Use case: Churn project with Ibis library on Google cloud platform (GCP)
Suppose an advanced analytics team is working on a new pipeline for their marketing department. They want to receive daily metrics on customer churn rate for key customer segments in order to better manage their anti-churn campaign. First, the analytics team is working on a minimal viable product version using the Ibis library in Jupyter notebooks on Vertex AI (Google/user-managed VMs with pre-installed Jupyterlab) where they have stored data locally.
Once the pipeline has achieved the quality necessary for production mode, it is sufficient to replace the connection to the local data source with the corresponding tables in the data warehouse – Big Query in this case. Quick and easy transfer of the pipeline using the Ibis library allows the team to add value for the marketing department faster.
Illustration of Ibis Code first tested locally and then executed in production mode involving a DWH
So much for the basics. In the second part of this blog series, I'll explain how you can set up Ibis on GCP Vertex AI. In addition, I will use an example to show how easily a pipeline can be converted for transfer from a local data source to a DWH with Python Ibis.