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

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

Connecting to Clio Data

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

Clio API Profile Settings

Clio uses OAuth-based authentication.

First, register an OAuth application with Clio. You can do so by logging to your Developer Account and clicking the Add button. Enter details and select the scope of your application here - these details will be shown to Clio users when they're asked to authorize your application. Your Oauth application will be assigned a client id (key) and a client secret (secret). Additionally set the Region in ProfileSettings connection property.

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

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientId: Set this to the client_id that is specified in you app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in you app settings.
• CallbackURL: Set this to the Redirect URI that is specified in your app settings.
• Region: Set this in ProfileSettings to your Clio geographic region. Defaults to app.clio.com.

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

cnxn = mod.connect("Profile=C:\profiles\Clio.apip;ProfileSettings='Region=your_region';Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Clio

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

sql = "SELECT Id, Total FROM Bills WHERE State = 'awaiting_payment'"

Extract, Transform, and Load the Clio Data

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

Loading Clio Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Total FROM Bills WHERE State = 'awaiting_payment'"

table1 = etl.fromdb(cnxn,sql)

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

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