![]() ![]() Slack offers an API that allows an application to consume messages through events. This simple graph will provide the data model for our analytics. There are four kinds of nodes in the graph: User, Message, Word and Channel, and we draw edges for reactions, posts, word count, and messages. For styling purposes, each user, besides their unique id and name, has their avatar. They also react with emojis to messages posted by other users.Įach message contains a certain amount of words. The graph model is straightforward, and it’s illustrated below:Įach user represents a unique node in the graph. ![]() Whenever you add a bot to the channel, the bot will collect the last X messages (this is configurable when starting the bot) and use those messages as cold start. The bot can only gather information from channels it is a part of. ![]() We knew that we wanted to do something with Slack data because the API was friendly, bots are fun to play with, and modeling with it would be relatively straightforward. If you want to jump right to the code, check out the GitHub repo, and if you want to learn more about it, join our Discord Community Chat! The Data Source Our team spent Hackathon week building a graph application on top of streaming Slack data. This week we’re looking at a Slack bot that can help you understand how your teammates interact. We recently held a company-wide hackathon where we challenged each other to build compelling, useful applications using a streaming data source, Kafka, Memgraph, and a Web Application backend. ![]()
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