Zoom to Superset

This page provides you with instructions on how to extract data from Zoom and analyze it in Superset. (If the mechanics of extracting data from Zoom seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Zoom?

Zoom is a cloud-based videoconferencing platform. It lets users set up interactive chats and webinars and offers screen-sharing, with up to 500 participants on a single call. It offers plans from the free Zoom Basic to the high-end Zoom Enterprise.

What is Superset?

Apache Superset is a cloud-native data exploration and visualization platform that businesses can use to create business intelligence reports and dashboards. It includes a state-of-the-art SQL IDE, and it's open source software, free of cost. The platform was originally developed at Airbnb and donated to the Apache Software Foundation.

Getting data out of Zoom

Zoom lets developers write code that interacts with the platform through a RESTful API that provides information about accounts, meetings, device, and other objects. For example, to get information about a particular meeting, you would call GET /meetings/{meetingId}.

Sample Zoom data

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

{
  "agenda": "API overview",
  "created_at": "2020-09-09T15:54:24Z",
  "duration": 60,
  "host_id": "ABcdofjdogh11111",
  "id": 1234555466,
  "join_url": "https://zoom.us/j/1234555466",
  "settings": {
    "alternative_hosts": "joe@stitchdata.com",
    "approval_type": 2,
    "audio": "both",
    "auto_recording": "local",
    "close_registration": false,
    "cn_meeting": false,
    "enforce_login": false,
    "enforce_login_domains": "stitchdata.com",
    "global_dial_in_countries": [
      "US"
    ],
    "global_dial_in_numbers": [
      {
        "city": "New York",
        "country": "US",
        "country_name": "US",
        "number": "+1 000011100",
        "type": "toll"
      },
      {
        "city": "San Jose",
        "country": "US",
        "country_name": "US",
        "number": "+1 6699006833",
        "type": "toll"
      },
      {
        "city": "San Jose",
        "country": "US",
        "country_name": "US",
        "number": "+1 221122112211",
        "type": "toll"
      }
    ],
    "host_video": false,
    "in_meeting": false,
    "join_before_host": true,
    "mute_upon_entry": false,
    "participant_video": false,
    "registrants_confirmation_email": true,
    "use_pmi": false,
    "waiting_room": false,
    "watermark": false,
    "registrants_email_notification": true
  },
  "start_time": "2020-08-30T22:00:00Z",
  "start_url": "https://zoom.us/1234555466/cdknfdffgggdfg4MDUxNjY0LCJpYXQiOjE1NjgwNDQ0NjQsImFpZCI6IjRBOWR2QkRqVHphd2J0amxoejNQZ1EiLCJjaWQiOiIifQ.Pz_msGuQwtylTtYQ",
  "status": "waiting",
  "timezone": "America/New_York",
  "topic": "The Zoom API",
  "type": 2,
  "uuid": "iAABBBcccdddd7A=="
}

Preparing Zoom 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. The Zoom 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 Superset

You must replicate data from your SaaS applications to a data warehouse before you can report on it using Superset. Superset can connect to almost 30 databases and data warehouses. Once you choose a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then specify the database schema or tables you want to work with.

Keeping Zoom 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, Zoom includes fields like created_at 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.

From Zoom to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Zoom data in Superset is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Zoom to Redshift, Zoom to BigQuery, Zoom to Azure Synapse Analytics, Zoom to PostgreSQL, Zoom to Panoply, and Zoom to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Zoom with Superset. With just a few clicks, Stitch starts extracting your Zoom data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Superset.