Data + AI : Powerful Data Pipeline Architectures, MUST know for success in AI Era.

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Why Read This Article?
AI relies on good data, and data pipelines are the highways that move your data.
This article is your guide to choosing the right data pipeline design for your specific needs

Understanding Data Pipelines

What is a data pipeline?

A series of steps that transform and move data from a source (like a website) to a destination (like a dashboard).

Three Key Parts:
Source: Where the data originates.
Processing: How the data changes along the way.
Destination: The final stop for your data.

Examples
"Split-Stream" Customer Insight: One data source (website clicks) feeds both real-time analytics AND an AI model for predicting customer churn.

Revenue Roll-Up: Data from multiple payment systems is combined into a single, clear revenue report.

Powerful Data Pipeline Architectures

Batch Processing
What: Data is processed in chunks, like baking cookies.
Use when: Up-to-the-second accuracy isn't needed, you have large datasets, or want to protect your source systems from heavy loads.

Streaming
What: Data is handled as it comes in, like a constant river flow.
Use when: You need immediate reactions (fraud detection), or real-time dashboards and alerts.

Lambda Architecture
What: The best of both! Balances real-time insights with the ability to reprocess historical data.
Use when: You need both current and long-term analysis, and are okay with some complexity.

ETL vs. ELT
What: The difference is when you transform your data (before or after loading it into your data warehouse).
ETL: Data is cleaned and formatted before. Good for strict quality needs or limited space.
ELT: Load the raw data first, transform later. Prioritizes speed and flexibility.

CDC (Change Data Capture)
What: Like a security camera for your database, sending only changes as they happen.
Use when: Near real-time updates are key and you want to minimize the impact on your source database.