Slack to Superset

This page provides you with instructions on how to extract data from Slack and analyze it in Superset. (If the mechanics of extracting data from Slack 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 Slack?

Slack is a popular cloud-based business communications platform. It provides channel-based messaging along with voice and video chatting capabilities and the ability to share files.

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 Slack

Slack lets developers write code that interacts with the platform through several APIs that provide information about conversations, events, and other objects. For example, to get a particular message from a conversation history from the Conversations API, you would call GET /api/conversations.history?token=authenticationtoken&channel=conversationID&latest=timestamp&inclusive=true&limit=1.

Sample Slack data

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

{
    "ok": true,
    "latest": "1512085950.000216",
    "messages": [
        {
            "type": "message",
            "user": "U012AB3CDE",
            "text": "Nobody would believe us",
            "ts": "1512085950.000216"
        }
    ],
    "has_more": true,
    "pin_count": 0,
    "response_metadata": {
        "next_cursor": "bmV4dF90czoxNTEyMzU2NTI2MDAwMTMw"
    }
}

Preparing Slack 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 Slack 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 Slack 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, Slack includes timestamp fields 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 Slack to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Slack 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 Slack to Redshift, Slack to BigQuery, Slack to Azure Synapse Analytics, Slack to PostgreSQL, Slack to Panoply, and Slack 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 Slack with Superset. With just a few clicks, Stitch starts extracting your Slack 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.