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

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

Connecting to Oracle Data

Connecting to Oracle 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.

To connect to Oracle, you'll first need to update your PATH variable and ensure it contains a folder location that includes the native DLLs. The native DLLs can be found in the lib folder inside the installation directory. Once you've done this, set the following to connect:

• Port: The port used to connect to the server hosting the Oracle database.
• User: The user Id provided for authentication with the Oracle database.
• Password: The password provided for authentication with the Oracle database.
• Service Name: The service name of the Oracle database.

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Port=1521;")

Create a SQL Statement to Query Oracle

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

sql = "SELECT CompanyName, City FROM Customers WHERE Country = 'US'"

Extract, Transform, and Load the Oracle Data

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

Loading Oracle Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Oracle

table1 = [ ['CompanyName','City'], ['NewCompanyName1','NewCity1'], ['NewCompanyName2','NewCity2'], ['NewCompanyName3','NewCity3'] ]

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Port=1521;")

sql = "SELECT CompanyName, City FROM Customers WHERE Country = 'US'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['CompanyName','City'], ['NewCompanyName1','NewCity1'], ['NewCompanyName2','NewCity2'], ['NewCompanyName3','NewCity3'] ]

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