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

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

Connecting to NetSuite Data

Connecting to NetSuite 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 User and Password properties, under the Authentication section, must be set to valid NetSuite user credentials. In addition, the AccountId must be set to the ID of a company account that can be used by the specified User. The RoleId can be optionally specified to log in the user with limited permissions.

See the "Getting Started" chapter of the help documentation for more information on connecting to NetSuite.

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

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

cnxn = mod.connect("Account Id=XABC123456;Password=password;User=user;Role Id=3;Version=2013_1;")

Create a SQL Statement to Query NetSuite

Use SQL to create a statement for querying NetSuite. In this article, we read data from the SalesOrder entity.

sql = "SELECT CustomerName, SalesOrderTotal FROM SalesOrder WHERE Class_Name = 'Furniture : Office'"

Extract, Transform, and Load the NetSuite Data

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

Loading NetSuite Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to NetSuite

table1 = [ ['CustomerName','SalesOrderTotal'], ['NewCustomerName1','NewSalesOrderTotal1'], ['NewCustomerName2','NewSalesOrderTotal2'], ['NewCustomerName3','NewSalesOrderTotal3'] ]

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

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

cnxn = mod.connect("Account Id=XABC123456;Password=password;User=user;Role Id=3;Version=2013_1;")

sql = "SELECT CustomerName, SalesOrderTotal FROM SalesOrder WHERE Class_Name = 'Furniture : Office'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['CustomerName','SalesOrderTotal'], ['NewCustomerName1','NewSalesOrderTotal1'], ['NewCustomerName2','NewSalesOrderTotal2'], ['NewCustomerName3','NewSalesOrderTotal3'] ]

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