Why incremental models save time and cost in dbt

Introduction

Incremental models only update new or changed data instead of rebuilding everything. This saves time and computing resources.

When you have a large dataset that grows over time.

When you want to update your data model daily without reprocessing all data.

When you want to reduce cloud computing costs by processing less data.

When your data source adds new records regularly but old data rarely changes.

When you want faster data refreshes for reports or dashboards.

Syntax

dbt

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

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

The materialized='incremental' tells dbt to build the model incrementally.

The is_incremental() function helps filter only new or changed rows during incremental runs.

Examples

Incrementally load new orders based on the latest order date.

dbt

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

select * from orders
{% if is_incremental() %}
  where order_date > (select max(order_date) from {{ this }})
{% endif %}

Incrementally update user records changed since last run.

dbt

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

select * from users
{% if is_incremental() %}
  where last_modified > (select max(last_modified) from {{ this }})
{% endif %}

Sample Program

This example shows an incremental model that loads only new rows with updated dates after the last run.

dbt

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

with source_data as (
  select * from (values
    (1, '2024-01-01'),
    (2, '2024-01-02'),
    (3, '2024-01-03')
  ) as t(id, updated_at)
)

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

Important Notes

Incremental models reduce processing time by avoiding full rebuilds.

Make sure your unique key and filter logic correctly identify new or changed data.

Test incremental logic carefully to avoid missing or duplicating data.

Summary

Incremental models update only new or changed data.

This saves time and lowers cloud computing costs.

Use is_incremental() to filter data during incremental runs.

Practice

1. What is the main reason incremental models save time and cost in dbt?

easy

A. They skip data validation steps to speed up execution.

B. They run all transformations in parallel automatically.

C. They use less memory by compressing data during processing.

D. They process only new or changed data instead of the entire dataset.

Solution

1. Step 1: Understand incremental model behavior

   Incremental models update only the new or changed data instead of reprocessing everything.

2. Step 2: Connect behavior to time and cost savings

   Processing less data means less compute time and lower cloud costs.

3. Final Answer:

   They process only new or changed data instead of the entire dataset. -> Option D

4. Quick Check:

   Incremental = process new data only [OK]

Hint: Incremental means update only new or changed data [OK]

Common Mistakes:

• Thinking incremental models reprocess all data
• Confusing incremental with parallel processing
• Assuming incremental skips validation

2. Which dbt function is used inside an incremental model to filter only new or changed data?

easy

A. is_incremental()

B. filter_new()

C. increment_only()

D. new_data_check()

Solution

1. Step 1: Recall dbt incremental syntax

   dbt provides the function is_incremental() to detect incremental runs.

2. Step 2: Identify correct function for filtering

   This function is used in the WHERE clause to filter new or changed data during incremental runs.

3. Final Answer:

   is_incremental() -> Option A

4. Quick Check:

   Use is_incremental() to filter incremental data [OK]

Hint: Use is_incremental() to filter new data in incremental models [OK]

Common Mistakes:

• Using non-existent functions like filter_new()
• Confusing function names with similar words
• Not using any function to filter incremental data

3. Given this dbt incremental model snippet, what rows will be processed during an incremental run?

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

medium

A. All rows from source_table every time

B. Only rows with updated_at newer than the max in the target table

C. Only rows with updated_at older than the max in the target table

D. No rows are processed during incremental runs

Solution

1. Step 1: Analyze the WHERE clause inside is_incremental()

   The condition filters rows where updated_at is greater than the max updated_at in the target table.

2. Step 2: Understand incremental run behavior

   During incremental runs, only new or updated rows with newer timestamps are processed.

3. Final Answer:

   Only rows with updated_at newer than the max in the target table -> Option B

4. Quick Check:

   Incremental filters rows newer than max timestamp [OK]

Hint: Check updated_at > max(updated_at) for incremental rows [OK]

Common Mistakes:

• Thinking all rows are processed every time
• Confusing greater than with less than in filter
• Assuming no rows processed during incremental

4. This incremental model code has an error. What is the problem?

select * from source_table
{% if is_incremental() %}
where updated_at < (select max(updated_at) from {{ this }})
{% endif %}

medium

A. The filter uses < instead of >, so it excludes new data.

B. The is_incremental() function is misspelled.

C. The model is missing a primary key declaration.

D. The select statement is missing a GROUP BY clause.

Solution

1. Step 1: Check the filter condition logic

   The condition uses < (less than) which filters older rows, not new or changed ones.

2. Step 2: Understand incremental filtering needs

   Incremental models must filter rows newer than the max timestamp to add new data.

3. Final Answer:

   The filter uses < instead of >, so it excludes new data. -> Option A

4. Quick Check:

   Incremental filter must use > for new data [OK]

Hint: Use > in incremental filter to include new data [OK]

Common Mistakes:

• Using less than instead of greater than in filter
• Ignoring is_incremental() spelling
• Thinking primary key or GROUP BY is required here

5. You have a large sales table updated daily. You want to build a dbt incremental model that only adds new sales since the last run. Which approach best saves time and cost?

hard

A. Use an incremental model but do not filter any rows inside is_incremental() block.

B. Use a full-refresh model that rebuilds the entire sales table every run.

C. Use an incremental model with is_incremental() filtering rows where sale_date > max(sale_date) in the target table.

D. Use a model that only selects sales from the current day without checking previous data.

Solution

1. Step 1: Identify incremental model best practice

   Filtering new rows using is_incremental() with a condition on sale_date ensures only new data is processed.

2. Step 2: Compare other options

   Full-refresh rebuilds all data (slow, costly). No filtering processes all data each run. Selecting only current day misses data if runs are delayed.

3. Final Answer:

   Use an incremental model with is_incremental() filtering rows where sale_date > max(sale_date) in the target table. -> Option C

4. Quick Check:

   Filter new data with is_incremental() for efficiency [OK]

Hint: Filter new rows with is_incremental() and max date check [OK]

Common Mistakes:

• Choosing full-refresh for large tables
• Not filtering rows in incremental runs
• Selecting only current day without history check