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

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

Connecting to Zoho CRM Data

Connecting to Zoho 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.

The connector is already registered with Zoho CRM as an OAuth application. As such, OAuth Credentials are embedded by default. If you would prefer to use your own custom OAuth app, see the Custom Credentials section in the Help documentation.

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Zoho CRM

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

sql = "SELECT Account_Name, Annual_Revenue FROM Accounts WHERE Industry = 'Data/Telecom OEM'"

Extract, Transform, and Load the Zoho CRM Data

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

Loading Zoho CRM Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Zoho CRM

table1 = [ ['Account_Name','Annual_Revenue'], ['NewAccount_Name1','NewAnnual_Revenue1'], ['NewAccount_Name2','NewAnnual_Revenue2'], ['NewAccount_Name3','NewAnnual_Revenue3'] ]

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Account_Name, Annual_Revenue FROM Accounts WHERE Industry = 'Data/Telecom OEM'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Account_Name','Annual_Revenue'], ['NewAccount_Name1','NewAnnual_Revenue1'], ['NewAccount_Name2','NewAnnual_Revenue2'], ['NewAccount_Name3','NewAnnual_Revenue3'] ]

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