What type of data storage is commonly used in conjunction with dbt?

The correct answer is relational databases, which are commonly used in conjunction with dbt because dbt is designed to work within a SQL-based environment and primarily targets analytics workflows that require structured data. Relational databases, such as PostgreSQL, MySQL, or Snowflake, provide a schema-based structure that aligns well with dbt's capabilities, allowing users to build transformation models using SQL.

In the dbt workflow, data is often extracted and loaded into a relational database where it can be transformed, analyzed, and reported. The structured nature of relational databases supports the complex querying and data integrity features that dbt leverages for building reliable analytics.

While flat files can be used for data storage, they lack the functional capabilities and scalability that relational databases provide, particularly in terms of handling large datasets and supporting concurrent queries. NoSQL databases, while increasingly popular for certain applications, do not provide the structured SQL interface that dbt relies on, making them less compatible with dbt’s standard usage. Data lakes, although they store a variety of data types, are more focused on raw and unstructured data, which is not the primary design for dbt’s transformation and analytic functions.