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

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

Connecting to Marketo Data

Connecting to Marketo 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.

Both the REST and SOAP APIs are supported and can be chosen by using the Schema property.

For the REST API: The OAuthClientId, OAuthClientSecret, and RESTEndpoint properties, under the OAuth and REST Connection sections, must be set to valid Marketo user credentials.

For the SOAP API: The UserId, EncryptionKey, and SOAPEndpoint properties, under the SOAP Connection section, must be set to valid Marketo user credentials.

See the "Getting Started" chapter of the help documentation for a guide to obtaining these values.

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

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

cnxn = mod.connect("Schema=REST;RESTEndpoint=https://311-IFS-929.mktorest.com/rest;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")

Create a SQL Statement to Query Marketo

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

sql = "SELECT Email, AnnualRevenue FROM Leads WHERE Country = 'U.S.A.'"

Extract, Transform, and Load the Marketo Data

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

Loading Marketo Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Marketo

table1 = [ ['Email','AnnualRevenue'], ['NewEmail1','NewAnnualRevenue1'], ['NewEmail2','NewAnnualRevenue2'], ['NewEmail3','NewAnnualRevenue3'] ]

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

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

cnxn = mod.connect("Schema=REST;RESTEndpoint=https://311-IFS-929.mktorest.com/rest;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")

sql = "SELECT Email, AnnualRevenue FROM Leads WHERE Country = 'U.S.A.'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Email','AnnualRevenue'], ['NewEmail1','NewAnnualRevenue1'], ['NewEmail2','NewAnnualRevenue2'], ['NewEmail3','NewAnnualRevenue3'] ]

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