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

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

   Set the following to establish a connection to Paylocity:

   • RSAPublicKey: Set this to the RSA Key associated with your Paylocity, if the RSA Encryption is enabled in the Paylocity account.

     This property is required for executing Insert and Update statements, and it is not required if the feature is disabled.

   • UseSandbox: Set to true if you are using sandbox account.
   • CustomFieldsCategory: Set this to the Customfields category. This is required when IncludeCustomFields is set to true. The default value for this property is PayrollAndHR.
   • Key: The AES symmetric key(base 64 encoded) encrypted with the Paylocity Public Key. It is the key used to encrypt the content.

     Paylocity will decrypt the AES key using RSA decryption.
     It is an optional property if the IV value not provided, The driver will generate a key internally.

   • IV: The AES IV (base 64 encoded) used when encrypting the content. It is an optional property if the Key value not provided, The driver will generate an IV internally.

   Connect Using OAuth Authentication

   You must use OAuth to authenticate with Paylocity. OAuth requires the authenticating user to interact with Paylocity using the browser. For more information, refer to the OAuth section in the Help documentation.

   The Pay Entry API

   The Pay Entry API is completely separate from the rest of the Paylocity API. It uses a separate Client ID and Secret, and must be explicitly requested from Paylocity for access to be granted for an account. The Pay Entry API allows you to automatically submit payroll information for individual employees, and little else. Due to the extremely limited nature of what is offered by the Pay Entry API, we have elected not to give it a separate schema, but it may be enabled via the UsePayEntryAPI connection property.

   Please be aware that when setting UsePayEntryAPI to true, you may only use the CreatePayEntryImportBatch & MergePayEntryImportBatchgtable stored procedures, the InputTimeEntry table, and the OAuth stored procedures. Attempts to use other features of the product will result in an error. You must also store your OAuthAccessToken separately, which often means setting a different OAuthSettingsLocation when using this connection property.

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 Paylocity 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 Paylocity

Use SQL to create a statement for querying Paylocity. In this article, we read data from the Employee entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Paylocity1).

sql = (
    "SELECT FirstName, LastName "
    "FROM [Paylocity1].[Paylocity].[Employee] "
    "WHERE EmployeeId = '1234'"
)

Extract, Transform, and Load the Paylocity Data

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

table1 = etl.fromdb(conn, sql)

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

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

Load New Rows Back into Paylocity

When Paylocity 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 [Paylocity1].[Paylocity].[Employee] (FirstName, LastName) "
    "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 Paylocity 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 Paylocity data through petl using the CData Connect AI Python SDK. For more information on connecting to Paylocity (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Paylocity 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 FirstName, LastName "
    "FROM [Paylocity1].[Paylocity].[Employee] "
    "WHERE EmployeeId = '1234'"
)

table1 = etl.fromdb(conn, sql)

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

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [Paylocity1].[Paylocity].[Employee] (FirstName, LastName) "
    "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()