Extract, Transform, and Load Teradata Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Teradata and the petl framework, you can build Teradata-connected applications and pipelines for extracting, transforming, and loading Teradata data. This article shows how to connect to Teradata with the CData Python Connector and use petl and pandas to extract, transform, and load Teradata data.

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

Connecting to Teradata Data

Connecting to Teradata 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 Teradata, provide authentication information and specify the database server name.

• User: Set this to the username of a Teradata user.
• Password: Set this to the password of the Teradata user.
• DataSource: Specify the Teradata server name, DBC Name, or TDPID.
• Port: Specify the port the server is running on.
• Database: Specify the database name. If not specified, the default database is used.

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;")

Create a SQL Statement to Query Teradata

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

sql = "SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = '5'"

Extract, Transform, and Load the Teradata Data

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

Loading Teradata Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Teradata

table1 = [ ['ProductId','ProductName'], ['NewProductId1','NewProductName1'], ['NewProductId2','NewProductName2'], ['NewProductId3','NewProductName3'] ]

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;")

sql = "SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = '5'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ProductId','ProductName'], ['NewProductId1','NewProductName1'], ['NewProductId2','NewProductName2'], ['NewProductId3','NewProductName3'] ]

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