In Azure Databricks, workspaces are functional instances of Apache Spark that classify objects like experiments, libraries, queries, notebooks, and dashboards into folders. They provide access to data, jobs, and clusters, thereby serving as an environment for accessing all Azure Databricks assets. Workspaces can be managed using Databricks CLI, workspace UI, and Databricks REST API reference. The workspace UI is most commonly used to manage workspaces. There can be multiple workspaces in Azure Databricks for different projects. Every workspace has a code editor, a debugger, and Machine Learning (ML) & SQL libraries as its main components. Multiple other components exist in a workspace that performs different functions. Data is imported from Azure Data Factory into Azure for the big data pipeline and is stored in a data lake. Azure Databricks reads data from several resources and transforms it into actionable insights.