Why BigQuery SQL and pricing model in GCP? - Purpose & Use Cases

The Big Idea

What if you could ask any question to huge data instantly and only pay for what you actually use?

The Scenario

Imagine you have a huge pile of data stored in many spreadsheets on your computer. To find answers, you open each file, search for the right info, and add up numbers by hand.

The Problem

This manual way is slow and tiring. You might make mistakes copying numbers or miss some files. If the data grows bigger, it becomes impossible to handle without errors or delays.

The Solution

BigQuery SQL lets you ask questions to all your data at once using simple commands. It automatically scans only what you need and charges you based on the amount of data processed, saving time and money.

Before vs After

✗ Before

Open file1.xlsx
Search for sales > 1000
Add numbers manually

✓ After

SELECT SUM(sales) FROM dataset.table WHERE sales > 1000

What It Enables

You can quickly analyze massive data sets without waiting or worrying about complex setups, paying only for what you use.

Real Life Example

A retail company uses BigQuery SQL to instantly find which products sold best last month across all stores, helping them decide what to stock next.

Key Takeaways

Manual data handling is slow and error-prone.

BigQuery SQL simplifies querying large data efficiently.

Pricing based on data scanned helps control costs.

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