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.