BigQuery SQL and pricing model in GCP - Mini Project: Build & Apply

BigQuery SQL and Pricing Model

📖 Scenario: You work as a data analyst for a retail company. Your manager wants you to analyze sales data stored in Google BigQuery. To keep costs low, you need to understand how BigQuery pricing works and write SQL queries that minimize data scanned.

🎯 Goal: Build a simple BigQuery SQL query to calculate total sales per product category, while considering the pricing model by filtering data efficiently.

📋 What You'll Learn

Create a table variable with sales data including product category and sales amount

Add a filter configuration variable to select a specific product category

Write a SQL query that sums sales amount grouped by product category using the filter

Add a LIMIT clause to restrict the number of rows returned

💡 Why This Matters

🌍 Real World

BigQuery is a cloud data warehouse where you run SQL queries on large datasets. Understanding how to write efficient queries and use filters helps reduce the amount of data scanned, lowering costs.

💼 Career

Data analysts and cloud engineers use BigQuery daily to analyze data. Knowing how to write filtered queries and apply limits is essential to optimize performance and control expenses.

1

Create sales data table variable

Create a BigQuery table variable called sales_data with these exact columns and values: product_category with values 'Electronics', 'Clothing', 'Books', and sales_amount with values 1000, 500, 300 respectively.

GCP

# Create the sales_data table variable with product_category and sales_amount columns
# Your code here

Hint

Use a list of dictionaries to represent the table rows with exact keys and values.

2

Add filter configuration variable

Add a variable called selected_category and set it to the string 'Electronics' to filter sales data by this category.

GCP

sales_data = [
    {'product_category': 'Electronics', 'sales_amount': 1000},
    {'product_category': 'Clothing', 'sales_amount': 500},
    {'product_category': 'Books', 'sales_amount': 300}
]
# Add the selected_category variable below
# Your code here

Hint

Assign the exact string 'Electronics' to the variable selected_category.

3

Write SQL query to sum sales by category with filter

Write a SQL query string variable called query that selects product_category and the sum of sales_amount as total_sales from sales_data, filtering where product_category equals selected_category, and grouping by product_category.

GCP

sales_data = [
    {'product_category': 'Electronics', 'sales_amount': 1000},
    {'product_category': 'Clothing', 'sales_amount': 500},
    {'product_category': 'Books', 'sales_amount': 300}
]
selected_category = 'Electronics'
# Write the SQL query string variable called query below
# Your code here

Hint

Use an f-string to insert the selected_category variable inside the WHERE clause.

4

Add LIMIT clause to SQL query

Add a LIMIT 1 clause at the end of the query string to restrict the number of rows returned to 1.

GCP

sales_data = [
    {'product_category': 'Electronics', 'sales_amount': 1000},
    {'product_category': 'Clothing', 'sales_amount': 500},
    {'product_category': 'Books', 'sales_amount': 300}
]
selected_category = 'Electronics'
query = f"""
SELECT product_category, SUM(sales_amount) AS total_sales
FROM sales_data
WHERE product_category = '{selected_category}'
GROUP BY product_category
"""
# Add LIMIT 1 clause to the query string below
# Your code here

Hint

Append the line LIMIT 1 at the end of the SQL query string.

Practice

1. What does BigQuery charge you for when you run a SQL query?

easy

A. The amount of data scanned by the query

B. The number of SQL commands used

C. The time it takes to write the query

D. The number of tables in your project

Solution

1. Step 1: Understand BigQuery pricing basics

   BigQuery charges based on how much data your query scans, not on commands or tables.

2. Step 2: Identify the correct pricing factor

   The cost depends on the volume of data scanned during query execution.

3. Final Answer:

   The amount of data scanned by the query -> Option A

4. Quick Check:

   Data scanned = cost [OK]

Hint: Remember: BigQuery bills by data scanned, not query length [OK]

Common Mistakes:

• Thinking cost depends on query complexity
• Assuming storage size affects query cost directly
• Confusing number of tables with cost

2. Which of the following is the correct way to write a simple BigQuery SQL SELECT statement?

easy

A. SELECT * FROM dataset.table;

B. GET * FROM dataset.table;

C. FETCH ALL FROM dataset.table;

D. READ * dataset.table;

Solution

1. Step 1: Recall BigQuery SQL syntax

   BigQuery uses standard SQL syntax where SELECT is used to retrieve data.

2. Step 2: Identify the correct syntax

   Only SELECT * FROM dataset.table; uses the correct SELECT statement format with FROM clause.

3. Final Answer:

   SELECT * FROM dataset.table; -> Option A

4. Quick Check:

   SELECT + FROM = correct query [OK]

Hint: SELECT is the keyword to get data in SQL [OK]

Common Mistakes:

• Using GET or FETCH instead of SELECT
• Omitting FROM keyword
• Incorrect command verbs

3. Consider this query:
SELECT COUNT(*) FROM `project.dataset.sales` WHERE sale_date >= '2023-01-01';
What will this query return?

medium

A. A list of all sales records from 2023-01-01 onwards

B. The sum of all sales amounts after 2023-01-01

C. The total number of sales records from 2023-01-01 onwards

D. An error because of incorrect date format

Solution

1. Step 1: Analyze the SELECT clause

   COUNT(*) counts the number of rows matching the condition.

2. Step 2: Understand the WHERE clause

   It filters records with sale_date on or after 2023-01-01.

3. Final Answer:

   The total number of sales records from 2023-01-01 onwards -> Option C

4. Quick Check:

   COUNT(*) + WHERE = count filtered rows [OK]

Hint: COUNT(*) counts rows, not sums values [OK]

Common Mistakes:

• Confusing COUNT with SUM
• Thinking it returns all records instead of count
• Assuming date format causes error

4. You wrote this query but it fails:
SELECT * FROM dataset.table WHERE date = 2023-01-01;
What is the error and how to fix it?

medium

A. Missing semicolon; add ; at end

B. Date value missing quotes; fix with WHERE date = '2023-01-01'

C. Table name incorrect; use backticks around table

D. SELECT * not allowed; specify columns explicitly

Solution

1. Step 1: Identify the error in WHERE clause

   Date literals must be in quotes in SQL; missing quotes cause syntax error.

2. Step 2: Correct the date format

   Enclose date in single quotes: '2023-01-01' to fix the query.

3. Final Answer:

   Date value missing quotes; fix with WHERE date = '2023-01-01' -> Option B

4. Quick Check:

   Date literals need quotes [OK]

Hint: Always quote date strings in SQL WHERE clauses [OK]

Common Mistakes:

• Forgetting quotes around dates
• Assuming semicolon fixes syntax
• Thinking SELECT * is invalid

5. You want to reduce BigQuery costs by limiting data scanned. Which approach is best?

hard

A. Use SELECT * to get all columns for flexibility

B. Run queries without filters to scan all data quickly

C. Store data in multiple small tables instead of one large table

D. Use partitioned tables and filter on partition column in queries

Solution

1. Step 1: Understand cost drivers in BigQuery

   Costs depend on data scanned; scanning less data reduces cost.

2. Step 2: Identify cost-saving technique

   Partitioning tables lets queries scan only relevant partitions, saving cost.

3. Final Answer:

   Use partitioned tables and filter on partition column in queries -> Option D

4. Quick Check:

   Partition + filter = less data scanned [OK]

Hint: Partition tables and filter to scan less data [OK]

Common Mistakes:

• Thinking scanning all data is cheaper
• Splitting tables without partitioning helps cost
• Using SELECT * increases scanned data