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

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

Connecting to Canny Data

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

Canny API Profile Settings

Canny uses a secret API key to control access to the API. The API key is sent as a JSON body parameter named 'apiKey' in every HTTP POST request to the Canny API endpoints.

To authenticate with the Canny API, you need your company-level secret API key. You can find this key in your Canny account under Settings > API.

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Canny company-level secret API key from the Settings > API page in your Canny dashboard.

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

cnxn = mod.connect("Profile=C:\profiles\Canny.apip;ProfileSettings='APIKey=your-canny-api-key';")

Create a SQL Statement to Query Canny

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

sql = "SELECT Id, Name FROM Boards WHERE IsPrivate = 'false'"

Extract, Transform, and Load the Canny Data

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

Loading Canny Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Canny 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 Canny 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\Canny.apip;ProfileSettings='APIKey=your-canny-api-key';")

sql = "SELECT Id, Name FROM Boards WHERE IsPrivate = 'false'"

table1 = etl.fromdb(cnxn,sql)

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

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