The article concludes a series on implementing the Medallion Architecture with Unity Catalog in Databricks by exploring automation. It discusses utilizing Gitlab, Databricks Assets Bundles, and jobs to streamline the management of data workflows. Emphasis is laid on the importance of using Databricks' REST API for orchestrating resources and workflows effectively. This final part aims to help practitioners fully automate their machine learning processes, ensuring efficient deployment and operation, ultimately leading to improved scalability and reliability of their data operations.
In principle, all these tools build around Databricks Rest API, which allows us to manage and control Databricks resources such as clusters, workspaces, workflows, and machine learning experiments.
In this last part we take see how we can automate the whole process using Gitlab, Databricks Assets Bundles, and Databricks jobs.
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