Materializations (view, table, incremental, ephemeral) in dbt - Time & Space Complexity

Time Complexity: Materializations (view, table, incremental, ephemeral)

O(n)

Understanding Time Complexity

When using dbt materializations, it's important to understand how the time to build models grows as data size increases.

We want to know how the choice of materialization affects the work done as data grows.

Scenario Under Consideration

Analyze the time complexity of these dbt materializations.

-- View materialization
{{ config(materialized='view') }}
select * from source_table

-- Table materialization
{{ config(materialized='table') }}
select * from source_table

-- Incremental materialization
{{ config(materialized='incremental') }}
select * from source_table where updated_at > (select max(updated_at) from {{ this }})

-- Ephemeral materialization
{{ config(materialized='ephemeral') }}
select * from source_table

These snippets show different ways dbt builds models from source data.

Identify Repeating Operations

Look at how often data is processed or scanned.

• Primary operation: Scanning rows from source_table.
• How many times:
  • View and Table: full scan every run.
  • Incremental: scans only new or changed rows.
  • Ephemeral: runs as a subquery, no storage, processed each time used.

How Execution Grows With Input

As source_table grows, the work changes by materialization type.

Input Size (n rows)	View/Table Operations	Incremental Operations	Ephemeral Operations
10,000	Scan 10,000 rows	Scan new rows only (e.g., 100)	Scan 10,000 rows each use
100,000	Scan 100,000 rows	Scan new rows only (e.g., 1,000)	Scan 100,000 rows each use
1,000,000	Scan 1,000,000 rows	Scan new rows only (e.g., 10,000)	Scan 1,000,000 rows each use

Pattern observation: View and Table scan all data every run, so work grows linearly with data size. Incremental scans only new data, so work grows with new rows, not total size. Ephemeral runs full scan each time it is referenced.

Final Time Complexity

Time Complexity: O(n)

This means the time to build or run the model grows roughly in direct proportion to the number of rows processed.

Common Mistake

[X] Wrong: "Incremental materialization always processes all data like a table."

[OK] Correct: Incremental only processes new or changed rows, so it usually does less work than full table rebuilds.

Interview Connect

Understanding how different materializations affect processing time helps you design efficient data pipelines and explain trade-offs clearly.

Self-Check

"What if we changed an incremental model to a full table rebuild every time? How would the time complexity change?"

Practice

1. Which dbt materialization creates a permanent table in the database that stores data physically?

easy

A. table

B. view

C. incremental

D. ephemeral

Solution

1. Step 1: Understand the purpose of 'table' materialization

   The 'table' materialization creates a physical table in the database that stores data permanently.

2. Step 2: Compare with other materializations

   'view' creates a virtual table, 'incremental' updates existing tables efficiently, and 'ephemeral' runs inline SQL without creating tables.

3. Final Answer:

   table -> Option A

4. Quick Check:

   Permanent storage = table [OK]

Hint: Permanent data storage means 'table' materialization [OK]

Common Mistakes:

• Confusing 'view' with 'table' as both represent data
• Thinking 'incremental' creates a full new table every time
• Assuming 'ephemeral' creates physical tables

2. Which of the following is the correct syntax to specify an incremental materialization in a dbt model's config block?

easy

A. config(materialization = 'incremental')

B. config(materialized = 'incremental')

C. materialized('incremental')

D. set materialized = incremental

Solution

1. Step 1: Recall dbt config syntax for materialization

   dbt uses config() with the keyword 'materialized' to set materialization type.

2. Step 2: Identify the correct keyword and format

   The correct syntax is config(materialized = 'incremental'). Other options use wrong keywords or syntax.

3. Final Answer:

   config(materialized = 'incremental') -> Option B

4. Quick Check:

   Correct keyword is 'materialized' inside config() [OK]

Hint: Use config(materialized = 'type') syntax for materializations [OK]

Common Mistakes:

• Using 'materialization' instead of 'materialized'
• Trying to call materialized as a function
• Using SQL-like SET syntax instead of config()

3. Given this dbt model config and SQL snippet:

-- model.sql
{{ config(materialized='incremental', unique_key='id') }}
select id, value from source_table
{% if is_incremental() %}
  where updated_at > (select max(updated_at) from {{ this }})
{% endif %}

What happens when you run this model multiple times?

medium

A. The model rebuilds the entire table every time

B. The model creates a view that always shows fresh data

C. The model appends only new or updated rows based on 'updated_at'

D. The model runs inline SQL without creating a table

Solution

1. Step 1: Understand incremental materialization with unique_key

   The model uses incremental materialization with a unique key 'id' to update data efficiently.

2. Step 2: Analyze the is_incremental() condition

   When running incrementally, it filters rows where 'updated_at' is newer than the max in the existing table, appending only new or updated rows.

3. Final Answer:

   The model appends only new or updated rows based on 'updated_at' -> Option C

4. Quick Check:

   Incremental + filter = append updates [OK]

Hint: Incremental with is_incremental() filters new data only [OK]

Common Mistakes:

• Thinking incremental rebuilds full table every run
• Confusing view materialization with incremental
• Ignoring the is_incremental() condition

4. You wrote this dbt model:

{{ config(materialized='ephemeral') }}
select * from source_table

But when you run dbt, you get an error saying the model is not found. What is the likely cause?

medium

A. Ephemeral models do not create tables or views, so they cannot be run directly

B. The config syntax for ephemeral is incorrect

C. Ephemeral models require a unique_key to run

D. You must specify incremental materialization for ephemeral models

Solution

1. Step 1: Recall what ephemeral materialization does

   Ephemeral models do not create tables or views; their SQL is inlined into dependent models.

2. Step 2: Understand why the error occurs

   Since ephemeral models don't create database objects, running them directly causes a 'model not found' error.

3. Final Answer:

   Ephemeral models do not create tables or views, so they cannot be run directly -> Option A

4. Quick Check:

   Ephemeral = inline SQL, no table/view created [OK]

Hint: Ephemeral models can't be run alone; they inline SQL [OK]

Common Mistakes:

• Trying to run ephemeral models directly
• Assuming ephemeral needs unique_key
• Confusing ephemeral with incremental

5. You want to build a dbt model that:
- Stores data permanently
- Updates only new rows efficiently
- Avoids rebuilding the entire dataset each run

Which materialization should you choose and why?

hard

A. Use 'table' materialization because it stores data permanently and rebuilds fully each run

B. Use 'ephemeral' materialization because it runs inline SQL without storage

C. Use 'view' materialization because it always shows fresh data without storage

D. Use 'incremental' materialization because it stores data permanently and updates only new rows

Solution

1. Step 1: Identify permanent storage requirement

   Both 'table' and 'incremental' materializations store data permanently.

2. Step 2: Consider update efficiency

   'Table' rebuilds fully each run, while 'incremental' updates only new or changed rows efficiently.

3. Step 3: Match requirements

   Since you want to avoid full rebuilds and update only new rows, 'incremental' fits best.

4. Final Answer:

   Use 'incremental' materialization because it stores data permanently and updates only new rows -> Option D

5. Quick Check:

   Permanent + efficient updates = incremental [OK]

Hint: Incremental = permanent storage + efficient updates [OK]

Common Mistakes:

• Choosing 'table' and expecting incremental updates
• Picking 'view' which does not store data permanently
• Confusing ephemeral with storage options