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

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

Connecting to Microsoft Teams Data

Connecting to Microsoft Teams 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.

You can connect to MS Teams using the embedded OAuth connectivity. When you connect, the MS Teams OAuth endpoint opens in your browser. Log in and grant permissions to complete the OAuth process. See the OAuth section in the online Help documentation for more information on other OAuth authentication flows.

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

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

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

Create a SQL Statement to Query Microsoft Teams

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

sql = "SELECT subject, location_displayName FROM Teams WHERE Id = 'Jq74mCczmFXk1tC10GB'"

Extract, Transform, and Load the Microsoft Teams Data

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

Loading Microsoft Teams Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Microsoft Teams

table1 = [ ['subject','location_displayName'], ['Newsubject1','Newlocation_displayName1'], ['Newsubject2','Newlocation_displayName2'], ['Newsubject3','Newlocation_displayName3'] ]

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

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

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

sql = "SELECT subject, location_displayName FROM Teams WHERE Id = 'Jq74mCczmFXk1tC10GB'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['subject','location_displayName'], ['Newsubject1','Newlocation_displayName1'], ['Newsubject2','Newlocation_displayName2'], ['Newsubject3','Newlocation_displayName3'] ]

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