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

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

Connecting to Zoho Books Data

Connecting to Zoho Books 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.

Zoho Books uses the OAuth authentication standard. To authenticate using OAuth, create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the OAuth section of the Getting Started guide in the Help documentation for an authentication guide.

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=myOAuthClientSecret;CallbackURL=https://localhost:33333;OrganizationId=MyOrganizationId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Zoho Books

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

sql = "SELECT InvoiceId, InvoiceNumber FROM INVOICES WHERE CustomerName = 'NewTech Industries'"

Extract, Transform, and Load the Zoho Books Data

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

Loading Zoho Books Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=myOAuthClientSecret;CallbackURL=https://localhost:33333;OrganizationId=MyOrganizationId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT InvoiceId, InvoiceNumber FROM INVOICES WHERE CustomerName = 'NewTech Industries'"

table1 = etl.fromdb(cnxn,sql)

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

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