Table Materialization

A table materialization rebuilds your model as a physical table during each dbt run. Unlike views, tables store the actual data rather than just the query logic, using a CREATE TABLE AS statement.

This means:

• Data is physically stored in your warehouse

• Queries are faster but builds take longer

• Data isn't automatically updated when source data changes

Tables are ideal when query performance is more important than build time or real-time data needs.

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Working with Tables

Let's understand tables through a simple example:

-- models/marts/customer_orders.sql
SELECT
    customer_id,
    COUNT(*) as order_count,
    SUM(amount) as total_spent,
    MAX(order_date) as last_order_date
FROM {{ ref('stg_orders') }}
GROUP BY customer_id

When dbt runs this model:

1. It creates a new table in your warehouse

2. The query results are stored in your database

3. Future queries read from stored data, not recompute

Under the hood, dbt executes a CREATE TABLE statement:

CREATE OR REPLACE TABLE "database"."schema"."customer_orders" AS (
    SELECT * FROM source_table WHERE condition = true
);

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When to Use Tables

Tables are ideal for:

• Performance-critical models (BI dashboards, reporting)

• Complex transformations (heavy aggregations, window functions)

• Frequently accessed data (repeated queries)

• Downstream dependencies (multiple models referencing the same data)

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Configuration

Views can be configured in your model files or project configuration.

Model-Level Configuration

-- In your model SQL file
{{
    config(
        materialized='table'
    )
}}

SELECT
    customer_id,
    product_id,
    SUM(purchase_amount) as total_purchases,
    COUNT(*) as purchase_count
FROM {{ ref('stg_transactions') }}
GROUP BY customer_id, product_id

Project-Level Configuration

# In your dbt_project.yml file
models:
  your_project:
    marts:
      +materialized: table

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Performance and Usage Guidelines

Advantages and Limitations

Advantages	Limitations
⚡ Fast query performance	🕒 Slower build times
📊 Efficient for BI tools	🔄 No automatic updates
💪 Great for complex queries	💾 Uses more storage
🔀 Ideal for multiple users	📈 Higher warehouse costs

When to Change Materialization

Consider using a table when you need:

• Fast query performance

• Support for heavy BI workloads

• Efficient complex transformations

• High concurrency access

Best Practices

1. Choose Strategically: Use tables for final reporting layers and complex transformations

2. Monitor Costs: Balance storage costs with performance needs

3. Optimize Build Times: Consider incremental models for large tables

Tables are ideal for performance-critical models and complex transformations. Use them when query speed is more important than build time or real-time data needs.