Materialization in DBT — Beginner to Advanced Comparative Guide

Materialization is how DBT persists the results of your SQL transformations in your data warehouse. Think of it as the “output format” of your model. In tools like DBT, “materialization” is a built-in concept to choose how a model is built (table, view, incremental, ephemeral). Different materialization strategies offer trade-offs between query performance, data freshness, compute cost, and storage cost.

Just because a tool has “materialization” like behavior doesn’t mean it has the full flexibility that DBT has (e.g., incremental logic, ephemeral modeling).

DBT Materialization Design

DBT provides four primary built-in materializations for models: view (the default), table, incremental, and ephemeral.

1. View

DBT Design: Set the materialization in the model’s configuration block (often at the top of the SQL file).

Code Example (Simplified):

DBT Action: 

• When you run dbt run, dbt executes a CREATE VIEW my_model AS (SELECT…) statement in the data warehouse.

• No data is actually stored; the query runs every time the view is queried

When to use:

• Lightweight transformations

• Models queried infrequently

• When you need always-fresh data

• Development/testing

Pros: No storage cost, always current data Cons: Slow query performance, recomputes every time

2. Table (Full Refresh)

DBT Design: Change the materialization from the default view to table.

Code Example (Simplified):

DBT Action: 

• When run, DBT executes a CREATE OR REPLACE TABLE AS … statement, rebuilding the entire table every time.

• A physical table is created and data is stored permanently

When to use:

• Models queried frequently

• Complex transformations that are expensive to compute

• Final output/reporting layers

• Medium-sized datasets (not constantly changing)

Pros: Fast query performance, data is materialized 

Cons: Full refresh on every run, storage costs, downtime during rebuild

3. Incremental Table

This is the biggest difference, as dbt manages the complex logic for only processing new records.

DBT Design: Set materialized=’incremental’ and use the special Jinja macro is_incremental() to wrap the logic that filters for new records. You must also define a unique_key or use an incremental_strategy (like append or merge) depending on your use case.

Code Example (Focusing on the incremental logic):

DBT Action:

• First Run: The is_incremental() block is ignored, and dbt runs a CREATE TABLE AS… for all data, in other words, it creates a table with all data

• Subsequent Runs: dbt executes the query, applying the WHERE filter to only process new data. It then performs a MERGE or INSERT operation (based on your configuration and warehouse) to update the existing customer_sales table ({{ this }}) with the new results. is_incremental() checks if the table already exists

When to use:

• Large datasets (millions+ rows)

• Event/log data that only grows

• When full refreshes are too slow/expensive

• Time-series data

Pros: Fast builds, efficient, handles large data Cons: More complex logic, potential for data inconsistencies if not careful

4. Ephemeral

DBT Design: Set materialized=’ephemeral’.

DBT Action: dbt does not create any object in the warehouse. Instead, it copies the SQL query frm the ephemeral model and pastes it into the SQL of any downstream model that references it, treating it as a Common Table Expression (CTE). This helps keep your logic modular without creating unnecessary database clutter.

When to use:

• Intermediate transformations you don’t want to materialize

• Reducing database clutter

• Simple transformations used by only one or two models

Pros: No storage, cleaner database Cons: Query complexity increases, harder to debug, no direct querying

Beginner to Advanced Comparative Guide

Below is the comparative guide to help with decision making with your use case.