How to connect and process FTP 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 FTP data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live FTP data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live FTP data. When you issue complex SQL queries to FTP, the driver pushes supported SQL operations, like filters and aggregations, directly to FTP 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 FTP data using native data types.

Install the CData JDBC Driver in Azure

To work with live FTP 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 "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.ftp.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to FTP from Databricks

With the JAR file installed, we are ready to work with live FTP 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 FTP, and create a basic report.

Configure the Connection to FTP

Connect to FTP 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.ftp.FTPDriver"
url = "jdbc:ftp:RTK=5246...;RemoteHost=MyFTPServer;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the FTP 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.

To connect to FTP or SFTP servers, specify at least RemoteHost and FileProtocol. Specify the port with RemotePort.

Set User and Password to perform Basic authentication. Set SSHAuthMode to use SSH authentication. See the Getting Started section of the data provider help documentation for more information on authenticating via SSH.

Set SSLMode and SSLServerCert to secure connections with SSL.

The data provider lists the tables based on the available folders in your FTP server. Set the following connection properties to control the relational view of the file system:

• RemotePath: Set this to the current working directory.
• TableDepth: Set this to control the depth of folders to list as views.
• FileRetrievalDepth: Set this to retrieve and list files recursively from the root table.

Stored Procedures are available to download files, upload files, and send protocol commands. See the Data Model chapter of the FTP data provider documentation for more information.

Load FTP Data

Once the connection is configured, you can load FTP 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" , "MyDirectory") \
	.load ()

Display FTP Data

Check the loaded FTP data by calling the display function.

display (remote_table.select ("Filesize"))

Analyze FTP 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 FTP data for analysis.

% sql

SELECT Filesize, Filename FROM MyDirectory

The data from FTP 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 FTP and start working with your live FTP data in Azure Databricks. Reach out to our Support Team if you have any questions.