Databricks Data Quality Monitoring with Zapier

Zapier connects over 9,000 apps. This course shows you how to put all of those connections to work with AI, in two very different ways.

The use case is a problem most data teams know well. Databricks runs your data quality checks just fine, but the results end up in a big, noisy table. It tells you what failed. It doesn't tell you how serious it is, who owns it, or what to do about it. That context usually lives somewhere else entirely, like a runbook in a spreadsheet. In this course you will use Zapier to bridge that gap.

In the first part you build a conversational data quality assistant. You set up a Zapier MCP server that exposes Databricks and Google Sheets as tools, then put a small Streamlit chat app powered by Claude in front of it. You ask something like "Which failed expectations are critical and who owns them?" and Claude queries Databricks, looks up the owners in your runbook, and gives you the answer. You don't write a single SQL query or integration. You will also add ChatGPT as a third tool and see how easily you can route a specific task to a specific model.

In the second part you switch to the Zapier SDK for full control. With Claude Code handling the setup, you work with a Next.js and React dashboard that shows the health of 100 pipeline runs at a glance, and an alerter that detects incidents, finds the right owner, and emails them through Gmail. The connections underneath stay the same, but now your code decides every step. That makes it fast, predictable, and cheap, because no model has to reason through each call.

Along the way you will learn exactly when an agent is worth the extra time and tokens and when deterministic code is the better choice. You can follow along on free tiers: Zapier's free plan, Databricks Free Edition, Google Sheets, and the free trial credits Anthropic offers new API users.

By the end of this course you will know how to use Zapier as the connection layer for both AI agents and your own applications, and how to turn raw pipeline metrics into alerts that land in the right person's inbox.