Why Multi-source fan-in patterns in dbt? - Purpose & Use Cases

The Big Idea

What if you could stop juggling data from many places and get one clear view with just a few lines of code?

The Scenario

Imagine you have data coming from many places like sales, customers, and products. You try to combine all this data by hand using spreadsheets or simple queries.

You spend hours copying, pasting, and matching rows across files. It feels like juggling too many balls at once.

The Problem

Doing this manually is slow and tiring. You might make mistakes like missing some data or mixing up columns.

Every time new data arrives, you have to repeat the whole process, which wastes time and causes frustration.

The Solution

Multi-source fan-in patterns let you bring data from many sources together in one place automatically.

With dbt, you write clear, reusable code that merges data smoothly. It saves time, reduces errors, and keeps your data fresh.

Before vs After

✗ Before

SELECT * FROM sales;
SELECT * FROM customers;
-- Manually join results in spreadsheet

✓ After

WITH sales AS (SELECT * FROM sales_source),
customers AS (SELECT * FROM customers_source)
SELECT * FROM sales JOIN customers ON sales.customer_id = customers.id

What It Enables

You can quickly combine and analyze data from many places to get a complete, accurate picture.

Real Life Example

A retail company uses multi-source fan-in to merge online orders, in-store sales, and customer info to understand buying trends and improve marketing.

Key Takeaways

Manual merging of multiple data sources is slow and error-prone.

Multi-source fan-in patterns automate and simplify combining data.

dbt helps write clean, reusable code to keep data accurate and up-to-date.