How to connect and process Presto Data from Azure Databricks

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 Azure, 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.

Getting Started

---

Install the CData JDBC Driver in Azure

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

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "DBFS" 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).

Connect to Presto from Databricks

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 workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).

Configure the Connection to Presto

Connect to Presto by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

driver = "cdata.jdbc.presto.PrestoDriver"
url = "jdbc:presto:RTK=5246...;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.

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

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.

display (remote_table.select ("FirstName"))

Analyze Presto Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the Presto data for analysis.

result = spark.sql("SELECT FirstName, LastName FROM SAMPLE_VIEW WHERE Id = '123456789'")

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 Azure Databricks. Reach out to our Support Team if you have any questions.