Joining tables is a core SQL operation that allows you to combine data from multiple tables based on related columns. In Google BigQuery, JOIN is used to bring together datasets stored across different tables or even datasets. This tutorial explains how to use various types of JOIN operations in BigQuery using Python.

Table of Contents

1. What Is a SQL JOIN?

2. BigQuery JOIN Syntax

3. Types of JOINs in BigQuery

4. Using JOIN in Python BigQuery Queries

5. Full Working Example

6. Tips and Best Practices

7. Common Pitfalls

8. Conclusion

What Is a SQL JOIN?

A JOIN in SQL is used to combine rows from two or more tables based on a related column between them, such as a user_id or order_id.

BigQuery JOIN Syntax

SELECT a.column1, b.column2
FROM `project.dataset.tableA` AS a
JOIN `project.dataset.tableB` AS b
ON a.common_field = b.common_field

Types of JOINs in BigQuery

Using JOIN in Python BigQuery Queries

Step 1: Install BigQuery Client

pip install google-cloud-bigquery

Step 2: Setup and Write a JOIN Query in Python

from google.cloud import bigquery
import os

# Authenticate
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/your-key.json"
client = bigquery.Client()

# INNER JOIN query example
query = """
    SELECT
        c.customer_id,
        c.name AS customer_name,
        o.order_id,
        o.amount
    FROM `my-project.sales.customers` AS c
    JOIN `my-project.sales.orders` AS o
    ON c.customer_id = o.customer_id
    ORDER BY o.order_date DESC
    LIMIT 10
"""

# Run the query
query_job = client.query(query)
results = query_job.result()

# Print results
for row in results:
    print(f"{row.customer_id} - {row.customer_name} - {row.order_id} - ${row.amount}")

Examples of Different JOIN Types

INNER JOIN

SELECT a.id, b.score
FROM `project.dataset.users` AS a
JOIN `project.dataset.scores` AS b
ON a.id = b.user_id

Returns records only where there is a match in both users and scores.

LEFT JOIN

SELECT a.id, b.score
FROM `project.dataset.users` AS a
LEFT JOIN `project.dataset.scores` AS b
ON a.id = b.user_id

Returns all users and their scores, even if some users don’t have scores (NULL will appear).

FULL OUTER JOIN

SELECT *
FROM `project.dataset.users` AS a
FULL JOIN `project.dataset.scores` AS b
ON a.id = b.user_id

Returns all records from both tables, including unmatched rows with NULLs.

Full Working Python Example: LEFT JOIN

from google.cloud import bigquery
import os

# Auth
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/your-key.json"
client = bigquery.Client()

# LEFT JOIN to show customers even if they haven't made orders
query = """
    SELECT
        c.customer_id,
        c.name AS customer_name,
        o.order_id,
        o.amount
    FROM `my-project.sales.customers` AS c
    LEFT JOIN `my-project.sales.orders` AS o
    ON c.customer_id = o.customer_id
    ORDER BY customer_name
    LIMIT 20
"""

query_job = client.query(query)
results = query_job.result()

for row in results:
    print(f"{row.customer_id} - {row.customer_name} - {row.order_id or 'No order'} - ${row.amount or 0}")

Tips and Best Practices

⚠️ Common Pitfalls

Conclusion

Using JOIN operations in BigQuery with Python enables you to analyze relational data at scale, such as combining users with their activity logs, transactions, or demographics.

Key Takeaways:

• Use the correct JOIN type for your need (INNER, LEFT, FULL, etc.)

• Always qualify your table and column names

• Handle NULLs gracefully, especially in outer joins

• Combine JOIN with LIMIT, ORDER BY, and WHERE for efficient, readable queries