Google Cloud BigQuery¶

Getting Started¶

To use Google Cloud BigQuery as a data source, you will need to install the google-cloud-bigquery Python package. You can install this package using pip:

pip install google-cloud-bigquery db-dtypes

Authentication¶

Application Default Credentials (Recommended)¶

The easiest way to authenticate with Google Cloud BigQuery is to use Application Default Credentials. If you are running marimo on Google Cloud and your resource has a service account attached, then Application Default Credentials will automatically be used. If you are running marimo locally, you can authenticate with Application Default Credentials by running the following command:

gcloud auth application-default login

Service Account Key File¶

To authenticate with Google Cloud BigQuery, you will need to create a service account and download the service account key file. You can create a service account and download the key file by following the instructions here.

Once you have downloaded the key file, you can authenticate with Google Cloud BigQuery by setting the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the key file:

export GOOGLE_APPLICATION_CREDENTIALS=/path/to/key/file.json

Reading Data¶

To read data from Google Cloud BigQuery, you will need to create a BigQueryClient object. You can then use this object to read data from Google Cloud BigQuery.

# Cell 1 - Load libraries
import marimo as mo
from google.cloud import bigquery

# Cell 2 - Load datasets
client = bigquery.Client()
datasets = list(client.list_datasets())

# Cell 3 - Select dataset
selected_dataset = mo.ui.dropdown(
    label="Select dataset", options=[d.dataset_id for d in datasets]
)
selected_dataset

# Cell 4 - Load tables
dataset = client.dataset(selected_dataset.value)
tables = list(client.list_tables(dataset))
selected_table = mo.ui.dropdown(
    label="Select table", options=[t.table_id for t in tables]
)
selected_table

# Cell 5 - Load table data
results = client.list_rows(dataset.table(selected_table.value), max_results=10)
mo.ui.table(results.to_dataframe(), selection=None)

Example¶

Check out our full example using Google Cloud BigQuery here

Or run it yourself:

marimo run https://raw.githubusercontent.com/marimo-team/marimo/main/examples/integrations/google_cloud_bigquery.py