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Extract, Transform, and Load DB2 Data in Python

The CData Python Connector for DB2 enables you to create ETL applications and pipelines for DB2 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 DB2 and the petl framework, you can build DB2-connected applications and pipelines for extracting, transforming, and loading DB2 data. This article shows how to connect to DB2 with the CData Python Connector and use petl and pandas to extract, transform, and load DB2 data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live DB2 data in Python. When you issue complex SQL queries from DB2, the driver pushes supported SQL operations, like filters and aggregations, directly to DB2 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to DB2 Data

Connecting to DB2 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.

After installing the CData DB2 Connector, follow the procedure below to install the other required modules and start accessing DB2 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 DB2 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.db2 as mod

You can now connect with a connection string. Use the connect function for the CData DB2 Connector to create a connection for working with DB2 data.

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

Create a SQL Statement to Query DB2

Use SQL to create a statement for querying DB2. In this article, we read data from the Orders entity.

sql = "SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'"

Extract, Transform, and Load the DB2 Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the DB2 data. In this example, we extract DB2 data, sort the data by the Freight column, and load the data into a CSV file.

Loading DB2 Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to DB2

table1 = [ ['OrderName','Freight'], ['NewOrderName1','NewFreight1'], ['NewOrderName2','NewFreight2'], ['NewOrderName3','NewFreight3'] ]

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

With the CData Python Connector for DB2, you can work with DB2 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 DB2 Python Connector to start building Python apps and scripts with connectivity to DB2 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.db2 as mod

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

sql = "SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['OrderName','Freight'], ['NewOrderName1','NewFreight1'], ['NewOrderName2','NewFreight2'], ['NewOrderName3','NewFreight3'] ]

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