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Get the Report →How to Build an ETL App for Sage Intacct Data in Python with CData
Create ETL applications and real-time data pipelines for Sage Intacct 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 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).
About Sage Intacct Data Integration
CData provides the easiest way to access and integrate live data from Sage Intact. Customers use CData connectivity to:
- Access Sage Intacct without worrying about API updates or changes.
- Access custom objects and fields in HubSpot with no extra configuration steps involved.
- Write data back to Sage Intacct using embedded Web Services credentials with Basic authentication.
- Use SQL stored procedures to perform functional operations like approving or declining vendors, inserting engagements, and creating or deleting custom objects or fields.
Users frequently integrate Sage Intact with analytics tools such as Tableau, Power BI, and Excel, and leverage our tools to replicate Workday data to databases or data warehouses.
To learn about how other customers are using CData's Sage Intacct solutions, check out our blog: Drivers in Focus: Accounting Connectivity.
Getting Started
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')