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

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

Connecting to Outreach.io Data

Connecting to Outreach.io 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.

You must use OAuth to authenticate with Outreach. Set the InitiateOAuth connection property to "GETANDREFRESH". For more information, refer to the OAuth section in the Help documentation.

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

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

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

Create a SQL Statement to Query Outreach.io

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

sql = "SELECT Name, NumberOfEmployees FROM Accounts WHERE Industry = 'Textiles'"

Extract, Transform, and Load the Outreach.io Data

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

Loading Outreach.io Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Outreach.io

table1 = [ ['Name','NumberOfEmployees'], ['NewName1','NewNumberOfEmployees1'], ['NewName2','NewNumberOfEmployees2'], ['NewName3','NewNumberOfEmployees3'] ]

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

With the CData Python Connector for Outreach.io, you can work with Outreach.io 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 Outreach.io to start building Python apps and scripts with connectivity to Outreach.io data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.outreach as mod

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

sql = "SELECT Name, NumberOfEmployees FROM Accounts WHERE Industry = 'Textiles'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','NumberOfEmployees'], ['NewName1','NewNumberOfEmployees1'], ['NewName2','NewNumberOfEmployees2'], ['NewName3','NewNumberOfEmployees3'] ]

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