--- title: "Data Quality Studio: Automate Data Quality for AI & Analytics" url: "https://atlan.com/activate-recording/data-quality-studio/" description: "Watch Atlan Data Quality Studio in action. Learn how to automate data quality with no-code checks and AI-suggested rules that run directly on Snowflake and Databricks." keywords: "Atlan data quality, snowflake, databricks, data quality rules, data observability, data quality studio" --- > Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/ # Data Quality Studio: Quality Checks on Snowflake and Databricks Activate 2025 / On demand / Recorded August 22, 2025 / 3:38 Watch: https://atlan.com/activate-recording/data-quality-studio/ Tom adds data quality rules to an employee table in Atlan: a no-code null check, a custom SQL rule, AI-suggested rules, and a Slack thread asking the table's owner what threshold to set. ## Speakers - Tom, Atlan ## Related videos - [Activate 2025: AI Governance Studio](https://atlan.com/activate-recording/ai-governance/) (8 min) - [Activate 2025: Atlan AI Search](https://atlan.com/activate-recording/ai-search/) (4 min) - [From 8% to 100% metadata coverage](https://atlan.com/know/enterprise-context-layer/videos/context-agents-metadata-coverage-demo/) (5 min) - [What is a context layer for AI systems?](https://atlan.com/know/enterprise-context-layer/videos/what-is-a-context-layer/) (10 min) ## Transcript **Host** [0:00] At Atlan, we have always believed in the power of data quality signals to power trust and context in data. This has driven first-class native integrations with top companies in observability and anomaly detection. Today, we're excited to take that one step further with a native Data Quality Studio experience in collaboration with Snowflake and Databricks' recently launched data quality functionality. Over to you, Tom, to show us what this looks like in action. **Tom** [0:28] Perfect. Again, take the screen from you. Awesome. All right. 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 Atlan's 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. [1:17] That's where Atlan's 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, 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. [2:02] 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 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. [2:47] 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 because he owns this. "Do you know what I should set this value to?" I add it as a resource for all to see. [3:19] 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 estate through partners like Anomalo, Atlan brings it all together, making data quality collaborative, scalable, and built for the way teams really work.