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

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

Connecting to Productboard Data

Connecting to Productboard 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.

Authentication

To authenticate to ProductBoard, and connect to your own data or to allow other users to connect to their data, you can use API Key authentication.

Using API Key Authentication

To authenticate using an API Key, you need to obtain your API Key from your ProductBoard workspace settings.

You can then connect by setting the AuthScheme to APIKey and providing your API key:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your API key from ProductBoard.

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

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

Create a SQL Statement to Query Productboard

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

sql = "SELECT ,  FROM Features WHERE IsArchived = 'false'"

Extract, Transform, and Load the Productboard Data

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

Loading Productboard Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

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

sql = "SELECT ,  FROM Features WHERE IsArchived = 'false'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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