I often grab data for a Vertex AI instance from BigQuery and shape it into a dataframe to do work. Minimum viable snippet to get that to work: import pandas as pd from google.cloud import bigquery client = bigquery.Client() query_string = "<some SQL query>" df = client.query(query_string).to_dataframe()
I often find it useful to stash .csv files in a Google Cloud Storage (GCS) bucket to be accessed from a Vertex JupyterLabs notebook. Once the file(s) are accessible from Vertex, I can do all sorts of things with it. One of the most common operations I perform in this scenario is to use the data in the .csv to create a Pandas dataframe that can in turn be used to enrich other data that I have stored in a BigQuery implementation. ...