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

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

Connecting to Acumatica Data

Connecting to Acumatica 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.

Set the following connection properties to connect to Acumatica:

• User: Set this to your username.
• Password: Set this to your password.
• Company: Set this to your company.
• Url: Set this to your Acumatica URL, in the format http://{Acumatica ERP instance URL}/entity/{Endpoint name}/{Endpoint version}/.
  For example: https://acumatica.com/entity/Default/17.200.001/

See the Getting Started guide in the CData driver documentation for more information.

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

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

cnxn = mod.connect("Url = https://try.acumatica.com/ISV/entity/Default/17.200.001/;User=user;Password=password;Company=CompanyName;")

Create a SQL Statement to Query Acumatica

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

sql = "SELECT Id, location_displayname FROM Events WHERE Id = '1'"

Extract, Transform, and Load the Acumatica Data

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

Loading Acumatica Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Acumatica

table1 = [ ['Id','location_displayname'], ['NewId1','Newlocation_displayname1'], ['NewId2','Newlocation_displayname2'], ['NewId3','Newlocation_displayname3'] ]

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

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

cnxn = mod.connect("Url = https://try.acumatica.com/ISV/entity/Default/17.200.001/;User=user;Password=password;Company=CompanyName;")

sql = "SELECT Id, location_displayname FROM Events WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','location_displayname'], ['NewId1','Newlocation_displayname1'], ['NewId2','Newlocation_displayname2'], ['NewId3','Newlocation_displayname3'] ]

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