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

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

Connecting to IBM Informix Data

Connecting to IBM Informix 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.

Set the following properties to connect to IBM Informix

• Server: Set this to the name of the server running IBM Informix.
• Port: Set this to the port the IBM Informix server is listening on.
• Database: Set this to the name of the IBM Informix database.
• User: Set this to the username of a user allowed to access the database.
• Password: Set this to the password of a user allowed to access the database.

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

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

cnxn = mod.connect("Server=10.0.1.2;Port=50000;User=admin;Password=admin;Database=test;")

Create a SQL Statement to Query IBM Informix

Use SQL to create a statement for querying IBM Informix. In this article, we read data from the Books entity.

sql = "SELECT Id, Price FROM Books WHERE Category = 'US'"

Extract, Transform, and Load the IBM Informix Data

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

Loading IBM Informix Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to IBM Informix

table1 = [ ['Id','Price'], ['NewId1','NewPrice1'], ['NewId2','NewPrice2'], ['NewId3','NewPrice3'] ]

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

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

cnxn = mod.connect("Server=10.0.1.2;Port=50000;User=admin;Password=admin;Database=test;")

sql = "SELECT Id, Price FROM Books WHERE Category = 'US'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Price'], ['NewId1','NewPrice1'], ['NewId2','NewPrice2'], ['NewId3','NewPrice3'] ]

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