This tutorial gets you going with Databricks Data Science & Engineering: you create a cluster and a notebook, create a table from a dataset, query the table, and display the query results.
You are logged into Databricks, and you’re in the Data Science & Engineering workspace. See Set up your Databricks on Google Cloud account.
From the sidebar at the left and the Common Tasks list on the landing page, you access fundamental Databricks Data Science & Engineering entities: the 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.
A cluster is a collection of Databricks computation resources. To create a cluster:
In the sidebar, click Compute.
On the Compute page, click Create Cluster.
On the Create Cluster page, specify the cluster name Quickstart and select 7.3 LTS (Scala 2.12, Spark 3.0.1) in the Databricks Runtime Version drop-down.
Click Create Cluster.
A notebook is a collection of cells that run computations on an Apache Spark cluster. To create a notebook in the Workspace:
In the sidebar, click Workspace.
In the Workspace folder, select Create > Notebook.
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.
Click Create. The notebook opens with an empty cell at the top.
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.
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")
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.
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")
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.
Run a SQL statement to query the table for the average diamond price by color.
To add a cell to the notebook, mouse over the cell bottom and click the icon.
Copy this snippet and paste it in the cell.
SELECT color, avg(price) AS price FROM diamonds GROUP BY color ORDER BY COLOR
Press SHIFT + ENTER. The notebook displays a table of diamond color and average price.
Display a chart of the average diamond price by color.
Click the Bar chart icon .
Click Plot Options.
Drag color into the Keys box.
Drag price into the Values box.
In the Aggregation drop-down, select AVG.
Click Apply to display the bar chart.
We’ve now covered the basics of Databricks Data Science & Engineering, including creating a cluster and a notebook, running SQL commands in the notebook, and displaying results.
To dive into various Apache Spark articles, see Introduction to Apache Spark.
To read more about the primary tools you use and tasks you can perform with Databricks Data Science & Engineering workspace, see:
To see some interesting applications of Databricks, watch these videos: