Extract, Transform, and Load 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 ETL applications and pipelines for Sage 50 UK data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Sage 50 UK and the petl framework, you can build Sage 50 UK-connected applications and pipelines for extracting, transforming, and loading Sage 50 UK data. This article shows how to connect to Sage 50 UK with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData Sage 50 UK Connector, follow the procedure below to install the other required modules and start accessing Sage 50 UK through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Sage 50 UK Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.sage50uk as mod

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

cnxn = mod.connect("URL=http://your-server:5493/sdata/accounts50/GCRM/your-address;User=Manager;")

Create a SQL Statement to Query Sage 50 UK

Use SQL to create a statement for querying Sage 50 UK. In this article, we read data from the TradingAccounts entity.

sql = "SELECT Name, FinanceBalance FROM TradingAccounts WHERE TradingAccountUUID = 'c2ef66a5-a545-413b-9312-79a53caadbc4'"

Extract, Transform, and Load the Sage 50 UK Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Sage 50 UK data. In this example, we extract Sage 50 UK data, sort the data by the FinanceBalance column, and load the data into a CSV file.

Loading Sage 50 UK Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the TradingAccounts table.

Adding New Rows to Sage 50 UK

table1 = [ ['Name','FinanceBalance'], ['NewName1','NewFinanceBalance1'], ['NewName2','NewFinanceBalance2'], ['NewName3','NewFinanceBalance3'] ]

etl.appenddb(table1, cnxn, 'TradingAccounts')

With the CData Python Connector for Sage 50 UK, you can work with Sage 50 UK data just like you would with any database, including direct access to data in ETL packages like petl.

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.



Full Source Code


import petl as etl
import pandas as pd
import cdata.sage50uk as mod

cnxn = mod.connect("URL=http://your-server:5493/sdata/accounts50/GCRM/your-address;User=Manager;")

sql = "SELECT Name, FinanceBalance FROM TradingAccounts WHERE TradingAccountUUID = 'c2ef66a5-a545-413b-9312-79a53caadbc4'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','FinanceBalance'], ['NewName1','NewFinanceBalance1'], ['NewName2','NewFinanceBalance2'], ['NewName3','NewFinanceBalance3'] ]

etl.appenddb(table3, cnxn, 'TradingAccounts')