---
title: "Data Quality Studio: Scheduling Your Data Quality Rule Execution"
url: "https://atlan.com/demos/data-quality-studio-scheduling-your-data-quality-rule-execution/"
description: "Keep data quality checks timely & aligned with SLAs! Schedule rule execution in Atlan’s Data Quality Studio with flexible, automatic cadences."
format: "Video"
video: "https://videos.ctfassets.net/nwa1c00rtgxb/1eRh1tx8Eug83kWch3hIw6/984b4505cb2becebcc3f726f5f491de6/Scheduling_Your_Data_Quality_Rule_Execution.mp4"
content_purpose: ["Product Overview"]
target_persona: ["General", "Data Engineer", "Data Analyst", "Data Scientist", "IT Administrator", "Data Steward", "Data Governance Lead"]
journey_stage: ["S2 - Discovery", "C1 - Onboarding", "C3 - Adoption", "C4 - Expansion"]
use_case_context: ["Training"]
product: ["Data Marketplace - Data Quality"]
content_type: "video transcript"
transcript_source: "contentful"
---

# Data Quality Studio: Scheduling Your Data Quality Rule Execution

Transcript of the video at https://atlan.com/demos/data-quality-studio-scheduling-your-data-quality-rule-execution/

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

## Scheduling Data Quality Rule Execution

Data quality is only useful if it’s timely. If you’re planning to do analysis on a data set on Friday, you’ll want to catch any issues before you dive in—not after. With Atlan’s Data Quality Studio, it’s easy to schedule checks that align with your SLAs. And if you’re using Snowflake, you can even trigger rules automatically when your data changes—no manual runs needed.

Once rules have been created, they will not run until you have created a schedule. In this, we’ll start from our people table in Databricks, where we recently created some new rules, but did not apply a schedule to them.

Clicking on the Data Quality tab, we now see the data quality rules that have been created on this table. In the last run value, it currently shows as inactive for all, as no schedule has been set for these rules. When rules are created, they do not immediately run on Snowflake or Databricks to return results, only running per the schedule that you attach, so it’s important to always add a schedule after creating your rules.

At the top, we see this pointed out, stating the lack of a schedule, so we click on the add schedule.

The add schedule popup window shows, and here, we are able to designate the cadence for which we want all of our rules to run. On the left hand side, we can specify the cadence, whether hourly, daily, specific days of the week, specific days of the month, or even according to a custom cron that we specify.

For each, we also select the run start time, and the time zone for the start time.

Jumping to a Snowflake table, we additionally have the ability in our scheduling options to have our data quality rules run everytime data changes on the table. Back on our databricks table, we select it to run weekly on Mondays and wednesdays at 5 AM. Below, it tells us exactly when our next run will be. Finally, we click save to apply the schedule.

With flexible scheduling options in Atlan’s Data Quality Studio—whether that’s daily, hourly, weekly, or triggered by data changes in Snowflake—you can align your data quality checks with the expectations of your consumers. It’s a simple way to make sure trust in your data keeps pace with your SLAs.