Process & Analyze Presto Data in Databricks (AWS)

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Presto JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Presto.



Host the CData JDBC Driver for Presto in AWS and use Databricks to perform data engineering and data science on live Presto data.

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Presto data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Presto data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Presto data. When you issue complex SQL queries to Presto, the driver pushes supported SQL operations, like filters and aggregations, directly to Presto and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Presto data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Presto data in Databricks, install the driver on your Databricks cluster.

  1. Navigate to your Databricks administration screen and select the target cluster.
  2. On the Libraries tab, click "Install New."
  3. Select "Upload" as the Library Source and "Jar" as the Library Type.
  4. Upload the JDBC JAR file (cdata.jdbc.presto.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Presto\lib).

Access Presto Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Presto data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Presto, and create a basic report.

Configure the Connection to Presto

Connect to Presto by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.presto.PrestoDriver"
url = "jdbc:presto:Server=127.0.0.1;Port=8080;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Presto JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.presto.jar

Fill in the connection properties and copy the connection string to the clipboard.

Set the Server and Port connection properties to connect, in addition to any authentication properties that may be required.

To enable TLS/SSL, set UseSSL to true.

Authenticating with LDAP

In order to authenticate with LDAP, set the following connection properties:

  • AuthScheme: Set this to LDAP.
  • User: The username being authenticated with in LDAP.
  • Password: The password associated with the User you are authenticating against LDAP with.

Authenticating with Kerberos

In order to authenticate with KERBEROS, set the following connection properties:

  • AuthScheme: Set this to KERBEROS.
  • KerberosKDC: The Kerberos Key Distribution Center (KDC) service used to authenticate the user.
  • KerberosRealm: The Kerberos Realm used to authenticate the user with.
  • KerberosSPN: The Service Principal Name for the Kerberos Domain Controller.
  • KerberosKeytabFile: The Keytab file containing your pairs of Kerberos principals and encrypted keys.
  • User: The user who is authenticating to Kerberos.
  • Password: The password used to authenticate to Kerberos.

Load Presto Data

Once you configure the connection, you can load Presto data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Customer") \
	.load ()

Display Presto Data

Check the loaded Presto data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("FirstName"))

Analyze Presto Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Presto data for reporting, visualization, and analysis.

% sql

SELECT FirstName, LastName FROM SAMPLE_VIEW ORDER BY LastName DESC LIMIT 5

The data from Presto is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Presto and start working with your live Presto data in Databricks. Reach out to our Support Team if you have any questions.