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

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

Connecting to BugHerd Data

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

BugHerd API Profile Settings

BugHerd uses HTTP Basic authentication with an API key as the username. To obtain an API key:

1. Sign in to your BugHerd account at https://www.bugherd.com
2. Navigate to Settings > General Settings
3. Locate the API Key section and copy the generated key

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your BugHerd API key. The driver uses the key as the Basic auth username and sets the password internally to the literal value 'x' as required by BugHerd.

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

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

Create a SQL Statement to Query BugHerd

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

sql = "SELECT Id, Title FROM Tasks WHERE ProjectId = '519666'"

Extract, Transform, and Load the BugHerd Data

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

Loading BugHerd Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Title FROM Tasks WHERE ProjectId = '519666'"

table1 = etl.fromdb(cnxn,sql)

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

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