Data Pipelines

Imagine your app generates data every second

• Users click buttons
• Orders are placed
• Logs are created

But raw data is messy
It’s scattered, unorganized, and hard to use

Now you build a system that:

• Collects data automatically
• Cleans and processes it
• Sends it to the right place

Everything becomes structured and useful

That system is called a Data Pipeline

What is a Data Pipeline?

A Data Pipeline is a system that:
Moves data from source → processing → destination

It ensures data is:

• Clean
• Organized
• Ready for analysis

Types of Data Pipelines

1. Batch Processing

• Processes data in chunks
• Example: Daily reports

2. Real-Time (Streaming)

• Processes data instantly
• Example: Live notifications

Pipeline Flow (ETL Process)

GCP Tools for Data Pipelines

Example Workflow

1. User action triggers event
2. Event sent to Pub/Sub
3. Dataflow processes data
4. Stored in BigQuery
5. Query for insights

Real-Life Examples / Use Cases

1. E-commerce Analytics

• Track user behavior
• Generate sales reports

2. Real-Time Notifications

• Send alerts instantly

3. Banking Systems

• Process transactions in real-time

4. Social Media Platforms

• Analyze user activity instantly