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Course Outline

BigQuery

Cloud Databases & Data Pipelines

Data Pipelines

AI Tools in GCP

Use Cases of Machine Learning (ML)

BigQuery

Last Updated: 10th September, 2026

Imagine you run an e-commerce company

Every day you collect massive data:

Customer orders
Payments
User activity

Now you want to answer questions like:

Which product sells the most?
Which city has the highest customers?
What are today’s total sales?

But your data is huge millions of rows

Traditional systems struggle

Now you use BigQuery…

Queries run in seconds
Handles massive data
No server management

That’s the power of BigQuery

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What is BigQuery?

BigQuery is a serverless data warehouse in GCP used to:

Store large datasets
Analyze data using SQL
Run fast queries on big data

No infrastructure management required

Key Features of BigQuery

Serverless → No setup needed
Fast Queries → Process TBs of data quickly
Scalable → Handles huge datasets
SQL Support → Easy to use
Cost Efficient → Pay per query

BigQuery Structure

Component	Description
Project	Top-level container
Dataset	Collection of tables
Table	Structured data
Row	Record
Column	Field

Technical Example (SQL Query)

SELECT product_name, COUNT(*) AS total_sales
FROM my_dataset.orders
GROUP BY product_name
ORDER BY total_sales DESC;

How BigQuery Works

Load data into tables
Write SQL queries
BigQuery processes data
Get results instantly

1. Business Analytics

Analyze sales trends
Customer behavior

2. Marketing Insights

Campaign performance
User segmentation

3. Financial Analysis

Revenue tracking
Risk analysis

4. IoT Data Processing

Analyze sensor data
Real-time insights

Module 5: Data & Analytics

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