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Extract, Transform, and Load Oracle Sales Cloud Data in Python

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

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

Connecting to Oracle Sales Cloud Data

Connecting to Oracle Sales Cloud 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.

OracleSalesCloud 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 Cloud Connector, follow the procedure below to install the other required modules and start accessing Oracle Sales Cloud 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 Cloud 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 Cloud Connector to create a connection for working with Oracle Sales Cloud data.

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

Create a SQL Statement to Query Oracle Sales Cloud

Use SQL to create a statement for querying Oracle Sales Cloud. 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 Cloud Data

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

Loading Oracle Sales Cloud 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 Cloud

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

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

With the CData Python Connector for Oracle Sales Cloud, you can work with Oracle Sales Cloud 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 Oracle Sales Cloud Python Connector to start building Python apps and scripts with connectivity to Oracle Sales Cloud 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')