Automating Oracle CPQ Data Loading Using API and Excel

In the fast-paced sales environment, keeping product data in Oracle CPQ up-to-date is crucial for accurate quoting and efficient sales processes. Manual data updates can be time-consuming and prone to errors. Automating data loading using Oracle CPQ’s API through Excel not only streamlines the process but also minimizes errors, ensuring that your CPQ system reflects the most current product information.

This technical article provides a step-by-step guide to setting up an automated process for loading data into Oracle CPQ from Excel using the API. It includes setup instructions, sample code, and example Excel data to help you get started.

Overview

The process involves:

1. Preparing your Excel data: Structuring your Excel file with the data to be loaded into Oracle CPQ.
2. Writing a script to read Excel data: Using a programming language like Python to read the data from the Excel file.
3. Using Oracle CPQ’s API to load data: Sending the data from the Excel file to Oracle CPQ via its API.

Prerequisites

• Oracle CPQ API Access: Ensure you have API access enabled in Oracle CPQ with the necessary permissions for data loading.
• Excel File with Data: Prepare an Excel file with the product data to be uploaded.
• Python Environment: Set up a Python environment for running scripts. This example uses Python, given its excellent support for working with APIs and Excel files.

Step 1: Preparing Your Excel Data

Your Excel file should be structured in a way that each column represents a field in Oracle CPQ, and each row represents a record (e.g., a product). For example:

Save this Excel file in an accessible location on your computer.

Step 2: Writing a Script to Read Excel Data

You will need to write a script that reads the Excel file and prepares the data for uploading. This example uses Python with the pandas library for reading Excel files and the requests library for making API calls.

Sample Python Script

First, install the necessary Python libraries:

pip install pandas requests

Then, write the script:

import pandas as pd
import requests
import json

# Load the Excel file
excel_data_df = pd.read_excel('path/to/your/excelfile.xlsx')

# Convert the Excel data to a JSON string
data_json = excel_data_df.to_json(orient='records')

# Parse the JSON string into a list
data_list = json.loads(data_json)

# Oracle CPQ API endpoint
api_endpoint = 'https://yourcpqdomain.com/rest/v1/products'

# Headers for the API call, including the API key or token for authentication
headers = {
    'Authorization': 'Bearer YOUR_ACCESS_TOKEN',
    'Content-Type': 'application/json'
}

# Loop through each record in the data list and make an API call to Oracle CPQ
for record in data_list:
    response = requests.post(api_endpoint, headers=headers, json=record)
    if response.status_code == 200:
        print("Data loaded successfully for:", record['Model Number'])
    else:
        print("Failed to load data for:", record['Model Number'], "; Response:", response.text)

Replace 'path/to/your/excelfile.xlsx' with the actual path to your Excel file, https://yourcpqdomain.com/rest/v1/products with your actual Oracle CPQ API endpoint, and 'Bearer YOUR_ACCESS_TOKEN' with your actual API token or key.

Step 3: Using Oracle CPQ’s API to Load Data

The script above handles this step by looping through each record in the Excel file, converting it to JSON, and making a POST request to Oracle CPQ’s API endpoint. Each record is uploaded individually, allowing for error handling on a per-record basis.

Conclusion

Automating data loading into Oracle CPQ using its API and Excel streamlines the update process, reduces errors, and ensures that your CPQ system uses the most current product data. By following the steps outlined in this guide and using the provided sample code, you can set up an efficient and reliable data loading process. This automation not only saves time but also enhances the overall accuracy and efficiency of your CPQ system.