How to Build an ETL App for Office 365 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 Office 365 and the petl framework, you can build Office 365-connected applications and pipelines for extracting, transforming, and loading Office 365 data. This article shows how to connect to Office 365 with the CData Python Connector and use petl and pandas to extract, transform, and load Office 365 data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Office 365 data in Python. When you issue complex SQL queries from Office 365, the driver pushes supported SQL operations, like filters and aggregations, directly to Office 365 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Office 365 Data

Connecting to Office 365 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.

Office 365 uses the OAuth authentication standard. To authenticate requests, obtain the OAuthClientId, OAuthClientSecret, and OAuthCallbackURL by registering an app with Office 365. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

After installing the CData Office 365 Connector, follow the procedure below to install the other required modules and start accessing Office 365 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 Office 365 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.office365 as mod

You can now connect with a connection string. Use the connect function for the CData Office 365 Connector to create a connection for working with Office 365 data.

cnxn = mod.connect("OAuthClientId=MyApplicationId;OAuthClientSecret=MyAppKey;OAuthCallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Office 365

Use SQL to create a statement for querying Office 365. In this article, we read data from the Files entity.

sql = "SELECT Name, Size FROM Files WHERE UserId = '54f34750-0d34-47c9-9949-9fac4791cddb'"

Extract, Transform, and Load the Office 365 Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Office 365 data. In this example, we extract Office 365 data, sort the data by the Size column, and load the data into a CSV file.

Loading Office 365 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 Office 365

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

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

With the CData Python Connector for Office 365, you can work with Office 365 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 Office 365 to start building Python apps and scripts with connectivity to Office 365 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.office365 as mod

cnxn = mod.connect("OAuthClientId=MyApplicationId;OAuthClientSecret=MyAppKey;OAuthCallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Name, Size FROM Files WHERE UserId = '54f34750-0d34-47c9-9949-9fac4791cddb'"

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')