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

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

Connecting to Oracle HCM Cloud Data

Connecting to Oracle HCM 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.

Using Basic Authentication

You must set the following to authenticate to Oracle HCM Cloud:

• Url: The Url of your account.
• User: The user of your account.
• Password: The password of your account.

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

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

cnxn = mod.connect("Url=https://abc.oraclecloud.com;User=user;Password=password;")

Create a SQL Statement to Query Oracle HCM Cloud

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

sql = "SELECT SiteId, SiteName FROM RecruitingCESites WHERE Language = 'English'"

Extract, Transform, and Load the Oracle HCM Cloud Data

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

Loading Oracle HCM Cloud Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("Url=https://abc.oraclecloud.com;User=user;Password=password;")

sql = "SELECT SiteId, SiteName FROM RecruitingCESites WHERE Language = 'English'"

table1 = etl.fromdb(cnxn,sql)

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

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