(DABs), which in April 2024, is becoming the go-to tool for packaging and deploying Databricks assets that facilitates the adoption of software engineering practices in our data teams. DABs supports packaging the configuration of workflows and tasks, as well as the code to be executed in those tasks, as a bundle that can be deployed to multiple environments through CI/CD pipelines. It comes with templates for common types of assets and supports custom templates, which allows for the creation of tailored service templates for data engineering and ML projects. Our teams have increasingly adopted it as a key part of their engineering workflows. Even though DABs includes templates for notebooks and supports deploying them to production, we don't recommend productionizing notebooks and instead encourage intentionally writing production code with the engineering practices that support the maintainability, resiliency and scalability needs of such workloads.
The recent of (DABs), included with , is becoming the officially recommended way to package Databricks assets for source control, testing and deployment. It has started to replace among our teams. DABs supports packaging the configuration of workflows, jobs and tasks, as well as the code to be executed in those tasks, as a bundle that can be deployed to multiple environments. It comes with templates for common types of assets and supports custom templates. While DABs includes templates for notebooks and supports deploying them to production, we continue to recommend against productionizing notebooks and instead encourage intentionally writing production code with the engineering practices that support the maintainability, resiliency and scalability needs of such workloads.
