How to Build an ETL App for Sage 200 Data in Python with CData

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 200 and the petl framework, you can build Sage 200-connected applications and pipelines for extracting, transforming, and loading Sage 200 data. This article shows how to connect to Sage 200 with the CData Python Connector and use petl and pandas to extract, transform, and load Sage 200 data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Sage 200 data in Python. When you issue complex SQL queries from Sage 200, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 200 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage 200 Data

Connecting to Sage 200 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.

• Schema: Determines which Sage 200 edition you are connecting to. Specify either StandardUK or ProfessionalUK.
• Subscription Key: Provides access to the APIs that are used to establish a connection. You will first need to log into the Sage 200 API website and subscribe to the API edition that matches your account. You can do so here: https://developer.columbus.sage.com/docs/services/api/uk. Afterwards, the subscription key may be found in your profile after logging into Sage 200.

After installing the CData Sage 200 Connector, follow the procedure below to install the other required modules and start accessing Sage 200 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 200 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.sage200 as mod

You can now connect with a connection string. Use the connect function for the CData Sage 200 Connector to create a connection for working with Sage 200 data.

cnxn = mod.connect("SubscriptionKey=12345;Schema=StandardUK;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Sage 200

Use SQL to create a statement for querying Sage 200. In this article, we read data from the Banks entity.

sql = "SELECT Id, Code FROM Banks WHERE Code = '12345'"

Extract, Transform, and Load the Sage 200 Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Sage 200 data. In this example, we extract Sage 200 data, sort the data by the Code column, and load the data into a CSV file.

Loading Sage 200 Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Sage 200, you can work with Sage 200 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 CData Python Connector for Sage 200 to start building Python apps and scripts with connectivity to Sage 200 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.sage200 as mod

cnxn = mod.connect("SubscriptionKey=12345;Schema=StandardUK;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Code FROM Banks WHERE Code = '12345'"

table1 = etl.fromdb(cnxn,sql)

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

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