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.
1. Views
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
models:
- 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
models:
- 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
models:
- 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
models:
- 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.
Get more useful articles on dbt
