How to Build an ETL App for SAS xpt 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 SASxpt and the petl framework, you can build SAS xpt-connected applications and pipelines for extracting, transforming, and loading SAS xpt data. This article shows how to connect to SAS xpt with the CData Python Connector and use petl and pandas to extract, transform, and load SAS xpt data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live SAS xpt data in Python. When you issue complex SQL queries from SAS xpt, the driver pushes supported SQL operations, like filters and aggregations, directly to SAS xpt and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to SAS xpt Data

Connecting to SAS xpt 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.

Connecting to Local SASXpt Files

You can connect to local SASXpt file by setting the URI to a folder containing SASXpt files.

Connecting to S3 data source

You can connect to Amazon S3 source to read SASXpt files. Set the following properties to connect:

• URI: Set this to the folder within your bucket that you would like to connect to.
• AWSAccessKey: Set this to your AWS account access key.
• AWSSecretKey: Set this to your AWS account secret key.
• TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

Connecting to Azure Data Lake Storage Gen2

You can connect to ADLS Gen2 to read SASXpt files. Set the following properties to connect:

• URI: Set this to the name of the file system and the name of the folder which contacts your SASXpt files.
• AzureAccount: Set this to the name of the Azure Data Lake storage account.
• AzureAccessKey: Set this to our Azure DataLakeStore Gen 2 storage account access key.
• TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

After installing the CData SAS xpt Connector, follow the procedure below to install the other required modules and start accessing SAS xpt 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 SAS xpt 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.sasxpt as mod

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

cnxn = mod.connect("URI=C:/folder;")

Create a SQL Statement to Query SAS xpt

Use SQL to create a statement for querying SAS xpt. In this article, we read data from the SampleTable_1 entity.

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'"

Extract, Transform, and Load the SAS xpt Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the SAS xpt data. In this example, we extract SAS xpt data, sort the data by the Column1 column, and load the data into a CSV file.

Loading SAS xpt Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

etl.tocsv(table2,'sampletable_1_data.csv')

With the CData Python Connector for SASxpt, you can work with SAS xpt 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 SASxpt to start building Python apps and scripts with connectivity to SAS xpt 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.sasxpt as mod

cnxn = mod.connect("URI=C:/folder;")

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

etl.tocsv(table2,'sampletable_1_data.csv')