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

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

Connecting to Pushover Data

Connecting to Pushover 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 Pushover Profile on disk (e.g. C:\profiles\Pushover.apip). Next, set the ProfileSettings connection property to the connection string for Pushover (see below).

Pushover API Profile Settings

Pushover uses API Key authentication. Your Pushover Application API Token is used to authenticate all requests. You can create and manage API tokens in the Pushover dashboard at pushover.net by registering an application under Settings > Your Applications.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your 30-character Pushover Application API Token.

After installing the CData Pushover Connector, follow the procedure below to install the other required modules and start accessing Pushover 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 Pushover 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 Pushover Connector to create a connection for working with Pushover data.

cnxn = mod.connect("Profile=C:\profiles\Pushover.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_application_token';")

Create a SQL Statement to Query Pushover

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

sql = "SELECT User, Memo FROM GroupMembers WHERE GroupKey = 'your_group_key'"

Extract, Transform, and Load the Pushover Data

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

Loading Pushover Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Pushover 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 Pushover 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\Pushover.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_application_token';")

sql = "SELECT User, Memo FROM GroupMembers WHERE GroupKey = 'your_group_key'"

table1 = etl.fromdb(cnxn,sql)

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

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