Data Pipelines 🔄 (From Code to Production Systems)

Data pipelines are how raw data becomes usable business value.

If Spark Internals tells you how computation happens, then Data Pipelines tell you:

🧠 “How data flows through real production systems.”

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🔥 Why Data Pipelines Matter

In real companies, data is not processed manually.

It flows through pipelines:

• Ingested from multiple sources
• Transformed using Spark / SQL
• Stored in data lakes or warehouses
• Used by analytics, ML, dashboards

Without pipelines:

Data engineering does not exist in production.

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⚙️ What is a Data Pipeline?

A data pipeline is a sequence of steps that:

1. Extract data from source systems
2. Transform and clean it
3. Load it into target systems

This is known as ETL (Extract → Transform → Load)

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🔄 Types of Data Pipelines

1. Batch Pipelines

Data is processed in chunks at intervals:

• Hourly
• Daily
• Weekly

✔ Good for:

• Reporting
• Analytics
• Historical processing

❌ Limitation:

• Not real-time

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2. Streaming Pipelines

Data is processed continuously:

• Event by event
• Near real-time processing

✔ Good for:

• Fraud detection
• Monitoring systems
• Real-time dashboards

❌ Complexity:

• Harder to design and debug

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⚙️ Typical Data Pipeline Architecture

A production pipeline usually looks like: