How to Build an ETL App for ChargeOver 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 ChargeOver-connected applications and pipelines for extracting, transforming, and loading ChargeOver data. This article shows how to connect to ChargeOver with the CData Python Connector and use petl and pandas to extract, transform, and load ChargeOver data.
With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live ChargeOver data in Python. When you issue complex SQL queries from ChargeOver, the driver pushes supported SQL operations, like filters and aggregations, directly to ChargeOver and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to ChargeOver Data
Connecting to ChargeOver 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 ChargeOver Profile on disk (e.g. C:\profiles\Chargeover.apip). Next, set the ProfileSettings connection property to the connection string for ChargeOver (see below).
ChargeOver API Profile Settings
Log into ChargeOver, navigate to Settings > Developer > REST API, enable the API, and copy your Public Key (APIKey) and Private Key (APISecret).
After installing the CData ChargeOver Connector, follow the procedure below to install the other required modules and start accessing ChargeOver 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 ChargeOver 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 ChargeOver Connector to create a connection for working with ChargeOver data.
cnxn = mod.connect("Profile=C:\profiles\Chargeover.apip;ProfileSettings='APIKey=your_public_key;APISecret=your_private_key;Domain=your_subdomain';")
Create a SQL Statement to Query ChargeOver
Use SQL to create a statement for querying ChargeOver. In this article, we read data from the AdminWorkers entity.
sql = "SELECT Id, Email FROM AdminWorkers WHERE Username = 'john_doe'"
Extract, Transform, and Load the ChargeOver Data
With the query results stored in a DataFrame, we can use petl to extract, transform, and load the ChargeOver data. In this example, we extract ChargeOver data, sort the data by the Email column, and load the data into a CSV file.
Loading ChargeOver Data into a CSV File
table1 = etl.fromdb(cnxn,sql) table2 = etl.sort(table1,'Email') etl.tocsv(table2,'adminworkers_data.csv')
With the CData API Driver for Python, you can work with ChargeOver 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 ChargeOver 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\Chargeover.apip;ProfileSettings='APIKey=your_public_key;APISecret=your_private_key;Domain=your_subdomain';")
sql = "SELECT Id, Email FROM AdminWorkers WHERE Username = 'john_doe'"
table1 = etl.fromdb(cnxn,sql)
table2 = etl.sort(table1,'Email')
etl.tocsv(table2,'adminworkers_data.csv')