Why incremental models save time and cost in dbt - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why do incremental models reduce processing time?

In dbt, incremental models only process new or changed data instead of the entire dataset. Why does this approach reduce processing time?

A Because it stores data in memory instead of disk.

B Because it deletes the entire table before loading new data.

C Because it runs all transformations in parallel regardless of data size.

D Because it processes only the new or updated rows, avoiding reprocessing all data.

💡 Hint

Think about what happens when you only update part of a large dataset.

Incremental models save time by processing only new or changed data, so they avoid reprocessing the entire dataset each time.

❓ data_output

intermediate

Output of incremental model run with new data

Given a table with 1000 rows, an incremental model processes 100 new rows. What is the total number of rows after the incremental run?

A 1000

B 100

C 1100

D 900

💡 Hint

Incremental models add new rows without deleting existing ones.

The incremental model appends 100 new rows to the existing 1000 rows, resulting in 1100 rows total.

❓ Predict Output

advanced

Result of incremental model SQL logic

Consider this simplified incremental model SQL snippet:

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

What does this query return during an incremental run?

A All rows from source_table regardless of updated_at.

B Only rows from source_table with updated_at newer than the latest in target_table.

C Rows from target_table with updated_at older than source_table.

D An empty result set.

💡 Hint

Think about how max(updated_at) in target_table limits new rows.

The query selects only rows from source_table that have a more recent updated_at timestamp than the maximum in target_table, so only new or changed rows are processed.

❓ visualization

advanced

Visualizing time saved by incremental models

You have two bar charts showing processing times: one for full refresh runs and one for incremental runs over 5 days. Which chart best represents the time saved by incremental models?

A A bar chart with full refresh times constant and incremental times much lower each day.

B A line chart showing incremental times increasing above full refresh times.

C A bar chart with incremental times higher than full refresh times.

D A pie chart showing equal time distribution.

💡 Hint

Incremental models should take less time than full refreshes consistently.

The correct visualization shows full refresh times steady and high, while incremental times are consistently lower, illustrating time savings.

🚀 Application

expert

Choosing incremental model strategy for cost savings

You manage a large dataset updated daily. Running full refreshes takes hours and costs a lot. Which incremental model strategy will save the most cost and time?

A Process only new rows based on a timestamp column and append them to the target table.

B Delete the entire target table and reload all data every day.

C Process all rows but skip transformations to save time.

D Run full refreshes only on weekends and incremental runs on weekdays.

💡 Hint

Focus on minimizing data processed daily.

Processing only new rows daily reduces compute time and cost significantly compared to full reloads or skipping transformations.

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