Get BigQuery Table Metadata using Python

In this tutorial, we will learn how to access detailed metadata for a BigQuery table using Python. This metadata includes table schema, creation time, row count, and more.

Python Code to Get Table Metadata

We will use the get_table method from the google-cloud-bigquery library.

Python

# Import the packages
from dotenv import load_dotenv
import os
from google.oauth2 import service_account
from google.cloud import bigquery

def main():
    # Loads environment variables from a .env file
    load_dotenv()

    # Use environment variables from .env file
    credentials_path = os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
    project_name = os.getenv("project_id")

    # Get the service account credentials
    credentials = service_account.Credentials.from_service_account_file(credentials_path)

    # Create the BigQuery client
    client = bigquery.Client(credentials=credentials, project=project_name)

    # dataset_id = 'your-project.your_dataset'
    table_id = 'ashishcoder.ds_from_tf.table_from_tf'

    # Get the table object
    table = client.get_table(table_id)

    # Print table metadata
    print(f"Table ID: {table.table_id}")
    print(f"Full Table Name: {table.full_table_id}")
    print(f"Table Description: {table.description}")
    print(f"Table Creation Time: {table.created}")
    print(f"Table Row Count: {table.num_rows}")
    print(f"Project name: {table.project}")
    print(f"Table Schema: {table.schema}")
    print(f"Table Schema (First field): {table.schema[0].name}")

if __name__ == "__main__":
    main()

Key Metadata Properties

table.created: The date and time the table was created.
table.num_rows: The total number of rows currently in the table.
table.schema: A list of SchemaField objects representing the table structure.

Note: Metadata is useful for validating table structures before running automated data pipelines.

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