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

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

Connecting to Vault CRM Data

Connecting to Vault CRM 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 Vault CRM Connector, follow the procedure below to install the other required modules and start accessing Vault CRM 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 Vault CRM 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.vaultcrm as mod

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

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

Create a SQL Statement to Query Vault CRM

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

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

Extract, Transform, and Load the Vault CRM Data

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

Loading Vault CRM 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 Vault CRM

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

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

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