work with for your specific use case. It is an alternative to graphql that is more pythonic.

(Beginner) Exploration

If you are just curious about pygen and all you have is a CDF project, and you don't eve have a data model or data a great place to start is in the CDF notebook with a demo model packaged into pygen, see Generating SDK using Demo Data Model. The advantage of using a CDF notebook is that you do not have to know how to install and setup Python.

Do you have your own data model you want to explore using the CDF notebook, then see (Explore in Notebook)[cdf_notebook.html]

If you know how to install and setup Python and you have a CDF project, and prefer to work in your own environment, then you can start with the Local Notebook guide.

Exploration is also useful in the data modeling process to quickly explore a data model and, for example, try how it can be queried, and whether it will support a specific use case.

(Intermediate) Building a Solution

If you have a CDF project and you want to build a solution, for example, a dashboard using plotly or dash, or a machine learning model workflow using scikit-learn or tensorflow, then you should generate a pygen SDK and check it into git history. See the Project for more information.

(Intermediate) Ingestion Data into CDF

pygen can also be used to create an extractor that is used to ingest data into CDF. The typical use case is when you have a data source that is creating nested data and you want to perform client side validation of the data before ingesting it into CDF. See the Data Population for more information.

(Intermediate) Migration Data between Data Models

pygen can also be used to move data from one model to another. In short, you create an SDK using pygen for each data model, and then write the code to move data between the models. See the Data migration for more information.