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

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

Connecting to Pushbullet Data

Connecting to Pushbullet 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.

Using API Key Authentication

Pushbullet uses token-based authentication (Access Token). To obtain an Access Token:

1. Log in to your Pushbullet account at https://www.pushbullet.com
2. Navigate to Settings > Account
3. Click "Create Access Token"
4. Copy the generated token

After obtaining your Access Token, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Pushbullet Access Token.

Example Connection String

Profile=C:\profiles\Pushbullet.apip;ProfileSettings='APIKey=your_access_token;';AuthScheme=APIKey;

Connecting to Pushbullet

Once the authentication is configured, you can connect to Pushbullet and query data from any of the available tables such as Users, Pushes, Devices, Chats, Subscriptions, and Channels.

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

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

Create a SQL Statement to Query Pushbullet

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

sql = "SELECT ,  FROM Users WHERE  = ''"

Extract, Transform, and Load the Pushbullet Data

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

Loading Pushbullet Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Pushbullet 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 Pushbullet 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.api as mod

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

sql = "SELECT ,  FROM Users WHERE  = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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