Use SQLAlchemy ORMs to Access Sage 50 UK Data in Python

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Sage 50 UK Python Connector

Python Connector Libraries for Sage 50 UK Data Connectivity. Integrate Sage 50 UK with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Sage 50 UK enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Sage 50 UK data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Sage 50 UK and the SQLAlchemy toolkit, you can build Sage 50 UK-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Sage 50 UK data to query, update, delete, and insert Sage 50 UK data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Sage 50 UK data in Python. When you issue complex SQL queries from Sage 50 UK, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Sage 50 UK and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage 50 UK Data

Connecting to Sage 50 UK data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Note: Only Sage 50 UK 2012 and above are supported.

The User and Password properties, under the Connection section, must be set to valid Sage 50 UK user credentials. These values will be the same used to log in to the Sage 50 UK software.

Additionally, the URL property, under the Connection section, will need to be set to the address of the company dataset desired. To obtain the address, do the following:

  1. If you have not already done so, open the Sage 50 UK software.
  2. Click Tools -> Internet Options.
  3. Select the SData Settings tab.
  4. Click the Details button next to Sage 50 Accounts. A window is displayed containing a list of company names along with the address to their corresponding datasets.
  5. Set the URL property to the value in the address field next to the company desired.

Follow the procedure below to install SQLAlchemy and start accessing Sage 50 UK through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Sage 50 UK Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Sage 50 UK data.

engine = create_engine("sage50uk:///?URL=http://your-server:5493/sdata/accounts50/GCRM/your-address&User=Manager")

Declare a Mapping Class for Sage 50 UK Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the TradingAccounts table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class TradingAccounts(base):
	__tablename__ = "TradingAccounts"
	Name = Column(String,primary_key=True)
	FinanceBalance = Column(String)
	...

Query Sage 50 UK Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("sage50uk:///?URL=http://your-server:5493/sdata/accounts50/GCRM/your-address&User=Manager")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(TradingAccounts).filter_by(TradingAccountUUID="c2ef66a5-a545-413b-9312-79a53caadbc4"):
	print("Name: ", instance.Name)
	print("FinanceBalance: ", instance.FinanceBalance)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

TradingAccounts_table = TradingAccounts.metadata.tables["TradingAccounts"]
for instance in session.execute(TradingAccounts_table.select().where(TradingAccounts_table.c.TradingAccountUUID == "c2ef66a5-a545-413b-9312-79a53caadbc4")):
	print("Name: ", instance.Name)
	print("FinanceBalance: ", instance.FinanceBalance)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Sage 50 UK Data

To insert Sage 50 UK data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Sage 50 UK.

new_rec = TradingAccounts(Name="placeholder", TradingAccountUUID="c2ef66a5-a545-413b-9312-79a53caadbc4")
session.add(new_rec)
session.commit()

Update Sage 50 UK Data

To update Sage 50 UK data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Sage 50 UK.

updated_rec = session.query(TradingAccounts).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.TradingAccountUUID = "c2ef66a5-a545-413b-9312-79a53caadbc4"
session.commit()

Delete Sage 50 UK Data

To delete Sage 50 UK data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(TradingAccounts).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

Download a free, 30-day trial of the Sage 50 UK Python Connector to start building Python apps and scripts with connectivity to Sage 50 UK data. Reach out to our Support Team if you have any questions.