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Use SQLAlchemy for Database Models — Beginner's Guide with Tally Prime

Use SQLAlchemy for Database Models — Beginner's Guide with Tally Prime. Get practical lessons and hands-on examples at AIComputerClasses in Indore to master programming & IT development skills quickly. This article from AIComputerClasses Indore breaks down Use SQLAlchemy for Database Models — Beginner's Guide with Tally Prime into actionable steps. Includes references to tools like ChatGPT, Power BI, Excel, Figma, or Python where appropriate. Follow practical exercises and tool-based examples to learn rapidly.

🧠 Use SQLAlchemy for Database Models — Beginner’s Guide with Tally Prime

If you’re a Python developer or a data enthusiast looking to integrate databases seamlessly into your applications, SQLAlchemy is one of the most powerful tools you can learn.

In this beginner-friendly guide, we’ll explore how SQLAlchemy works, how to define database models, and even how to connect SQLAlchemy with Tally Prime — a popular accounting and business management tool.

At AI Computer Classes – Indore, students get hands-on practice with Python, SQLAlchemy, and Tally integration to build real-world business solutions.

⚙️ What Is SQLAlchemy?

SQLAlchemy is a Python ORM (Object Relational Mapper) that allows developers to interact with databases using Python objects instead of raw SQL queries.

This means you can define tables, columns, and relationships using Python classes — making database management cleaner, safer, and easier to maintain.

🔍 Why Use SQLAlchemy?

Avoid writing long SQL queries
Make your code database-agnostic (works with MySQL, SQLite, PostgreSQL, etc.)
Easy to connect, create, update, and query data
Built-in support for relationships and joins
Integrates seamlessly with tools like Flask, Django, and even Tally Prime APIs

💡 Learn Practically at AI Computer Classes – Indore!

Master Python, SQLAlchemy, and database modeling through live projects.

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🧩 Step 1: Installing SQLAlchemy

Open your terminal and install SQLAlchemy:

pip install sqlalchemy

For connecting with SQLite (used for small projects):

pip install sqlite3

🧱 Step 2: Create a Database Model

Let’s create a simple Customer model that can later sync with Tally Prime.

from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import declarative_base, sessionmaker

Base = declarative_base()

class Customer(Base):
    __tablename__ = 'customers'
    id = Column(Integer, primary_key=True)
    name = Column(String)
    email = Column(String)
    phone = Column(String)

# Create database
engine = create_engine('sqlite:///business.db')
Base.metadata.create_all(engine)

Here’s what’s happening:

Base → defines a foundation for your database models
Customer → represents a table in your database
Column → defines table columns and data types
create_engine() → connects to your chosen database (SQLite in this case)

💾 Step 3: Insert Data Using Python

After defining your model, use sessions to add data.

Session = sessionmaker(bind=engine)
session = Session()

new_customer = Customer(name="Ravi Sharma", email="ravi@example.com", phone="9876543210")
session.add(new_customer)
session.commit()

✅ The above code adds a new customer entry in your SQLite database.

🧮 Step 4: Connect SQLAlchemy with Tally Prime

Now let’s integrate Tally Prime, so your accounting data syncs with your SQLAlchemy models.

Tally Prime provides an XML API that allows Python scripts to communicate with it.

Example snippet:

import requests

xml_request = """  
<ENVELOPE>  
<HEADER>  
<VERSION>1</VERSION>  
<TALLYREQUEST>Export Data</TALLYREQUEST>  
<TYPE>Collection</TYPE>  
<ID>Customer List</ID>  
</HEADER>  
<BODY>  
<DESC>  
<STATICVARIABLES>  
<SVEXPORTFORMAT>$$SysName:XML</SVEXPORTFORMAT>  
</STATICVARIABLES>  
<TDL>  
<TDLMessage>  
<Collection Name="Customer List" Type="Customer">  
<Fetch>NAME,EMAIL,PHONE</Fetch>  
</Collection>  
</TDLMessage>  
</TDL>  
</DESC>  
</BODY>  
</ENVELOPE>  
"""

response = requests.post("http://localhost:9000", data=xml_request)
print(response.text)

This fetches customer data from Tally and prints it as XML, which you can then parse and insert into your SQLAlchemy models.

🧠 Step 5: Query and Filter Data

You can easily query and filter your data using Python syntax:

customers = session.query(Customer).filter_by(name="Ravi Sharma").all()
for customer in customers:
    print(customer.email)

This replaces the need to write SQL queries like:

SELECT * FROM customers WHERE name = 'Ravi Sharma';

💡 Boost Your Python + SQLAlchemy Skills Practically!

At AI Computer Classes – Indore, you’ll learn how to:

✅ Build ORM-based databases

✅ Connect SQLAlchemy with APIs like Tally Prime

✅ Visualize data using Power BI & Excel

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🔗 Step 6: Visualize Tally Data with Power BI

Once you’ve stored accounting data from Tally in your SQLAlchemy database, you can:

Export it to CSV or Excel
Connect that file to Power BI
Create dashboards showing total sales, customer details, and outstanding payments

Example:

import pandas as pd

customers = session.query(Customer).all()
data = [{"Name": c.name, "Email": c.email, "Phone": c.phone} for c in customers]
df = pd.DataFrame(data)
df.to_csv("customers.csv", index=False)

This makes your SQLAlchemy database a bridge between Tally and analytics tools like Power BI or Excel.

🧭 Conclusion

By now, you’ve learned how to:

Set up SQLAlchemy models
Insert and query data
Connect Python with Tally Prime
Export and analyze your data

At AI Computer Classes – Indore, we teach students to integrate business tools like Tally, Power BI, and Python for real-world automation projects.

Learn the complete workflow — from data fetching to visualization — and become job-ready in Programming & IT Development.

📞 Contact AI Computer Classes – Indore

✉ Email: hello@aicomputerclasses.com

📱 Phone: +91 91113 33255

📍 Address: 208, Captain CS Naidu Building, near Greater Kailash Road, opposite School of Excellence For Eye, Opposite Grotto Arcade, Old Palasia, Indore, Madhya Pradesh 452018

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