Introduction to Workflow Automation

Balancing speed of delivery with building in an optimized way is a constant tradeoff for data teams. This means that inefficiencies are introduced that go unnoticed, which could be slowing down your pipelines and dashboards, or racking up unnecessary cost over time.

With Alvin's optimization insights, you get full visibility into your data warehouse, with the potential to uncover huge cost savings as they become available. Build automated workflows to put optimization on auto-pilot, and focus your team on delivering business value, safe in the knowledge that your data warehouse is operating at maximum efficiency.

How It Works

1. Detect Events Alvin automatically detects inefficiencies in your warehouse and logs them as events.

2. Trigger Workflows Workflows are triggered based on a subset of events that you care about, like stale dbt models, bad clustering, or cost anomalies.

3. Automate Actions Once a Workflow is triggered, you can automate one or more actions, including:

   1. Create contextualized issue: Transform raw events into contextualized issues: What happened? How much does it cost you? What can you do about it? Sort issues by impact (e.g. potential cost savings) to prioritize the biggest wins. You decide whether to dismiss an issue, add it to your backlog, or take action immediately, right from Alvin.

   2. Generate a PR in Github to delete a model

   3. Switch data warehouse pricing model

   4. Send notification to Slack

   5. More automated actions coming soon!

4. Track Resolution and Impact Once resolved, Alvin helps you verify the outcome: Did costs go down? Was the problem resolved fully?