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

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

Connecting to Veeva Data

Connecting to Veeva 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.

You are ready to connect after specifying the following connection properties:

• Url: The host you see in the URL after you login to your account. For example: https://my-veeva-domain.veevavault.com
• User: The username you use to login to your account.
• Password: The password you use to login to your account.

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

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

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

Create a SQL Statement to Query Veeva

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

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

Extract, Transform, and Load the Veeva Data

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

Loading Veeva 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 Veeva

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

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

With the CData Python Connector for Veeva, you can work with Veeva 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 Veeva to start building Python apps and scripts with connectivity to Veeva 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.veevavault 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')