Extract, Transform, and Load ADP Data in Python

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

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

Connecting to ADP Data

Connecting to ADP 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.

Connect to ADP by specifying the following properties:

• SSLClientCert: Set this to the certificate provided during registration.
• SSLClientCertPassword: Set this to the password of the certificate.
• UseUAT: The connector makes requests to the production environment by default. If using a developer account, set UseUAT = true.
• RowScanDepth: The maximum number of rows to scan for the custom fields columns available in the table. The default value will be set to 100. Setting a high value may decrease performance.

The connector uses OAuth to authenticate with ADP. OAuth requires the authenticating user to interact with ADP using the browser. For more information, refer to the OAuth section in the Help documentation.

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

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

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;SSLClientCert='c:\cert.pfx';SSLClientCertPassword='admin@123'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query ADP

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

sql = "SELECT AssociateOID, WorkerID FROM Workers WHERE AssociateOID = 'G3349PZGBADQY8H8'"

Extract, Transform, and Load the ADP Data

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

Loading ADP Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to ADP

table1 = [ ['AssociateOID','WorkerID'], ['NewAssociateOID1','NewWorkerID1'], ['NewAssociateOID2','NewWorkerID2'], ['NewAssociateOID3','NewWorkerID3'] ]

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

With the CData Python Connector for ADP, you can work with ADP 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 ADP to start building Python apps and scripts with connectivity to ADP 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.adp as mod

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;SSLClientCert='c:\cert.pfx';SSLClientCertPassword='admin@123'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT AssociateOID, WorkerID FROM Workers WHERE AssociateOID = 'G3349PZGBADQY8H8'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['AssociateOID','WorkerID'], ['NewAssociateOID1','NewWorkerID1'], ['NewAssociateOID2','NewWorkerID2'], ['NewAssociateOID3','NewWorkerID3'] ]

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