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

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

Connecting to Zoho Projects Data

Connecting to Zoho Projects 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 uses OAuth to authenticate with Zoho Projects. CData Software has already registered an OAuth application with Zoho Project which is embedded and used to authenticate.

If you use the embedded credentials, set the InitiateOAuth connection property to "GETANDREFRESH".

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

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Zoho Projects

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

sql = "SELECT Id, Name FROM Portals WHERE CrmPartner = 'true'"

Extract, Transform, and Load the Zoho Projects Data

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

Loading Zoho Projects Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Zoho Projects

table1 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Id, Name FROM Portals WHERE CrmPartner = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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