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

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

Connecting to Oracle SCM Data

Connecting to Oracle SCM 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.

The following connection properties are required to connect to Oracle SCM data.

• Url: The URL of the account that you want to connect to. Typically, this will be the URL of your Oracle Cloud service. For example, https://servername.fa.us2.oraclecloud.com.
• User: The username of your Oracle Cloud service account.
• Password: The password of your Oracle Cloud service account.

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

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

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

Create a SQL Statement to Query Oracle SCM

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

sql = "SELECT CarrierId, CarrierName FROM Carriers WHERE ActiveFlag = 'false'"

Extract, Transform, and Load the Oracle SCM Data

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

Loading Oracle SCM Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Oracle SCM, you can work with Oracle SCM 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 SCM to start building Python apps and scripts with connectivity to Oracle SCM data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.oraclescm as mod

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

sql = "SELECT CarrierId, CarrierName FROM Carriers WHERE ActiveFlag = 'false'"

table1 = etl.fromdb(cnxn,sql)

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

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