Materializations strategy in dbt - Deep Dive

Overview - Materializations strategy

What is it?

Materializations strategy in dbt is about deciding how your data models are stored and updated in your database. It controls whether data is built as tables, views, incremental tables, or ephemeral structures. This strategy helps manage performance, storage, and freshness of your data. It makes your data transformations efficient and reliable.

Why it matters

Without a materializations strategy, data transformations could be slow, use too much storage, or produce outdated results. This would make data analysis frustrating and unreliable. A good strategy ensures fast queries, saves resources, and keeps data fresh, which helps businesses make timely decisions based on accurate data.

Where it fits

Before learning materializations, you should understand basic SQL and dbt models. After mastering materializations, you can explore advanced dbt features like hooks, macros, and testing. This topic fits in the middle of your dbt learning journey, bridging model creation and performance optimization.

Mental Model

Core Idea

Materializations strategy decides how and where your transformed data lives in the database to balance speed, storage, and freshness.

Think of it like...

It's like choosing how to store your clothes: folded in drawers (tables), hung on hangers (views), or packed in suitcases for travel (incremental updates). Each way suits different needs for access and space.

┌───────────────┐
│  dbt Model   │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│Materialization│
│  Strategy     │
└──────┬────────┘
       │
       ▼
┌───────────────┬───────────────┬───────────────┐
│   Table       │    View       │ Incremental   │
│ (Physical     │ (Virtual      │ (Partial      │
│  Storage)     │  Query)       │  Updates)     │
└───────────────┴───────────────┴───────────────┘

Build-Up - 7 Steps

1

Foundation

What is Materialization in dbt

Concept: Materialization means how dbt saves the result of a model in your database.

When you write a dbt model, it is just SQL code. Materialization decides if dbt creates a table, a view, or something else from that SQL. For example, a table stores data physically, while a view is a saved query that runs when you ask for data.

Result

You understand that materialization controls the form of your data in the database.

Understanding materialization is key because it affects how fast your data queries run and how much space they use.

2

Foundation

Common Materialization Types

Concept: dbt supports several materialization types: table, view, incremental, and ephemeral.

Table: creates a physical table with data stored. View: creates a virtual table that runs the SQL each time. Incremental: updates only new or changed data in a table. Ephemeral: does not create anything in the database, used inside other models.

Result

You can identify the main materialization types and their basic behavior.

Knowing these types helps you pick the right one for your data needs and performance goals.

3

Intermediate

Choosing Materializations for Performance

🤔Before reading on: do you think views or tables are faster for repeated queries? Commit to your answer.

Concept: Different materializations affect query speed and resource use differently.

Tables store data physically, so queries are fast but use storage. Views run SQL on demand, so they use less storage but can be slower. Incremental tables update only changed data, saving time on large datasets. Choosing depends on how often data changes and how fast you need results.

Result

You learn to balance speed and storage by selecting materializations based on use case.

Understanding performance trade-offs prevents slow reports and wasted resources.

4

Intermediate

Using Incremental Materializations

🤔Before reading on: do you think incremental models rebuild all data or only new data? Commit to your answer.

Concept: Incremental materializations update only new or changed rows instead of rebuilding the whole table.

When you run an incremental model, dbt checks for new or updated data and adds or changes only those rows. This saves time and compute power for large datasets that grow over time.

Result

You can efficiently update large datasets without full rebuilds.

Knowing incremental updates helps scale data pipelines and reduces costs.

5

Intermediate

Ephemeral Materializations Explained

Concept: Ephemeral models do not create tables or views but are used inside other models as subqueries.

Ephemeral models are like temporary steps in your SQL. dbt inlines their SQL into the models that use them, so nothing is saved separately in the database. This is useful for reusable logic without extra storage.

Result

You understand how to write modular SQL without creating extra database objects.

Using ephemeral models keeps your database clean and your SQL DRY (Don't Repeat Yourself).

6

Advanced

Custom Materializations in dbt

🤔Before reading on: do you think you can create your own materialization types in dbt? Commit to your answer.

Concept: dbt allows you to write custom materializations to control exactly how models are built and updated.

You can write SQL and Jinja code to define new materialization logic. For example, you might create a materialization that writes data to a special schema or uses a custom update method. This extends dbt beyond built-in types.

Result

You gain flexibility to optimize or customize data builds for special needs.

Knowing custom materializations unlocks advanced control and innovation in data pipelines.

7

Expert

Materializations Impact on Data Freshness and Testing

🤔Before reading on: do you think materialization choice affects how fresh your data is? Commit to your answer.

Concept: Materializations influence how often data is updated and how tests run on your models.

Tables and incremental models store data physically, so freshness depends on how often you run dbt. Views always show current data but can be slower. Testing incremental models requires care to ensure tests run on new data only. Choosing materializations affects your data quality and update strategy.

Result

You understand the link between materialization, data freshness, and testing reliability.

Knowing this prevents stale data and false test results in production.

Under the Hood

dbt compiles your model SQL and runs it against your database. Materializations control the SQL commands dbt generates: CREATE TABLE, CREATE VIEW, INSERT INTO for incremental, or inlining SQL for ephemeral. The database executes these commands to store or present data accordingly. Incremental materializations track changes using unique keys or timestamps to update only new data.

Why designed this way?

Materializations were designed to balance flexibility, performance, and resource use. Early data tools forced full rebuilds or views only, which were slow or costly. dbt introduced multiple materializations to let users pick the best fit for their data size and update frequency. Custom materializations allow extending this flexibility for unique environments.

┌───────────────┐
│  dbt Model   │
└──────┬────────┘
       │ Compile SQL
       ▼
┌───────────────┐
│Materialization│
│  Logic       │
└──────┬────────┘
       │ Generate SQL commands
       ▼
┌───────────────┬───────────────┬───────────────┐
│ CREATE TABLE  │ CREATE VIEW   │ INSERT INTO   │
│ (Physical)   │ (Virtual)     │ (Incremental) │
└───────────────┴───────────────┴───────────────┘
       │
       ▼
┌───────────────┐
│ Database      │
│ Executes SQL  │
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Do views store data physically in the database? Commit to yes or no.

Common Belief: Views store data just like tables do, so they use the same storage space.

Reality: Views do not store data physically; they are saved SQL queries that run when accessed.

Why it matters: Thinking views store data leads to overestimating storage needs and misunderstanding query speed.

Quick: Does incremental materialization rebuild the entire table every time? Commit to yes or no.

Common Belief: Incremental models always rebuild the whole table just like tables.

Reality: Incremental models update only new or changed rows, saving time and resources.

Why it matters: Misunderstanding this causes inefficient pipeline designs and longer runtimes.

Quick: Can ephemeral models be queried directly from the database? Commit to yes or no.

Common Belief: Ephemeral models create tables or views you can query directly.

Reality: Ephemeral models do not create any database objects; they are inlined SQL used inside other models.

Why it matters: Expecting ephemeral models to exist in the database causes confusion and errors in querying.

Quick: Does choosing a materialization affect data freshness? Commit to yes or no.

Common Belief: Materialization choice does not impact how fresh the data is.

Reality: Materialization affects how often data is updated and how fresh it appears to users.

Why it matters: Ignoring this leads to stale data in reports and wrong business decisions.

Expert Zone

1

Incremental materializations require careful unique key or timestamp management to avoid data duplication or missing updates.

2

Ephemeral models improve performance by reducing database objects but can increase query complexity and debugging difficulty.

3

Custom materializations can integrate with external systems or optimize for cloud data warehouses' specific features.

When NOT to use

Avoid using views for large, frequently queried datasets because they can slow down queries. Do not use incremental materializations when data changes are complex or require full rebuilds. Use tables for stable, large datasets needing fast access. Ephemeral models are not suitable when you need to share intermediate results across multiple models or teams.

Production Patterns

In production, teams often use incremental materializations for large event or log data to save costs. Tables are used for core business metrics for fast reporting. Views are common for small or rarely used datasets. Custom materializations help integrate dbt with data lake architectures or specialized cloud features like clustering or partitioning.

Connections

Database Indexing

Materializations and indexing both optimize data access speed and resource use.

Understanding materializations helps appreciate how physical storage and query plans affect performance, similar to how indexes speed up database queries.

Software Build Systems

Materializations are like build artifacts in software compilation, deciding what files are generated and reused.

Knowing this connection clarifies why incremental builds save time by reusing unchanged parts, just like incremental materializations update only new data.

Supply Chain Management

Materializations manage data flow and storage like supply chains manage goods flow and inventory.

This analogy helps understand trade-offs between storage cost, freshness, and speed in both data and physical goods management.

Common Pitfalls

#1 Using views for large datasets expecting fast query performance.

Wrong approach:

materialized: view

Correct approach:

materialized: table

Root cause:

Misunderstanding that views run SQL on demand and can be slow for big data.

#2 Running incremental models without defining unique keys.

Wrong approach:

materialized: incremental

-- no unique_key specified

Correct approach:

materialized: incremental
unique_key: id

Root cause:

Not specifying unique keys causes dbt to fail or produce incorrect incremental updates.

#3 Trying to query ephemeral models directly from the database.

Wrong approach:

SELECT * FROM ephemeral_model_name;

Correct approach:

Use ephemeral models only inside other models; do not query directly.

Root cause:

Misunderstanding that ephemeral models do not create database objects.

Key Takeaways

Materializations control how dbt saves or presents your transformed data in the database.

Choosing the right materialization balances query speed, storage use, and data freshness.

Incremental materializations update only new data, saving time on large datasets.

Ephemeral models are temporary SQL snippets used inside other models without creating database objects.

Custom materializations extend dbt's flexibility for special use cases and optimizations.

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