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

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

Connecting to Rootly Data

Connecting to Rootly 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

To authenticate using an API key, you will need to obtain your API key from your Rootly account settings.

To get your API key:

1. Log in to your Rootly account
2. Navigate to Settings > API & Integrations
3. Click on "API Tokens"
4. Copy the generated token

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Rootly API token.

Example Connection String

Profile=Rootly.apip;Authscheme=APIKey;ProfileSettings="APIKey=your_apikey";

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

cnxn = mod.connect("Profile=Rootly.apip;Authscheme=APIKey;ProfileSettings="APIKey=your_apikey";")

Create a SQL Statement to Query Rootly

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

sql = "SELECT ,  FROM Incidents WHERE Status = 'started'"

Extract, Transform, and Load the Rootly Data

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

Loading Rootly Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Rootly 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 Rootly 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=Rootly.apip;Authscheme=APIKey;ProfileSettings="APIKey=your_apikey";")

sql = "SELECT ,  FROM Incidents WHERE Status = 'started'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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