Slack + Bruin

Source

Ingest Slack data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Operational analytics
Slack data in your warehouse means analytics that Slack's built-in reporting can't provide. Cross-tool, cross-team, custom.
Cross-tool project views
Combine Slack with Jira, GitHub, Slack, and other tools. One dashboard that shows the real state of projects.
Team workload insights
Understand collaboration patterns, bottlenecks, and workload distribution from Slack data — automatically updated.
No manual data pulling
Slack data syncs on schedule. Managers and leads get fresh data without asking anyone.

For data & engineering teams

How it works

Incremental sync
Only sync new and changed Slack records. No full reloads, no wasted compute.
YAML-defined, Git-versioned
Your Slack pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Schema change handling
Bruin detects Slack schema changes automatically. No manual intervention when fields get added or renamed.
Cross-tool joins
Combine Slack data with other tools in SQL transforms. Bruin resolves dependencies across sources automatically.

Before you start

Slack app creation

Bot token with appropriate scopes

Step 1

Add your Slack connection

Connect using Slack bot token. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_keySlack bot token (starts with xoxb-)

connections:
  slack:
    type: slack
    uri: "slack://?api_key=<api_key>"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Slack and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.

Available tables

channelsmessagesusersconversationsfiles

name: raw.slack_channels
type: ingestr

parameters:
  source_connection: slack
  source_table: 'channels'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Slack data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.

Validate workspace data synced completely

Ensure record IDs are unique and titles are present

Catch missing or null fields on every sync

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique
  - name: title
    checks:
      - name: not_null

custom_checks:
  - name: workspace sync is complete
    query: |
      SELECT COUNT(*) > 0
      FROM raw.slack_channels

Step 4

Run it

One command. Bruin connects to Slack, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops — bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Slack to your dashboards

$ bruin run .

Running pipeline...

  slack_channels
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

Other Productivity integrations

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Asana

Bitbucket

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ClickUp

Ready to connect Slack?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs