Delta Live Tables (DLT): DLT is an ETL (Extract, Transform, Load) framework designed to simplify the creation and management of data pipelines. It uses a declarative approach to build reliable data pipelines and automatically manages infrastructure at scale.
dbt (data build tool): dbt is primarily focused on the "Transform" part of ETL. It allows data analysts and engineers to transform data in their data warehouse using SQL.
Key Differences:
- Language Support: DLT supports both Python and SQL, while dbt is limited to SQL.
- Streaming: DLT has native support for streaming data, whereas dbt can handle streaming via the dbt-databricks package.
- Infrastructure Management: DLT manages the underlying compute resources and integrates with Databricks Workflows, while dbt requires external orchestration tools.
- Scope: DLT handles the full ETL process, whereas dbt focuses only on the transformation step.
Currently, you cannot use dbt to directly create Delta Live Tables. They are separate tools with different functionalities and purposes. However, you can use dbt to transform data within Databricks and then use that transformed data in Delta Live Tables pipelines.
