How to Build an ETL App for SFTP Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for SFTP and the petl framework, you can build SFTP-connected applications and pipelines for extracting, transforming, and loading SFTP data. This article shows how to connect to SFTP with the CData Python Connector and use petl and pandas to extract, transform, and load SFTP data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live SFTP data in Python. When you issue complex SQL queries from 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).

Connecting to SFTP Data

Connecting to SFTP data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

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.

After installing the CData SFTP Connector, follow the procedure below to install the other required modules and start accessing SFTP through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for SFTP Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.sftp as mod

You can now connect with a connection string. Use the connect function for the CData SFTP Connector to create a connection for working with SFTP data.

cnxn = mod.connect("RemoteHost=MyFTPServer;")

Create a SQL Statement to Query SFTP

Use SQL to create a statement for querying SFTP. In this article, we read data from the MyDirectory entity.

sql = "SELECT Filesize, Filename FROM MyDirectory WHERE FilePath = '/documents/doc.txt'"

Extract, Transform, and Load the SFTP Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the SFTP data. In this example, we extract SFTP data, sort the data by the Filename column, and load the data into a CSV file.

Loading SFTP Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Filename')

etl.tocsv(table2,'mydirectory_data.csv')

In the following example, we add new rows to the MyDirectory table.

Adding New Rows to SFTP

table1 = [ ['Filesize','Filename'], ['NewFilesize1','NewFilename1'], ['NewFilesize2','NewFilename2'], ['NewFilesize3','NewFilename3'] ]

etl.appenddb(table1, cnxn, 'MyDirectory')

With the CData Python Connector for SFTP, you can work with SFTP data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for SFTP to start building Python apps and scripts with connectivity to SFTP data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.sftp as mod

cnxn = mod.connect("RemoteHost=MyFTPServer;")

sql = "SELECT Filesize, Filename FROM MyDirectory WHERE FilePath = '/documents/doc.txt'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Filename')

etl.tocsv(table2,'mydirectory_data.csv')

table3 = [ ['Filesize','Filename'], ['NewFilesize1','NewFilename1'], ['NewFilesize2','NewFilename2'], ['NewFilesize3','NewFilename3'] ]

etl.appenddb(table3, cnxn, 'MyDirectory')