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

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

Connecting to Exact Online Data

Connecting to Exact Online 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.

Exact Online uses the OAuth authentication standard. You can use the embedded OAuth credentials or you can register an OAuth app with Exact to obtain your own. In addition to the OAuth values, provide the Region. If Division is not set, the default Division is determined.

See the "Getting Started" chapter of the help documentation for more information.

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

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

cnxn = mod.connect("Region='United States';Division=5512;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Exact Online

Use SQL to create a statement for querying Exact Online. In this article, we read data from the Accounts entity.

sql = "SELECT Name, CreditLinePurchase FROM Accounts WHERE IsCompetitor = 'False'"

Extract, Transform, and Load the Exact Online Data

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

Loading Exact Online Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Exact Online

table1 = [ ['Name','CreditLinePurchase'], ['NewName1','NewCreditLinePurchase1'], ['NewName2','NewCreditLinePurchase2'], ['NewName3','NewCreditLinePurchase3'] ]

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

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

cnxn = mod.connect("Region='United States';Division=5512;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Name, CreditLinePurchase FROM Accounts WHERE IsCompetitor = 'False'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','CreditLinePurchase'], ['NewName1','NewCreditLinePurchase1'], ['NewName2','NewCreditLinePurchase2'], ['NewName3','NewCreditLinePurchase3'] ]

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