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

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

Connecting to Zoho Creator Data

Connecting to Zoho Creator 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 Creator as an OAuth application.

If you would prefer to use your own custom OAuth app, see the Help documentation.

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

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

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

Create a SQL Statement to Query Zoho Creator

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

sql = "SELECT ID, Leave_Type FROM Leave_Types WHERE Leave_Type = 'Sick'"

Extract, Transform, and Load the Zoho Creator Data

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

Loading Zoho Creator Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Zoho Creator

table1 = [ ['ID','Leave_Type'], ['NewID1','NewLeave_Type1'], ['NewID2','NewLeave_Type2'], ['NewID3','NewLeave_Type3'] ]

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

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

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

sql = "SELECT ID, Leave_Type FROM Leave_Types WHERE Leave_Type = 'Sick'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ID','Leave_Type'], ['NewID1','NewLeave_Type1'], ['NewID2','NewLeave_Type2'], ['NewID3','NewLeave_Type3'] ]

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