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Extract, Transform, and Load Sugar CRM Data in Python

The CData Python Connector for Sugar CRM enables you to create ETL applications and pipelines for Sugar CRM data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Sugar CRM and the petl framework, you can build Sugar CRM-connected applications and pipelines for extracting, transforming, and loading Sugar CRM data. This article shows how to connect to Sugar CRM with the CData Python Connector and use petl and pandas to extract, transform, and load Sugar CRM data.

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

Connecting to Sugar CRM Data

Connecting to Sugar 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 User and Password properties, under the Authentication section, must be set to valid SugarCRM user credentials. This will use the default OAuth token created to allow client logins. OAuthClientId and OAuthClientSecret are required if you do not wish to use the default OAuth token.

You can generate a new OAuth consumer key and consumer secret in Admin -> OAuth Keys. Set the OAuthClientId to the OAuth consumer key. Set the OAuthClientSecret to the consumer secret.

Additionally, specify the URL to the SugarCRM account.

Note that retrieving SugarCRM metadata can be expensive. It is advised that you store the metadata locally as described in the "Caching Metadata" chapter of the help documentation.

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

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

cnxn = mod.connect("User=MyUser;Password=MyPassword;URL=MySugarCRMAccountURL;CacheMetadata=True;")

Create a SQL Statement to Query Sugar CRM

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

sql = "SELECT Name, AnnualRevenue FROM Accounts WHERE Name = 'Bob'"

Extract, Transform, and Load the Sugar CRM Data

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

Loading Sugar CRM Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Sugar CRM

table1 = [ ['Name','AnnualRevenue'], ['NewName1','NewAnnualRevenue1'], ['NewName2','NewAnnualRevenue2'], ['NewName3','NewAnnualRevenue3'] ]

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

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

cnxn = mod.connect("User=MyUser;Password=MyPassword;URL=MySugarCRMAccountURL;CacheMetadata=True;")

sql = "SELECT Name, AnnualRevenue FROM Accounts WHERE Name = 'Bob'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','AnnualRevenue'], ['NewName1','NewAnnualRevenue1'], ['NewName2','NewAnnualRevenue2'], ['NewName3','NewAnnualRevenue3'] ]

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