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Extract, Transform, and Load Sage 300 Data in Python

The CData Python Connector for Sage 300 enables you to create ETL applications and pipelines for Sage 300 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 300 and the petl framework, you can build Sage 300-connected applications and pipelines for extracting, transforming, and loading Sage 300 data. This article shows how to connect to Sage 300 with the CData Python Connector and use petl and pandas to extract, transform, and load Sage 300 data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Sage 300 data in Python. When you issue complex SQL queries from Sage 300, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 300 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage 300 Data

Connecting to Sage 300 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.

Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

  • Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
  • Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
  • Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

Authenticate to Sage 300 using Basic authentication.

Connect Using Basic Authentication

You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

  • Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
  • User: Set this to the username of your account.
  • Password: Set this to the password of your account.

After installing the CData Sage 300 Connector, follow the procedure below to install the other required modules and start accessing Sage 300 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 300 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.sage300 as mod

You can now connect with a connection string. Use the connect function for the CData Sage 300 Connector to create a connection for working with Sage 300 data.

cnxn = mod.connect("User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;")

Create a SQL Statement to Query Sage 300

Use SQL to create a statement for querying Sage 300. In this article, we read data from the OEInvoices entity.

sql = "SELECT InvoiceUniquifier, ApprovedLimit FROM OEInvoices WHERE AllowPartialShipments = 'Yes'"

Extract, Transform, and Load the Sage 300 Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Sage 300 data. In this example, we extract Sage 300 data, sort the data by the ApprovedLimit column, and load the data into a CSV file.

Loading Sage 300 Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Sage 300, you can work with Sage 300 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 300 Python Connector to start building Python apps and scripts with connectivity to Sage 300 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.sage300 as mod

cnxn = mod.connect("User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;")

sql = "SELECT InvoiceUniquifier, ApprovedLimit FROM OEInvoices WHERE AllowPartialShipments = 'Yes'"

table1 = etl.fromdb(cnxn,sql)

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

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