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Extract, Transform, and Load Sage US Data in Python

The CData Python Connector for Sage US enables you to create ETL applications and pipelines for Sage US 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 US and the petl framework, you can build Sage US-connected applications and pipelines for extracting, transforming, and loading Sage US data. This article shows how to connect to Sage US with the CData Python Connector and use petl and pandas to extract, transform, and load Sage US data.

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

Connecting to Sage US Data

Connecting to Sage US 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.

The Application Id and Company Name connection string options are required to connect to Sage as a data source. You can obtain an Application Id by contacting Sage directly to request access to the Sage 50 SDK.

Sage must be installed on the machine. The Sage.Peachtree.API.dll and Sage.Peachtree.API.Resolver.dll assemblies are required. These assemblies are installed with Sage in C:\Program Files\Sage\Peachtree\API\. Additionally, the Sage SDK requires .NET Framework 4.0 and is only compatible with 32-bit applications. To use the Sage SDK in Visual Studio, set the Platform Target property to "x86" in Project -> Properties -> Build.

You must authorize the application to access company data: To authorize your application to access Sage, restart the Sage application, open the company you want to access, and connect with your application. You will then be prompted to set access permissions for the application in the resulting dialog.

While the compiled executable will require authorization only once, during development you may need to follow this process to reauthorize a new build. To avoid restarting the Sage application when developing with Visual Studio, click Build -> Configuration Manager and uncheck "Build" for your project.

After installing the CData Sage US Connector, follow the procedure below to install the other required modules and start accessing Sage US 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 US 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.sage50us as mod

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

cnxn = mod.connect("ApplicationId=8dfafu4V4ODmh1fM0xx;CompanyName=Bellwether Garden Supply - Premium;")

Create a SQL Statement to Query Sage US

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

sql = "SELECT Name, LastInvoiceAmount FROM Customer WHERE Name = 'ALDRED'"

Extract, Transform, and Load the Sage US Data

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

Loading Sage US Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Sage US

table1 = [ ['Name','LastInvoiceAmount'], ['NewName1','NewLastInvoiceAmount1'], ['NewName2','NewLastInvoiceAmount2'], ['NewName3','NewLastInvoiceAmount3'] ]

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

With the CData Python Connector for Sage US, you can work with Sage US 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 US Python Connector to start building Python apps and scripts with connectivity to Sage US 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.sage50us as mod

cnxn = mod.connect("ApplicationId=8dfafu4V4ODmh1fM0xx;CompanyName=Bellwether Garden Supply - Premium;")

sql = "SELECT Name, LastInvoiceAmount FROM Customer WHERE Name = 'ALDRED'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','LastInvoiceAmount'], ['NewName1','NewLastInvoiceAmount1'], ['NewName2','NewLastInvoiceAmount2'], ['NewName3','NewLastInvoiceAmount3'] ]

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