Get started as a Databricks user

This tutorial gets you going with Databricks: you create a cluster and a notebook, create a table from a dataset, query the table, and display the query results.

Tip

As a supplement to this article, check out the Quickstart Tutorial notebook, available on your Databricks workspace landing page, for a 5-minute hands-on introduction to Databricks. Simply log in to your Databricks workspace and click Explore the Quickstart Tutorial.

Requirements

You are logged into a Databricks workspace. See Sign up for a free Databricks trial.

Step 1: Orient yourself to the Databricks UI

Landing page

From the sidebar at the left and the Common Tasks list on the landing page, you access fundamental Databricks entities: Workspace, clusters, tables, notebooks, jobs, and libraries. The Workspace is the special root folder that stores your Databricks assets, such as notebooks and libraries, and the data that you import.

To get help, click the question icon Question Icon at the top right-hand corner.

Help menus

Step 2: Create a cluster

A cluster is a collection of Databricks computation resources. To create a cluster:

  1. In the sidebar, click the Clusters button Clusters Icon.

  2. On the Clusters page, click Create Cluster.

    Create cluster

  3. On the Create Cluster page, specify the cluster name Quickstart and select 6.3 (Scala 2.11, Spark 2.4.4) in the Databricks Runtime Version drop-down.

  4. Click Create Cluster.

Step 3: Create a notebook

A notebook is a collection of cells that run computations on an Apache Spark cluster. To create a notebook in the Workspace:

  1. In the sidebar, click the Workspace button Workspace Icon.

  2. In the Workspace folder, select Down Caret Create > Notebook.

    Create notebook

  3. On the Create Notebook dialog, enter a name and select SQL in the Language drop-down. This selection determines the default language of the notebook.

  4. Click Create. The notebook opens with an empty cell at the top.

Step 4: Create a table

Create a table using data from a sample CSV data file available in Databricks datasets, a collection of datasets mounted to Databricks File System (DBFS), a distributed file system installed on Databricks clusters. You have two options for creating the table.

Option 1: Create a Spark table from the CSV data

Use this option if you want to get going quickly, and you only need standard levels of performance. Copy and paste this code snippet into a notebook cell:

DROP TABLE IF EXISTS diamonds;

CREATE TABLE diamonds USING CSV OPTIONS (path "/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv", header "true")

Option 2: Write the CSV data to Delta Lake format and create a Delta table

Delta Lake offers a powerful transactional storage layer that enables fast reads and other benefits. Delta Lake format consists of Parquet files plus a transaction log. Use this option to get the best performance on future operations on the table.

  1. Read the CSV data into a DataFrame and write out in Delta Lake format. This command uses a Python language magic command, which allows you to interleave commands in languages other than the notebook default language (SQL). Copy and paste this code snippet into a notebook cell:

    %python

diamonds = spark.read.csv("/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv", header="true", inferSchema="true")
diamonds.write.format("delta").save("/mnt/delta/diamonds")

  2. Create a Delta table at the stored location. Copy and paste this code snippet into a notebook cell:

    DROP TABLE IF EXISTS diamonds;

CREATE TABLE diamonds USING DELTA LOCATION '/mnt/delta/diamonds/'

Run cells by pressing SHIFT + ENTER. The notebook automatically attaches to the cluster you created in Step 2 and runs the command in the cell.

Step 5: Query the table

Run a SQL statement to query the table for the average diamond price by color.

  1. To add a cell to the notebook, mouse over the cell bottom and click the Add Cell icon.

    Add cell

  2. Copy this snippet and paste it in the cell.

    SELECT color, avg(price) AS price FROM diamonds GROUP BY color ORDER BY COLOR

  3. Press SHIFT + ENTER. The notebook displays a table of diamond color and average price.

    Run command

Step 6: Display the data

Display a chart of the average diamond price by color.

  1. Click the Bar chart icon Chart Button.

  2. Click Plot Options.

    • Drag color into the Keys box.

    • Drag price into the Values box.

    • In the Aggregation drop-down, select AVG.

      Select aggregation

  3. Click Apply to display the bar chart.

    Apply chart type

What’s next

We’ve now covered the basics of Databricks, including creating a cluster and a notebook, running SQL commands in the notebook, and displaying results.