DBT : Trade-offs between different materialization strategies in DBT


The Art of Balance in DBT: Trading Off Between Materialization Strategies

In the complex world of data transformation, choosing the right materialization strategy is akin to finding the perfect balance in a piece of art. DBT (Data Build Tool) provides several strategies, each with its unique characteristics and trade-offs. This article dives into these trade-offs, focusing on refresh frequency, storage costs, and query performance.


Views are virtual tables that are re-computed every time they’re queried.

  • Refresh Frequency: Always up-to-date as they’re computed on-the-fly.
  • Storage Costs: Minimal, as data isn’t physically stored.
  • Query Performance: Can be slower, especially for complex queries, as they’re computed at query time.
Example: A View for Real-Time Analytics
  - name: real_time_orders
    materialized: view

This view is suitable for real-time analytics, but may suffer from slower query performance for complex analyses.

2. Tables

Tables physically store data, leading to different trade-offs.

  • Refresh Frequency: Dependent on how often the table is rebuilt.
  • Storage Costs: Higher, as data is physically stored.
  • Query Performance: Typically faster compared to views, as data is pre-computed.
Example: A Table for Frequent Queries
  - name: product_catalog
    materialized: table

This table is great for frequently queried data, at the expense of storage.

3. Ephemeral Models

Ephemeral models are temporary structures used within a DBT run.

  • Refresh Frequency: Not applicable, as they don’t persist beyond a run.
  • Storage Costs: None, as they’re temporary.
  • Query Performance: Similar to views but limited to the scope of the DBT run.
Example: An Ephemeral Model for Intermediate Computation
  - name: temporary_computation
    materialized: ephemeral

This ephemeral model aids in complex transformations without storage overhead.

4. Incremental Models

Incremental models build upon existing tables, adding only new data.

  • Refresh Frequency: Incremental, adding only changed data.
  • Storage Costs: Similar to tables but optimized for incremental changes.
  • Query Performance: Optimized for handling large datasets with small, frequent changes.
Example: An Incremental Model for Daily Sales
  - name: daily_sales
    materialized: incremental

Incremental models like this balance the need for regular updates with efficiency.

DBT’s materialization strategies offer a rich tapestry of choices, each with its unique trade-offs. Whether it’s the real-time reflection of views, the solid performance of tables, the transient nature of ephemeral models, or the evolving capability of incremental models, understanding these trade-offs is key.

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