Slack connector

Set up the Slack connector in Kaivo: authentication, configuration, the 5 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated About 5 hours ago

Kaivo is a fully managed data platform that syncs your Slack data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your project management data instead of moving it.

What is the Slack connector

Sync your Slack channels, messages, threads, and users into BigQuery with Kaivo to analyse workspace activity and engagement alongside your other data.

Category	Tech, Project Management
Status	Generally available
Authentication	API key
Setup	Self-service

Getting started with the Slack connector

Sign up for Kaivo and create a workspace.
Connect your Slack account.
Choose which tables to sync.
Wait for the initial sync to finish.
Query your data in BigQuery or your favourite AI or BI tool.

Authenticating Slack

Authenticate with your API Token.

Field	Description
API Token	A Slack bot token. See the Slack docs for instructions on how to generate it.

Configuring the Slack connector

When you set up the connector, you provide:

Field	Description
Start Date	Any data before this date will not be fetched.
Join all channels	Whether to join all channels or to sync data only from channels the bot is already in. If false, you'll need to manually add the bot to all the channels from which you'd like to sync messages.
Include private channels	Whether to read information from private channels that the bot is already in. If false, only public channels will be read. If true, the bot must be manually added to private channels.
Channel name filter	A channel name list (without leading '#' char) which limits the channels from which you'd like to sync. Empty list means no filter.

Tables and columns synced from Slack

Kaivo syncs 5 tables from Slack into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

How the Slack sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Slack supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.

Frequently asked questions

How long does the initial sync take for Slack?

It depends on how much history is in your Slack account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.

Can I sync only some tables or columns?

Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.

What happens when Slack's schema changes?

New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in Slack, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.

Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in Slack instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the Slack connector in Kaivo and all of its synced data is deleted with it.

Common use cases for Slack data

Channel activity trends

Aggregate channel_messages by channel and date to track message volume, the most active channels, and how usage shifts over time.

Engagement and participation

Combine channel_members with channel_messages to see who is active, who is lurking, and who drives conversation in each channel.

Thread response times

Use threads and their timestamps to measure how quickly questions get answered and where conversations go unresolved.

Workspace directory reporting

Analyse the users table to report on membership, time zones, and deactivated accounts across teams.

Use Slack data in your AI and BI tools

Once Slack data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with Slack data.

Claude

Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →

Power BI

Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →

Data Studio

Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →

Tableau

The premium analytics standard, with native BigQuery integration. Setup guide →

Google Sheets

Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →

Excel

Connect via Power Query's BigQuery connector. Setup guide →

Metabase

Open-source BI tool with strong BigQuery support. Setup guide →

See our pricing page for Slack connector pricing and plan details.

Adform: Sync Adform to BigQuery.
Amplitude: Sync Amplitude to BigQuery.
Auth0: Sync Auth0 to BigQuery.
ClickUp: Sync ClickUp to BigQuery.
Convex: Sync Convex to BigQuery.
GitHub: Sync GitHub to BigQuery.