How to Build an ETL App for SAS Data Sets Data in Python with CData Connect AI

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

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to SAS Data Sets in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "SAS Data Sets" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SAS Data Sets.

   Set the following connection properties to connect to your SAS DataSet files:

   Connecting to Local Files

   • Set the Connection Type to "Local." Local files support SELECT, INSERT, and DELETE commands.
   • Set the URI to a folder containing SAS files, e.g. C:\PATH\TO\FOLDER\.

   Connecting to Cloud-Hosted SAS DataSet Files

   While the driver is capable of pulling data from SAS DataSet files hosted on a variety of cloud data stores, INSERT, UPDATE, and DELETE are not supported outside of local files in this driver.

   Set the Connection Type to the service hosting your SAS DataSet files. A unique prefix at the beginning of the URI connection property is used to identify the cloud data store and the remainder of the path is a relative path to the desired folder (one table per file) or single file (a single table). For more information, refer to the Getting Started section of the Help documentation.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for SAS Data Sets Data in Python

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

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Create a SQL Statement to Query SAS Data Sets

Use SQL to create a statement for querying SAS Data Sets. In this article, we read data from the restaurants entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, SASDataSets1).

sql = (
    "SELECT name, borough "
    "FROM [SASDataSets1].[SASDataSets].[restaurants] "
    "WHERE cuisine = 'American'"
)

Extract, Transform, and Load the SAS Data Sets Data

With a connection and query in hand, use petl to extract, transform, and load the SAS Data Sets data. In this example, we extract SAS Data Sets data, sort the data by the borough column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

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

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

Load New Rows Back into SAS Data Sets

When SAS Data Sets supports writes, load rows back with a batch INSERT. The SDK's executemany takes @name placeholders and a list of parameter dictionaries, one per row.

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [SASDataSets1].[SASDataSets].[restaurants] (name, borough) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")

conn.close()

Note: Even for writable sources, a read-only PAT or connection permission will reject write operations.

With the CData Connect AI Python SDK, you can work with SAS Data Sets data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live SAS Data Sets data through petl using the CData Connect AI Python SDK. For more information on connecting to SAS Data Sets (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live SAS Data Sets data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT name, borough "
    "FROM [SASDataSets1].[SASDataSets].[restaurants] "
    "WHERE cuisine = 'American'"
)

table1 = etl.fromdb(conn, sql)

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

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [SASDataSets1].[SASDataSets].[restaurants] (name, borough) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")
conn.close()