---
title: "Data Quality Studio: Understanding Data Quality Failures using Failures Rows SQL"
url: "https://atlan.com/demos/data-quality-studio-understanding-data-quality-failures-using-failures-rows-sql/"
excerpt: "Troubleshoot failed data quality rules faster! Access Failure Rows SQL in Atlan's Data Quality Studio to surface row-level issues and start remediation now."
description: "Discover how to move from identifying a failed rule to examining the exact problematic records using Failure Rows SQL in Atlan's Data Quality Studio. This walkthrough shows how to filter for failing rules, open detailed results, and access prebuilt SQL that you can run directly in your source system - or even execute instantly through Atlan Insights. You'll see how quickly you can surface row-level issues and begin remediation without leaving the platform. With direct access to the data behind the failure, troubleshooting becomes faster, clearer, and far more efficient."
format: "Video"
duration: "PT2M16S"
video: "https://videos.ctfassets.net/nwa1c00rtgxb/18vgD0ogIR3mlgITwbX3Ok/cca2205707c8d66d4c758baafe36f202/Understanding_Data_Quality_Failures_using_Failure_Rows_SQL.mp4"
thumbnail: "https://images.ctfassets.net/nwa1c00rtgxb/626FGlzsOFgG3JxfZ9utRo/5eb499aa9e2abfac0a955c0d2f95fda0/data-quality-studio-understanding-failures-via-row-sql.webp"
content_purpose: ["Product Overview"]
target_persona: ["General", "Data Scientist", "Data Analyst", "Data Engineer"]
journey_stage: ["S2 - Discovery", "C1 - Onboarding", "C3 - Adoption", "C4 - Expansion"]
use_case_context: ["Training"]
product: ["Data Marketplace - Data Quality"]
published: "2025-11-17"
updated: "2026-06-02"
content_type: "video transcript"
transcript_source: "sheet"
---

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# Data Quality Studio: Understanding Data Quality Failures using Failures Rows SQL

Transcript of the video at https://atlan.com/demos/data-quality-studio-understanding-data-quality-failures-using-failures-rows-sql/

Understanding Data Quality failures using Failure Rows SQLAtlan's Data Quality Studio gives you clarity on what failed and how often - but it doesn't stop there. For any predefined rule, you can access the failed rows SQL, making it easy to run the exact query on your source system to inspect the data behind the issue for remediation. It's a direct path from detection to investigation. Clicking on the asset icon on the left hand navigation takes us to the asset discovery page. Here, we utilize the Databricks data quality filter for failed status by rule and select null count, a rule that checks to ensure that there are no null values, which can be critical in ensuring that your data is complete.

Our results update and we see several columns and tables that have this failing rule. Clicking on the Customer Dimensions table takes us to the overview page and we click on the data quality tab to see the data quality rules. We filter the results by failed and see that we have several null count failures. Our results show the total amount of failures for each, but what if we wanted to actually return those results. By clicking on one of the results and then clicking on view more details, the results popup expands and we see the failed rows in Databricks option.

We expand this, showing off a SQL statement, prebuilt, that we can simply copy and paste and run on Databricks to return the rows from this table that failed. Since we have Insights set up in Atlan, we can actually get these results in just a few clicks. We copy the query, and then Back at the table, we click on the query drop down in the top navigation and select new SQL. A new insights page opens and start running a sample data query for this table, allowing us to validate we have access and allowing us to understand the data the table contains.

We remove the existing SQL, paste in our query, and hit run. In just a few moments, our results will return, providing us with the exact rows where our data quality failed, allowing us to begin our investigation and remediation. With Atlan's Data Quality Studio, both spotting and digging into data quality issues can happen in one place - right inside Atlan. It's a faster, more focused way to move from problem to solution and keep your data quality journey moving forward.