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

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

Connecting to SuiteCRM Data

Connecting to SuiteCRM 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 User and Password properties must be set to valid SuiteCRM user credentials. Additionally, specify the URL to the SuiteCRM application, for example http://suite.crm.com.

Note that retrieving SuiteCRM metadata can be expensive. It is advised that you store the metadata locally as described in the Caching Metadata section of the data provider help documentation.

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

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

cnxn = mod.connect("URL=http://mySuiteCRM.com;User=myUser;Password=myPassword;")

Create a SQL Statement to Query SuiteCRM

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

sql = "SELECT Name, Industry FROM Accounts WHERE Industry = 'Manufacturing'"

Extract, Transform, and Load the SuiteCRM Data

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

Loading SuiteCRM Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to SuiteCRM

table1 = [ ['Name','Industry'], ['NewName1','NewIndustry1'], ['NewName2','NewIndustry2'], ['NewName3','NewIndustry3'] ]

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

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

cnxn = mod.connect("URL=http://mySuiteCRM.com;User=myUser;Password=myPassword;")

sql = "SELECT Name, Industry FROM Accounts WHERE Industry = 'Manufacturing'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Industry'], ['NewName1','NewIndustry1'], ['NewName2','NewIndustry2'], ['NewName3','NewIndustry3'] ]

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