Table vs View vs Incremental in dbt: Key Differences and Usage

In dbt, a table stores data physically and rebuilds fully on each run, a view is a saved query that runs on demand without storing data, and an incremental model updates only new or changed data to improve performance and reduce processing time.

Quick Comparison

This table summarizes the main differences between table, view, and incremental models in dbt.

Feature	Table	View	Incremental
Data Storage	Physical storage of data	No storage, runs query on demand	Physical storage with partial updates
Performance	Slower on rebuilds due to full refresh	Fast for small queries, slower on complex queries	Faster by processing only new/changed data
Update Method	Full rebuild every run	Always fresh, no rebuild needed	Append or update only changed rows
Use Case	Stable datasets, large data	Lightweight, simple transformations	Large datasets with frequent updates
Cost	Higher storage and compute cost	Lower storage cost, compute cost on query	Balanced storage and compute cost

Key Differences

Tables in dbt physically store the results of your SQL queries in the database. Each time you run dbt, the table is fully rebuilt, which can be slow for large datasets but ensures data is fully refreshed.

Views do not store data physically; instead, they save the SQL query itself. When you query a view, the database runs the query live. This means views are always up-to-date but can be slower for complex or large data because the query runs every time.

Incremental models combine the benefits of tables and views by storing data physically but only updating new or changed rows during dbt runs. This approach reduces processing time and cost for large datasets that update frequently, making it efficient for incremental data loads.

Code Comparison

Here is how you define a simple full-refresh table model in dbt that selects all data from a source.

sql

{{ config(materialized='table') }}

select * from {{ source('my_source', 'my_table') }}

Output

A physical table named after the model is created or replaced with all rows from the source.

Incremental Equivalent

This is the equivalent incremental model that only adds new rows based on a unique key and timestamp.

sql

{{ config(materialized='incremental', unique_key='id') }}

select * from {{ source('my_source', 'my_table') }}

{% if is_incremental() %}
  where updated_at > (select max(updated_at) from {{ this }})
{% endif %}

Output

On first run, creates a full table; on subsequent runs, appends only rows with newer updated_at values.

When to Use Which

Choose table when you need a stable dataset that is fully rebuilt regularly and data freshness is critical without complex update logic.

Choose view for lightweight transformations or when you want always up-to-date data without storage cost, especially for small or simple datasets.

Choose incremental for large datasets that update frequently where full rebuilds are costly, and you want to optimize performance by processing only new or changed data.

Key Takeaways

Tables store data physically and rebuild fully on each dbt run.

Views save queries and run them live without storing data.

Incremental models update only new or changed data for efficiency.

Use tables for stable, large datasets needing full refresh.

Use views for simple, always fresh queries with low storage cost.