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

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

Connecting to Box Data

Connecting to Box 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.

Box uses the OAuth standard to authenticate. To authenticate to Box, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

After installing the CData Box Connector, follow the procedure below to install the other required modules and start accessing Box 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 Box 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.box as mod

You can now connect with a connection string. Use the connect function for the CData Box Connector to create a connection for working with Box data.

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Box

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

sql = "SELECT Name, Size FROM Files WHERE Id = '123'"

Extract, Transform, and Load the Box Data

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

Loading Box Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Files table.

Adding New Rows to Box

table1 = [ ['Name','Size'], ['NewName1','NewSize1'], ['NewName2','NewSize2'], ['NewName3','NewSize3'] ]

etl.appenddb(table1, cnxn, 'Files')

With the CData Python Connector for Box, you can work with Box 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 Python Connector for Box to start building Python apps and scripts with connectivity to Box 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.box as mod

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Name, Size FROM Files WHERE Id = '123'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Size'], ['NewName1','NewSize1'], ['NewName2','NewSize2'], ['NewName3','NewSize3'] ]

etl.appenddb(table3, cnxn, 'Files')