Materializations in DBT are a way to define how the output of a model should be stored in the destination database. There are several built-in materializations in DBT, such as table, view, incremental, and ephemeral. Each has its use case depending on the requirements and the underlying data. Let’s discuss each one in detail, along with examples.
-
Table materialization:
This materialization creates a new table every time a model is run, replacing the existing table in the process. It’s ideal for cases where data changes are infrequent, or when you want to rebuild the entire table from scratch.
Example use case: You have a small dataset of customer information, and you want to transform and store this data in a structured format. Since the dataset is small and changes infrequently, you can use the table materialization.
-
View materialization:
View materialization creates a database view instead of a physical table. A view is a virtual table that’s based on the result set of a SQL query. It doesn’t store data, but rather retrieves it from the underlying tables when queried. This materialization is useful when you want to keep storage costs low and can afford the performance overhead of re-running the query each time the data is accessed.
Example use case: You have a dataset of daily sales transactions and want to create a view that calculates the average transaction amount per day. Since the underlying data changes frequently and the calculation is relatively simple, you can use the view materialization.
-
Incremental materialization:
Incremental materialization is designed for large datasets where rebuilding the entire table would be time-consuming or resource-intensive. Instead of recreating the table from scratch, this materialization only processes the new or updated records, appending or updating them in the destination table.
Example use case: You have a large dataset of user events (e.g., page views, clicks) that’s constantly growing. Rebuilding the entire table each time new events are recorded would be inefficient. Instead, you can use incremental materialization to process only new records, reducing processing time and resource usage.
-
Ephemeral materialization:
Ephemeral materialization doesn’t create a table or view in the database. Instead, it generates a subquery that’s inserted into the SQL query of the downstream models. This is useful when you have intermediate transformations that don’t need to be materialized as standalone tables or views.
Example use case: You have a dataset of user events and want to calculate the number of events per user per day. You first need to clean and normalize the data, but you don’t want to store the intermediate results in a separate table or view. In this case, you can use ephemeral materialization for the intermediate transformation.
To choose the right materialization for a specific use case, consider the following factors:
- Data size and frequency of changes: If your dataset is small or changes infrequently, table materialization might be suitable. For large, frequently changing datasets, incremental materialization could be more efficient.
- Storage costs and performance: If storage costs are a concern, view materialization can help reduce them. However, be mindful of the performance overhead when querying views.
- Intermediate transformations: If you have intermediate transformations that don’t need to be materialized as separate tables or views, consider using ephemeral materialization.
- Query complexity: Complex queries and transformations may benefit from being broken down into smaller, more manageable pieces using ephemeral materialization.
By evaluating your use case against these factors, you can choose the most suitable materialization for your DBT models.
Get more useful articles on dbt
