Why Materializations (view, table, incremental, ephemeral) in dbt? - Purpose & Use Cases

The Big Idea

What if your data reports could update themselves faster without running everything from scratch?

The Scenario

Imagine you have a huge spreadsheet with thousands of rows. Every time you want to see a summary, you have to scroll through all the data and recalculate everything by hand.

Or picture running the same slow query on your database every time you want a report, even if the data hasn't changed much.

The Problem

Doing all calculations manually or running full queries repeatedly is slow and tiring.

You might make mistakes copying or recalculating data, and it wastes your time.

Also, it can overload your computer or database, making everything slower for everyone.

The Solution

Materializations in dbt let you save results in smart ways.

Views act like saved queries you can reuse quickly.

Tables store the full results so you don't recalculate every time.

Incremental materializations update only new or changed data, saving time.

Ephemeral models run inside other queries without saving, keeping things simple and fast.

Before vs After

✗ Before

SELECT * FROM big_table WHERE date > '2020-01-01'; -- run every time

✓ After

-- incremental materialization
{{ config(materialized='incremental') }}
SELECT * FROM source_table WHERE updated_at > (SELECT MAX(updated_at) FROM this)

What It Enables

Materializations let you build fast, reliable data pipelines that save time and avoid repeating heavy work.

Real Life Example

A company updates its sales data daily. Instead of recalculating all sales every day, incremental materialization updates only new sales, making reports ready faster.

Key Takeaways

Manual recalculations are slow and error-prone.

Materializations save and reuse data smartly.

Incremental updates save time by processing only new data.