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Tutorial: Query and visualize data from a notebook - Azure Databricks

This get started article walks you through using an Azure Databricks notebook to query sample data stored in Unity Catalog using SQL, Python, Scala, and R and then visualize the query results in the notebook.

To complete the tasks in this article, you must meet the following requirements:

• Your workspace must have Unity Catalog enabled. For information on getting started with Unity Catalog, see Get started with Unity Catalog.
• You must have permission to use an existing compute resource or create a new compute resource. See Get started tutorials on Azure Databricks or see your Databricks administrator.

To create a notebook in your workspace, click New in the sidebar, and then click Notebook. A blank notebook opens in the workspace.

To learn more about creating and managing notebooks, see Manage notebooks.

Query the samples.nyctaxi.trips table in Unity Catalog using the language of your choice.

1. Copy and paste the following code into the new empty notebook cell. This code displays the results from querying the samples.nyctaxi.trips table in Unity Catalog.

   SQL

   SELECT * FROM samples.nyctaxi.trips
   

   Python

   display(spark.read.table("samples.nyctaxi.trips"))
   

   Scala

   display(spark.read.table("samples.nyctaxi.trips"))
   

   R

   library(SparkR)
   display(sql("SELECT * FROM samples.nyctaxi.trips"))
   

2. Press Shift+Enter to run the cell and then move to the next cell.

   The query results appear in the notebook.

Display the average fare amount by trip distance, grouped by the pickup zip code.

1. Next to the Table tab, click + and then click Visualization.

   The visualization editor displays.

2. In the Visualization Type drop-down, verify that Bar is selected.

3. Select fare_amount for the X column.

4. Select trip_distance for the Y column.

5. Select Average as the aggregation type.

6. Select pickup_zip as the Group by column.

7. Click Save.

• To learn about adding data from CSV file to Unity Catalog and visualize data, see Tutorial: Import and visualize CSV data from a notebook.
• To learn how to load data into Databricks using Apache Spark, see Tutorial: Load and transform data using Apache Spark DataFrames.
• To learn more about ingesting data into Databricks, see Standard connectors in Lakeflow Connect.
• To learn more about querying data with Databricks, see Query data.
• To learn more about visualizations, see Visualizations in Databricks notebooks and SQL editor.
• To learn more about exploratory data analysis (EDA) techniques, see Tutorial: EDA techniques using Databricks notebooks.

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