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

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

Connecting to ActiveTrail Data

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

ActiveTrail API Profile Settings

ActiveTrail uses API key authentication. To obtain an API key:

1. Log in to your ActiveTrail account at https://app.activetrail.com
2. Navigate to your account settings and locate the API integration section
3. Generate or copy your existing API key

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your ActiveTrail API key.

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

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

Create a SQL Statement to Query ActiveTrail

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

sql = "SELECT Id, Email FROM GroupMembers WHERE GroupId = '356069'"

Extract, Transform, and Load the ActiveTrail Data

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

Loading ActiveTrail Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Email FROM GroupMembers WHERE GroupId = '356069'"

table1 = etl.fromdb(cnxn,sql)

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

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