How to Build an ETL App for ADP 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 ADP-connected applications and pipelines for extracting, transforming, and loading ADP data. This article shows how to connect to Connect AI and use petl to extract, transform, and load ADP 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 ADP 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 "ADP" from the Add Connection panel

3. Enter the necessary authentication properties to connect to ADP.

   Connect to ADP by specifying the following properties:

   • OAuthClientId: The client Id of the custom OAuth application you obtained from ADP.
   • OAuthClientSecret: The custom OAuth application's client secret.
   • 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. OAuth access can be configured in ADP through ADP API Central. For more information, refer ADP's API Central Quick Start Guide and the OAuth section in CData's 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 ADP 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 ADP

Use SQL to create a statement for querying ADP. In this article, we read data from the Workers entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, ADP1).

sql = (
    "SELECT AssociateOID, WorkerID "
    "FROM [ADP1].[ADP].[Workers] "
    "WHERE AssociateOID = 'G3349PZGBADQY8H8'"
)

Extract, Transform, and Load the ADP Data

With a connection and query in hand, 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.

table1 = etl.fromdb(conn, sql)

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

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

Load New Rows Back into ADP

When ADP 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 [ADP1].[ADP].[Workers] (AssociateOID, WorkerID) "
    "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 ADP 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 ADP data through petl using the CData Connect AI Python SDK. For more information on connecting to ADP (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live ADP 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 AssociateOID, WorkerID "
    "FROM [ADP1].[ADP].[Workers] "
    "WHERE AssociateOID = 'G3349PZGBADQY8H8'"
)

table1 = etl.fromdb(conn, sql)

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

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [ADP1].[ADP].[Workers] (AssociateOID, WorkerID) "
    "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()