Creating an Azure-based Robotic Process Automation (RPA) sample involves leveraging Azure services to automate processes. Below is a simple project example that integrates Azure Cognitive Services and Power Automate to create an RPA system.

Project Overview: Automated Invoice Processing

• Objective: Automate the process of extracting data from invoices and saving it to a database using Azure services.
• Tech Stack: Azure Cognitive Services, Azure Logic Apps or Power Automate, Azure Storage, and a database (Azure SQL Database or a simple file-based solution).

Step-by-Step Guide

1. Set Up Azure Resources

1. Azure Form Recognizer:

   • Go to the Azure Portal.
   • Create a "Form Recognizer" resource.
   • Note the

     Endpoint

     and

     Key

     .

2. Azure Blob Storage:

   • Create a storage account and a container (e.g.,

     invoices

     ).
   • Upload sample invoice PDFs or images.

3. Database:

   • Set up an Azure SQL Database or use Azure Table Storage.
   • Define a table structure:

     CREATE TABLE Invoices (
      InvoiceId INT IDENTITY PRIMARY KEY,
      VendorName NVARCHAR(255),
      InvoiceDate DATE,
      TotalAmount DECIMAL(18, 2)
     );
     

2. Create an Azure Logic App or Power Automate Workflow

1. Trigger:

   • Set up a trigger for when a new file is added to the Blob Storage container.

2. Extract Invoice Data:

   • Use the "Azure Form Recognizer" connector in Logic Apps or Power Automate.
   • Configure it to analyze the uploaded file and extract data (e.g., vendor name, invoice date, and total amount).

3. Store Data:

   • Insert the extracted data into the Azure SQL Database using the "SQL Server" connector.

3. Azure Function for Custom Logic (Optional)

If additional processing is required, write an Azure Function in Python or C# to handle custom logic. Example:

• Azure Function: Validate extracted data before saving it to the database.

import logging
import pyodbc
import azure.functions as func

def main(req: func.HttpRequest) -> func.HttpResponse:
 logging.info('Processing invoice data...')
 # Extract data from request
 vendor_name = req.params.get('vendor_name')
 invoice_date = req.params.get('invoice_date')
 total_amount = req.params.get('total_amount')

 if not (vendor_name and invoice_date and total_amount):
 return func.HttpResponse(
 "Missing one or more required fields.",
 status_code=400
 )
 
 # Database connection
 conn = pyodbc.connect(
 'DRIVER={ODBC Driver 17 for SQL Server};'
 'SERVER=your_server;DATABASE=your_db;UID=your_user;PWD=your_password'
 )
 cursor = conn.cursor()
 cursor.execute(
 "INSERT INTO Invoices (VendorName, InvoiceDate, TotalAmount) VALUES (?, ?, ?)",
 vendor_name, invoice_date, total_amount
 )
 conn.commit()
 return func.HttpResponse("Invoice processed successfully.")

4. Test the Workflow

1. Upload a sample invoice to the Blob Storage container.
2. Monitor the Logic App/Power Automate workflow.
3. Check the SQL database for the saved data.

Technologies Used

1. Azure Cognitive Services - Form Recognizer:

   • Extracts structured data from unstructured documents.

2. Azure Blob Storage:

   • Stores the uploaded invoice files.

3. Azure Logic Apps or Power Automate:

   • Automates the workflow of file processing and data storage.

4. Azure SQL Database:

   • Stores extracted data in a structured format.

Extensions

1. Notifications:

   • Use Power Automate to send an email or Teams message when a new invoice is processed.

2. Dashboard:

   • Create a Power BI dashboard to visualize the processed data.

3. Error Handling:

   • Implement retry mechanisms in Logic Apps or Azure Functions for failed operations.

4. Scalability:

   • Use Azure Event Grid to trigger processing workflows for large-scale systems.

Sample Logic App Workflow

1. Trigger: "When a blob is added or modified" (Blob Storage trigger).
2. Step 1: Call Form Recognizer to extract data.
3. Step 2: Parse the extracted JSON response.
4. Step 3: Insert parsed data into Azure SQL Database.