Process & Analyze SFTP Data in Databricks (AWS)

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SFTP JDBC Driver

An easy-to-use database-like interface for Java based applications and reporting tools access to remote files and directories.



Host the CData JDBC Driver for SFTP in AWS and use Databricks to perform data engineering and data science on live SFTP 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 SFTP data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live SFTP data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live SFTP 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.sftp.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for SFTP\lib).

Access SFTP Data in your Notebook: Python

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

Configure the Connection to SFTP

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

Step 1: Connection Information

driver = "cdata.jdbc.sftp.SFTPDriver"
url = "jdbc:sftp:RemoteHost=MyFTPServer;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.sftp.jar

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

SFTP can be used to transfer files to and from SFTP servers using the SFTP Protocol. To connect, specify the RemoteHost;. service uses the User and Password and public key authentication (SSHClientCert). Choose an SSHAuthMode and specify connection values based on your selection.

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 subfolders to report as views.
  • FileRetrievalDepth: Set this to retrieve files recursively and list them in the Root table.
Stored Procedures are available to download files, upload files, and send protocol commands. See gdatamodel for more on using SQL to interact with the server.

Load SFTP Data

Once you configure the connection, you can load SFTP 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" , "MyDirectory") \
	.load ()

Display SFTP Data

Check the loaded SFTP data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Filesize"))

Analyze SFTP 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 SFTP data for reporting, visualization, and analysis.

% sql

SELECT Filesize, Filename FROM SAMPLE_VIEW ORDER BY Filename DESC LIMIT 5

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