Why incremental models save time and cost in dbt - The Real Reasons

The Big Idea

What if you could update huge data sets in minutes instead of hours?

The Scenario

Imagine you have a huge pile of data that grows every day. Every time you want to update your reports, you run the whole process from the start, even for data you already processed.

The Problem

This means waiting a long time, using lots of computer power, and sometimes making mistakes by reprocessing old data. It feels like painting the entire wall every time you want to fix a small spot.

The Solution

Incremental models let you update only the new or changed data. Instead of redoing everything, you add just the fresh pieces. This saves time, reduces cost, and makes your work faster and smarter.

Before vs After

✗ Before

select * from big_table

✓ After

select * from big_table where updated_at > (select max(updated_at) from target_table)

What It Enables

It makes data updates quick and efficient, so you can focus on insights instead of waiting for processing.

Real Life Example

A company updating daily sales reports only processes the new sales data each day, instead of recalculating all past sales, saving hours of work and computing costs.

Key Takeaways

Manual full data reloads waste time and resources.

Incremental models update only new or changed data.

This approach saves time, reduces cost, and speeds up data workflows.

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