Use this to generate fake data in your dbt pipelines as an alternative to dbt seeds with csv files: https://www.loom.com/share/90084f27396746619d4f53f44143faab

fake_data_example.py

from faker import Faker
import pandas as pd

fake = Faker()

def create_rows_faker(num=1):
    output = [{"name":fake.name(),
                   "address":fake.address(),
                   "name":fake.name(),
                   "email":fake.email(),
                   #"bs":fake.bs(),
                   "city":fake.city(),
                   "state":fake.state(),
                   "date_time":fake.date_time(),
                   #"paragraph":fake.paragraph(),
                   #"Conrad":fake.catch_phrase(),
                   "randomdata":100} for x in range(num)
                   ]
    return output


def model( dbt,_):
    dbt.config(
        materialized='table',
        packages = ['Faker'] # how to import python libraries in dbt's context
    )

    df = pd.DataFrame(create_rows_faker(num=100))
    return df