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

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

Connecting to Sentry Data

Connecting to Sentry 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

Sentry uses token-based authentication. To obtain an Auth Token:

1. Log in to your Sentry account at https://sentry.io
2. Navigate to Settings > Auth Tokens
3. Click "Create New Token"
4. Select the required scopes and click "Create Token"
5. Copy the generated token (it will only be shown once)

After obtaining your Auth Token, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Sentry Auth Token.
• OrganizationId: Set this to your Sentry organization slug or ID.

Example Connection String

Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";

Connecting to Sentry

Once the authentication is configured, you can connect to Sentry and query data from any of the available tables such as Organizations, Projects, Issues, and Events.

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

cnxn = mod.connect("Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";")

Create a SQL Statement to Query Sentry

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

sql = "SELECT , FROM UserOrganizations WHERE = ''"

Extract, Transform, and Load the Sentry Data

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

Loading Sentry Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Sentry 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 Sentry data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

cnxn = mod.connect("Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";")

sql = "SELECT , FROM UserOrganizations WHERE = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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