---
title: "Data Quality Studio: An Introduction and Demo"
url: "https://atlan.com/demos/data-quality-studio-introduction/"
description: "Unify data quality with Atlan Data Quality Studio across Snowflake, Databricks, and your entire data estate for collaborative, scalable, and trustworthy insights."
format: "Video"
video: "https://videos.ctfassets.net/nwa1c00rtgxb/65u9F4urs1BXjGJUcjAaEK/812ad168ede1a7be3595b785f6010cff/Introducing_the_Atlan_Data_Quality_Studio.mp4"
content_purpose: ["Product Overview"]
target_persona: ["General", "Data Steward", "IT Administrator"]
journey_stage: ["S2 - Discovery", "C1 - Onboarding", "C3 - Adoption"]
use_case_context: ["Training"]
product: ["Data Marketplace - Data Quality"]
content_type: "video transcript"
transcript_source: "contentful"
---

# Data Quality Studio: An Introduction and Demo

Transcript of the video at https://atlan.com/demos/data-quality-studio-introduction/

<!-- Body: machine transcript stored on the Contentful entry (field contentBody), timestamps kept, product names corrected. Not yet edited by a person. -->

Introducing the Atlan Data Quality Studio

[00:00] When it comes to data quality, we see two kinds of needs from our customers. The first is the Cold Start problem. Some are focused on their Snowflake or Databricks warehouse. They want something simple, a way to set up data quality checks fast, with or without code, and in a system that both the technical team and the business team can use.

That's where Atlas Data Quality Studio shines. It runs natively on Snowflake and Databricks, so you can start small and get to value quickly. The second is low visibility means low trust. Others have a broader challenge. They need data quality across their entire estate. That's where Atlas partner ecosystem and app framework come in.

We can bring in signals from our partners or custom in-house data quality solutions and surface them right alongside your warehouse. Check, giving you one place to see the full picture. Let's start at the reporting level here. Right away [01:00] I can see aggregated results. Which rules are passing, which are failing, and what tables are most popular that have no rules on the right hand side.

This gives me a clear starting point. Let's click into the employee dimension here to investigate what's going on when we get to the data quality studio. We'll see this table doesn't have any rules yet. I could add them one by one from custom SQL checks to no code options. You know what? Let me show you both quickly.

Let's start with a null count. Here. I select the salesperson ID column. I set the threshold, set my alert priority, and click create. Just like that. My first rule is live. Next, I'm gonna add a custom SQL rule. I paste in my query, in this case, checking name lengths, give it a name, fill in the details, and hit create.

Doing this [02:00] column by column can seem a little daunting though, especially if you have tens or hundreds of columns to fill out. That's where our AI suggestions can help Atlan scans. The schema recommends a set of rules, and I can choose which ones to apply in seconds, but I'm actually not sure if I selected the right thresholds.

As an engineer. Maybe I need to contact the business. So from Atlan, I open up a Slack chat. I find my business intelligence channel in Slack, and I message Sean 'cause he owns this. Do you know what I should set this value to? I add it as a resource for all to see. So whether you're getting started with Snowflake or Databricks and need fast, visible data quality, or you need to unify signals across your whole data state through partners like Anoma.

Atlan brings it all together, making data quality, collaborative, scalable, and built for the way teams really work