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

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

Connecting to Oracle Sales Data

Connecting to Oracle Sales 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.

Oracle Sales uses Basic authentication over SSL; after setting the following connection properties, you are ready to connect:

• Username: Set this to the user name that you use to log into your Oracle Cloud service.
• Password: Set this to your password.
• HostURL: Set this to the Web address (URL) of your Oracle Cloud service.

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

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

cnxn = mod.connect("HostURL=https://my.host.oraclecloud.com; Username=abc123; Password=abcdef;")

Create a SQL Statement to Query Oracle Sales

Use SQL to create a statement for querying Oracle Sales. In this article, we read data from the Opportunities entity.

sql = "SELECT OptyId, Name FROM Opportunities WHERE CreatedBy = 'Jack'"

Extract, Transform, and Load the Oracle Sales Data

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

Loading Oracle Sales Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Oracle Sales

table1 = [ ['OptyId','Name'], ['NewOptyId1','NewName1'], ['NewOptyId2','NewName2'], ['NewOptyId3','NewName3'] ]

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

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

cnxn = mod.connect("HostURL=https://my.host.oraclecloud.com; Username=abc123; Password=abcdef;")

sql = "SELECT OptyId, Name FROM Opportunities WHERE CreatedBy = 'Jack'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['OptyId','Name'], ['NewOptyId1','NewName1'], ['NewOptyId2','NewName2'], ['NewOptyId3','NewName3'] ]

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