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

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

Connecting to Retently Data

Connecting to Retently 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.

Start by setting the Profile connection property to the location of the Retently Profile on disk (e.g. C:\profiles\Retently.apip). Next, set the ProfileSettings connection property to the connection string for Retently (see below).

Retently API Profile Settings

Log into your Retently account and navigate to the OAuth settings page to register an application and receive a Client ID and Client Secret.

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

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

cnxn = mod.connect("Profile=C:\profiles\Retently.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Retently

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

sql = "SELECT Id, Value FROM Campaigns WHERE Id = 'campaign_123'"

Extract, Transform, and Load the Retently Data

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

Loading Retently Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Retently 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 API Driver for Python to start building Python apps and scripts with connectivity to Retently 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Retently.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT Id, Value FROM Campaigns WHERE Id = 'campaign_123'"

table1 = etl.fromdb(cnxn,sql)

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

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