![]() Click New in the sidebar, then click Notebook.To get started writing and executing interactive code on Azure Databricks, create a notebook. To learn more about Databricks clusters, see Compute. Click Create compute to create the cluster.Select the desired Databricks runtime version, 11.1 or above to use Unity Catalog.Make sure your email address is visible in the Single User field.Select Single User from the Access mode dropdown.Click New in the sidebar, then select Cluster.To do exploratory data analysis and data engineering, create a cluster to provide the compute resources needed to execute commands. If you only have access to the Databricks SQL workspace, see Set up your workspace to use Databricks SQL. If you do not have cluster control privileges, you can still complete most of the steps below as long as you have access to a cluster. Databricks SQL allows analysts to run SQL queries against the same tables used in production ETL workloads, allowing for real time business intelligence at scale. Unity Catalog allows data stewards to configure and secure storage credentials, external locations, and database objects for users throughout an organization. Querying Unity Catalog tables from Databricks SQLĪzure Databricks provides a suite of production-ready tools that allow data professionals to quickly develop and deploy extract, transform, and load (ETL) pipelines.Scheduling a notebook as a Databricks job.Executing notebook cells to process, query, and preview data.Configuring incremental data ingestion to a Unity Catalog table with Auto Loader.Writing and reading data from a Unity Catalog external location.Launching a Unity Catalog enabled compute cluster.Tasks in this tutorialīy the end of this article, you will feel comfortable: If you are not using Unity Catalog, see Run your first ETL workload on Azure Databricks. This tutorial uses interactive notebooks to complete common ETL tasks in Python on Unity Catalog enabled clusters.
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