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

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

Connecting to Sage Intacct Data

Connecting to Sage Intacct 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.

To connect using the Login method, the following connection properties are required: User, Password, CompanyId, SenderId and SenderPassword.

User, Password, and CompanyId are the credentials for the account you wish to connect to.

SenderId and SenderPassword are the Web Services credentials assigned to you by Sage Intacct.

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

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

cnxn = mod.connect("User=myusername;CompanyId=TestCompany;Password=mypassword;SenderId=Test;SenderPassword=abcde123;")

Create a SQL Statement to Query Sage Intacct

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

sql = "SELECT Name, TotalDue FROM Customer WHERE CustomerId = '12345'"

Extract, Transform, and Load the Sage Intacct Data

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

Loading Sage Intacct Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Sage Intacct

table1 = [ ['Name','TotalDue'], ['NewName1','NewTotalDue1'], ['NewName2','NewTotalDue2'], ['NewName3','NewTotalDue3'] ]

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

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

cnxn = mod.connect("User=myusername;CompanyId=TestCompany;Password=mypassword;SenderId=Test;SenderPassword=abcde123;")

sql = "SELECT Name, TotalDue FROM Customer WHERE CustomerId = '12345'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','TotalDue'], ['NewName1','NewTotalDue1'], ['NewName2','NewTotalDue2'], ['NewName3','NewTotalDue3'] ]

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