Materializations strategy in dbt - Mini Project: Build & Apply

Materializations Strategy with dbt

📖 Scenario: You work as a data analyst in a company that uses dbt to manage data transformations. You want to learn how to control how dbt builds your models using different materializations.Materializations decide if a model is built as a table, view, or incremental load in the database.

🎯 Goal: Learn how to set up a dbt model with different materializations and see how the output changes.

📋 What You'll Learn

Create a simple dbt model SQL file

Add a configuration variable to set materialization

Use the materialization config in the model

Run the model and observe the output materialization

💡 Why This Matters

🌍 Real World

Data teams use dbt materializations to control how data models are built and stored in the warehouse, optimizing for speed and cost.

💼 Career

Understanding materializations is key for data engineers and analysts to manage data pipelines efficiently using dbt.

1

Create a simple dbt model SQL file

Create a dbt model SQL file named my_model.sql with a simple SELECT statement that selects id and name from a table called raw.customers.

dbt

-- Create a dbt model SQL file named my_model.sql
-- Write a SELECT statement selecting id and name from raw.customers

-- Your code here

Hint

Use a simple SELECT statement in your SQL file.

2

Add a materialization configuration

Add a configuration block at the top of my_model.sql to set the materialization to table using {{ config(materialized='table') }}.

dbt

-- Add a config block to set materialized='table' at the top of the file

select id, name from raw.customers

-- Your code here

Hint

Use the Jinja config function to set materialized='table'.

3

Change materialization to view

Change the materialization in the config block to view by updating materialized='table' to materialized='view' in my_model.sql.

dbt

-- Change materialized='table' to materialized='view' in the config block

{{ config(materialized='table') }}

select id, name from raw.customers

-- Your code here

Hint

Just update the materialized value inside the config function.

4

Print the materialization type

Print the string Materialization is set to view to confirm the current materialization setting.

dbt

-- Print the materialization confirmation message

{{ config(materialized='view') }}

select id, name from raw.customers

-- Your code here

Hint

Use a print statement to show the materialization type.

Practice

1. What does the table materialization do in dbt?

easy

A. Runs the model only when data changes incrementally.

B. Creates a temporary view that disappears after the session ends.

C. Creates a physical table in the database that stores data permanently.

D. Does not create any database object but runs SQL inline.

Solution

1. Step 1: Understand table materialization

   The table materialization creates a real table in the database that stores the data physically.

2. Step 2: Compare with other materializations

   Unlike views or ephemeral models, tables persist data and improve query speed for large datasets.

3. Final Answer:

   Creates a physical table in the database that stores data permanently. -> Option C

4. Quick Check:

   table materialization = physical table [OK]

Hint: Tables store data physically; views do not [OK]

Common Mistakes:

• Confusing tables with views
• Thinking ephemeral models create tables
• Assuming incremental means full table creation

2. Which of the following is the correct syntax to set a model as incremental in dbt?

easy

A. materialized: 'incremental'

B. materialization: 'incremental'

C. materialized = incremental

D. materialize: incremental

Solution

1. Step 1: Recall dbt model config syntax

   In dbt, model materialization is set using materialized: 'type' inside the model config block.

2. Step 2: Identify correct syntax for incremental

   The correct key is materialized with a colon and the value in quotes, like 'incremental'.

3. Final Answer:

   materialized: 'incremental' -> Option A

4. Quick Check:

   Use materialized: 'incremental' for incremental models [OK]

Hint: Use 'materialized' key with colon and quotes [OK]

Common Mistakes:

• Using 'materialization' instead of 'materialized'
• Missing quotes around 'incremental'
• Using equals sign instead of colon

3. Given this dbt model config and SQL, what will be the result when run?

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

Model config:

materialized: 'incremental'

medium

A. The model will fully refresh the table every run.

B. The model will fail due to syntax error.

C. The model will create a view instead of a table.

D. The model will add only new rows with updated timestamps.

Solution

1. Step 1: Understand the incremental logic in SQL

   The SQL uses is_incremental() to run different queries: full select on first run, then only new rows after.

2. Step 2: Match with incremental materialization behavior

   With materialized: 'incremental', dbt appends new rows where updated_at is greater than the max in the existing table.

3. Final Answer:

   The model will add only new rows with updated timestamps. -> Option D

4. Quick Check:

   Incremental model adds new rows only [OK]

Hint: Incremental models append new data, not full refresh [OK]

Common Mistakes:

• Thinking incremental always refreshes full table
• Confusing view creation with incremental
• Ignoring the is_incremental() condition

4. You wrote this dbt model config:

materialized: incremental

What is the error and how to fix it?

medium

A. Wrong key name; fix: materialization: 'incremental'

B. Missing quotes around 'incremental'; fix: materialized: 'incremental'

C. Should use equals sign; fix: materialized = 'incremental'

D. No error; config is correct

Solution

1. Step 1: Identify syntax error in config

   The value incremental must be a string, so it needs quotes.

2. Step 2: Correct the syntax

   Change to materialized: 'incremental' to fix the error.

3. Final Answer:

   Missing quotes around 'incremental'; fix: materialized: 'incremental' -> Option B

4. Quick Check:

   Materialization values must be strings in quotes [OK]

Hint: Always quote materialization types [OK]

Common Mistakes:

• Forgetting quotes around materialization type
• Using wrong key 'materialization'
• Using equals sign instead of colon

5. You have a large dataset that updates daily with new and changed rows. You want fast query performance and to avoid rebuilding the entire table each time. Which materialization strategy should you choose in dbt?

hard

A. Use incremental materialization to add and update rows efficiently.

B. Use table materialization and rebuild daily.

C. Use view materialization for real-time data.

D. Use ephemeral materialization to avoid creating tables.

Solution

1. Step 1: Analyze dataset size and update pattern

   The dataset is large and updates daily with new and changed rows, so full rebuilds are costly.

2. Step 2: Match materialization to needs

   incremental materialization updates only new or changed rows, improving performance and avoiding full rebuilds.

3. Final Answer:

   Use incremental materialization to add and update rows efficiently. -> Option A

4. Quick Check:

   Incremental = efficient updates for large, changing data [OK]

Hint: Incremental suits large, frequently updated datasets [OK]

Common Mistakes:

• Choosing view which rebuilds every query
• Using table and rebuilding fully each time
• Using ephemeral which does not create tables