DBT : Materialization strategies available in DBT, such as views, tables, and ephemeral models


Sculpting Data in DBT: A Craftsmen’s Guide to Materialization Strategies

DBT (Data Build Tool) is a powerful instrument in the data professional’s toolkit, enabling seamless transformation and modeling of data. One of the distinguishing features of DBT is its materialization strategies. In this article, we’ll unravel these strategies, shedding light on views, tables, and ephemeral models, each with their specific use cases and benefits.


In DBT, views are SQL queries stored in the database. They don’t consume storage space like tables, as the data isn’t physically stored. Views are ideal when you want up-to-date data without storage overhead.

Example: Creating a View in DBT
  - name: freshers_customer_orders_view
    materialized: view

This DBT model creates a view that can be queried just like a table, but without persisting the data.

2. Tables

Tables in DBT are physical storage structures that persist data. They are suitable when performance is key, as reading from a table is typically faster than a view. Tables are best for heavily queried or large datasets.

Example: Creating a Table in DBT
  - name: freshers_sales_summary
    materialized: table

This model will create a physical table freshers_sales_summary, allowing for quick data retrieval.

3. Ephemeral Models

Ephemeral models in DBT are temporary and only exist during the execution of a DBT run. They are perfect for intermediate transformations that don’t need to be stored or reused outside of a specific DBT run.

Example: Creating an Ephemeral Model in DBT
  - name: temp_customer_analysis
    materialized: ephemeral

This code defines an ephemeral model that only exists during the DBT run, perfect for temporary or intermediate calculations.

4. Incremental Models

Incremental models allow data to be added to an existing table incrementally, reducing the processing time and resources. They are particularly useful for large datasets where only a small portion changes regularly.

Example: Creating an Incremental Model in DBT
  - name: freshers_daily_sales
    materialized: incremental

With an incremental model, new data can be added to the freshers_daily_sales table, without having to rebuild the entire table.

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