Microsoft Teams to Panoply

This page provides you with instructions on how to extract data from Microsoft Teams and load it into Panoply. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Microsoft Teams?

Microsoft Teams is a suite of tools that provides video, voice, and text chat, along with the ability to collaborate on Microsoft Office documents, to users on Windows, macOS, iOS, and Android platforms. It's included free as part of Microsoft Office 365, the company's SaaS office suite.

What is Panoply?

Panoply is a fully managed data warehouse service that can spin up an Amazon Redshift instance in just a few clicks. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration. With Panoply, you can use your favorite analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

Getting data out of Microsoft Teams

Microsoft lets developers interact with Teams objects through the RESTful Graph API that lets developers fetch information about teams, channels, schedules, and other objects. For example, to list the members of a team, you would call GET https://graph.microsoft.com/beta/teams/{teamsId}/members.

Sample Microsoft Teams data

Here's an example of the kind of response you might see with a query like the one above.

{
    "@odata.context": "https://graph.microsoft.com/beta/$metadata#teams('ee0f5ae2-8bc6-4ae5-8466-7daeebbfa062')/members",
    "@odata.count": 2,
    "value": [
        {
            "@odata.type": "#microsoft.graph.aadUserConversationMember",
            "id": "ZWUwZjVhZTItOGJjNi00YWU1LTg0NjYtN2RhZWViYmZhMDYyIyM3Mzc2MWYwNi0yYWM5LTQ2OWMtOWYxMC0yNzlhOGNjMjY3Zjk=",
            "roles": [],
            "displayName": "Amos True",
            "userId": "73761f06-2ac9-469c-9f10-279a8cc267f9",
            "email": "AmosT@M365x987948.OnMicrosoft.com"
        },
        {
            "@odata.type": "#microsoft.graph.aadUserConversationMember",
            "id": "ZWUwZjVhZTItOGJjNi00YWU1LTg0NjYtN2RhZWViYmZhMDYyIyM1OThlZmNkNC1lNTQ5LTQwMmEtOTYwMi0wYjUwMjAxZmFlYmU=",
            "roles": [
                "owner"
            ],
            "displayName": "MOD Administrator",
            "userId": "598efcd4-e549-402a-9602-0b50201faebe",
            "email": "admin@M365x987948.OnMicrosoft.com"
        }
    ]
}

Preparing Microsoft Teams data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Microsoft's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" — some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Panoply

Once you have identified all of the columns you want to insert, you can use the CREATE TABLE statement in Panoply's Redshift data warehouse to create a table to receive all of the data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Keeping Microsoft Teams data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, the Graph API results for some endpoints include fields like lastModifiedDateTime that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Other data warehouse options

Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to move data from Microsoft Teams to Panoply automatically. With just a few clicks, Stitch starts extracting your Microsoft Teams data, structuring it in a way that's optimized for analysis, and inserting that data into your Panoply data warehouse.