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

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

Connecting to Brex Data

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

Brex API Profile Settings

Register your application in the Brex Developer Portal at dashboard.brex.com to obtain a Client ID and Client Secret.

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

cnxn = mod.connect("Profile=C:\profiles\Brex.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Brex

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

sql = "SELECT Id, SourceId FROM AccountingRecords WHERE ReviewStatus = 'pending'"

Extract, Transform, and Load the Brex Data

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

Loading Brex Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Brex 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 Brex 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\Brex.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT Id, SourceId FROM AccountingRecords WHERE ReviewStatus = 'pending'"

table1 = etl.fromdb(cnxn,sql)

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

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