Materializations (view, table, incremental, ephemeral) in dbt - Deep Dive

Overview - Materializations (view, table, incremental, ephemeral)

What is it?

Materializations in dbt are ways to store or represent the results of your data transformations. They define how your data models are saved in the database, such as a view, a table, or other forms. Each materialization type controls when and how the data is refreshed or updated. This helps manage performance and storage depending on your needs.

Why it matters

Without materializations, you would have no control over how your transformed data is saved or updated. This could lead to slow queries, unnecessary data duplication, or outdated information. Materializations let you balance speed, storage, and freshness, making your data workflows efficient and reliable. They are essential for building scalable and maintainable data pipelines.

Where it fits

Before learning materializations, you should understand basic SQL and how dbt models work. After mastering materializations, you can explore advanced dbt features like hooks, macros, and testing. Materializations are a core part of dbt's data modeling layer and connect to how data warehouses store and optimize data.

Mental Model

Core Idea

Materializations decide how and where your transformed data is saved and refreshed in the database to balance speed, storage, and freshness.

Think of it like...

Imagine you bake cookies (your data transformation). Materializations are like choosing whether to keep the cookies on a plate (view), store them in a jar (table), add new cookies to the jar over time (incremental), or just use the dough immediately without saving (ephemeral). Each choice affects how quickly you can eat them later and how much space they take.

┌───────────────┐
│  dbt Model   │
└──────┬────────┘
       │
       ▼
┌───────────────┐       ┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│   View        │       │   Table       │       │ Incremental   │       │  Ephemeral    │
│ (virtual, no  │       │ (physical,    │       │ (append or    │       │ (no storage,   │
│ storage, fast)│       │ stored data)  │       │ update data)  │       │ used in query) │
└───────────────┘       └───────────────┘       └───────────────┘       └───────────────┘

Build-Up - 7 Steps

1

Foundation

What is a Materialization in dbt

Concept: Materialization is how dbt saves the output of a model in the database.

When you write a dbt model, it creates a SQL query. Materialization decides if this query becomes a view, a table, or something else in your database. This choice affects how data is stored and refreshed.

Result

You understand that materialization controls the form and storage of your transformed data.

Knowing that materialization is about storage form helps you control performance and data freshness.

2

Foundation

Basic Types: View and Table

Concept: View and table are the two simplest materializations in dbt.

A view is like a saved query that runs every time you ask for data. It uses no extra storage but can be slower for big data. A table stores the data physically, so queries are faster but storage is used. Tables need to be refreshed to update data.

Result

You can choose between fast storage (table) or no storage but slower queries (view).

Understanding the tradeoff between views and tables helps you optimize your data pipeline.

3

Intermediate

Incremental Materialization Explained

🤔Before reading on: do you think incremental materialization rebuilds the entire table every time or only updates parts? Commit to your answer.

Concept: Incremental materialization updates only new or changed data instead of rebuilding the whole table.

Incremental models add or update rows in an existing table based on a unique key or timestamp. This saves time and resources when working with large datasets that grow over time.

Result

Your data pipeline runs faster by processing only new data instead of full refreshes.

Knowing incremental updates reduce workload is key for scaling data transformations.

4

Intermediate

Ephemeral Materialization and Its Use

Concept: Ephemeral models do not create database objects but are inlined into dependent models.

Ephemeral materializations run their SQL inside other models during compilation. They don't create views or tables. This is useful for temporary calculations or reusable logic without storage cost.

Result

You can write modular SQL without extra storage or query overhead.

Understanding ephemeral models helps you write cleaner, more efficient dbt projects.

5

Intermediate

Choosing Materializations by Use Case

🤔Before reading on: which materialization would you pick for a small, frequently updated dataset? Commit to your answer.

Concept: Different materializations fit different data sizes, update frequencies, and performance needs.

Use views for small or rarely used data to save storage. Use tables for large datasets needing fast queries. Use incremental for growing data with frequent updates. Use ephemeral for temporary logic inside other models.

Result

You can pick the right materialization to balance speed, cost, and freshness.

Knowing when to use each materialization prevents performance and cost issues.

6

Advanced

How dbt Manages Incremental Logic

🤔Before reading on: do you think dbt automatically detects new data for incremental models or requires explicit logic? Commit to your answer.

Concept: dbt requires you to define how to identify new or changed data for incremental models.

You write SQL with a WHERE clause that filters new or updated rows. dbt runs this SQL to append or update the existing table. This manual control lets you tailor incremental logic to your data.

Result

Incremental models run efficiently and correctly only when you provide proper filtering logic.

Knowing that incremental logic is user-defined helps avoid data duplication or missing updates.

7

Expert

Custom Materializations and Performance Tuning

🤔Before reading on: can you create your own materialization type in dbt or only use built-in ones? Commit to your answer.

Concept: dbt allows creating custom materializations to fit unique needs and optimize performance.

You can write macros to define new materializations combining features or adding custom behavior. This is useful for complex workflows or optimizing database-specific features like clustering or partitioning.

Result

You gain full control over how data is stored and refreshed, improving pipeline efficiency.

Understanding custom materializations unlocks advanced dbt power and fine-tuned data engineering.

Under the Hood

dbt compiles your model SQL and wraps it with commands depending on the materialization. For views, it creates a database view object referencing the query. For tables, it runs a CREATE OR REPLACE TABLE command with the query result. Incremental materializations run a SELECT with filters to append or update data in an existing table. Ephemeral models inline their SQL into dependent models during compilation, so no database object is created.

Why designed this way?

Materializations were designed to give users control over data storage and performance tradeoffs. Views save storage but can be slow, tables speed up queries but use space, incremental saves time on large datasets by updating only changes, and ephemeral avoids unnecessary storage for temporary logic. This design balances flexibility, efficiency, and simplicity.

┌───────────────┐
│  dbt Compile  │
└──────┬────────┘
       │
       ▼
┌───────────────────────────────┐
│ Materialization Logic Selected │
└──────┬────────┬────────┬───────┘
       │        │        │
       ▼        ▼        ▼
┌─────────┐ ┌─────────┐ ┌─────────────┐
│  View   │ │  Table  │ │ Incremental │
│ (CREATE │ │ (CREATE │ │ (INSERT or  │
│  VIEW)  │ │  TABLE) │ │  UPDATE)    │
└─────────┘ └─────────┘ └─────────────┘
       │
       ▼
┌─────────────┐
│ Ephemeral   │
│ (Inline SQL │
│  in models) │
└─────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does a view store data physically in the database? Commit yes or no.

Common Belief: Views store data physically like tables do.

Reality: Views are virtual and do not store data; they run the underlying query each time.

Why it matters: Thinking views store data can lead to wrong assumptions about query speed and storage costs.

Quick: Does incremental materialization automatically detect new data without user input? Commit yes or no.

Common Belief: dbt automatically figures out which rows are new for incremental models.

Reality: You must write SQL logic to specify how to find new or changed rows for incremental updates.

Why it matters: Without proper logic, incremental models can miss data or duplicate rows, causing errors.

Quick: Are ephemeral models saved as tables or views in the database? Commit yes or no.

Common Belief: Ephemeral models create temporary tables or views in the database.

Reality: Ephemeral models do not create any database objects; their SQL is embedded in other models.

Why it matters: Misunderstanding ephemeral models can cause confusion about data lineage and debugging.

Quick: Is it always better to use tables instead of views for performance? Commit yes or no.

Common Belief: Tables are always faster and better than views.

Reality: Views can be faster for small datasets or when storage is limited; tables are better for large or complex data.

Why it matters: Choosing tables blindly can waste storage and increase costs unnecessarily.

Expert Zone

1

Incremental models require careful handling of unique keys and update logic to avoid data duplication or loss.

2

Ephemeral models improve compilation speed and reduce database load but can complicate debugging due to inlined SQL.

3

Custom materializations can leverage database-specific features like clustering or partitioning for performance gains.

When NOT to use

Avoid incremental materialization when your data source does not have reliable unique keys or timestamps; use full-refresh tables instead. Do not use ephemeral models for large or reused datasets as they increase query complexity. Custom materializations should be used only when built-in types cannot meet performance or business needs.

Production Patterns

In production, teams use incremental materializations for large event or log data to save processing time. Ephemeral models are common for reusable SQL snippets or complex joins. Views are used for lightweight, frequently changing data. Custom materializations help optimize data pipelines on cloud warehouses like Snowflake or BigQuery by leveraging their unique features.

Connections

Database Indexing

Materializations affect how data is stored, which impacts indexing strategies.

Understanding materializations helps optimize indexing for faster queries and efficient storage.

Software Caching

Views are like no-cache queries, tables like cached data, and incremental updates like cache invalidation.

Knowing caching principles clarifies why materializations balance freshness and speed.

Manufacturing Inventory Management

Incremental materialization is like restocking inventory only with new items instead of full replacement.

This connection shows how incremental updates save resources by avoiding full rebuilds.

Common Pitfalls

#1 Using incremental materialization without defining unique keys or filters.

Wrong approach:

materialized='incremental'

-- Missing WHERE clause for new data
SELECT * FROM source_table

Correct approach:

materialized='incremental'

SELECT * FROM source_table WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})

Root cause:

Not understanding that incremental models need explicit logic to identify new or changed rows.

#2 Expecting ephemeral models to create database objects.

Wrong approach:

materialized='ephemeral'

-- Trying to query ephemeral model directly in database

Correct approach:

Use ephemeral models only as CTEs inside other models; do not query them standalone.

Root cause:

Misunderstanding ephemeral models as physical tables or views.

#3 Using views for very large datasets expecting fast queries.

Wrong approach:

materialized='view'

-- Large dataset query runs slowly every time

Correct approach:

materialized='table'

-- Store data physically for faster repeated queries

Root cause:

Not recognizing that views run the full query each time, which is slow on big data.

Key Takeaways

Materializations control how dbt saves and refreshes transformed data in your database.

Views are virtual and save no data, tables store data physically, incremental updates only new data, and ephemeral models inline SQL without storage.

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

Incremental models require explicit logic to identify new or changed data to work correctly.

Advanced users can create custom materializations to optimize performance and fit unique workflows.