> For the complete documentation index, see [llms.txt](https://upsolver.gitbook.io/content/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://upsolver.gitbook.io/content/reference-1/monitoring/datasets/data-violations.md). # Data Violations The **Data Violations** tab applies to source datasets, and displays the expectations within jobs that ingest to the dataset. Expectations enable you to define how you *expect* the data in a column to arrive, for example, values should be within a range, a set number of characters, or not NULL. By applying expectations during ingestion to capture data quality issues in-flight, you can prevent unwanted values reaching your analytics target. ## Expectations The count of expectations monitoring the data that is ingested into the dataset is displayed in the tab header, and the **Expectations** table details all expectations monitoring the data flowing into your dataset:

**Data Violations** display the expectations in the jobs that write to the dataset.

The Expectations table includes the following information:

Column	Description
Expectation	The name given to the expectation when it was created.
Action	The action that should be taken when the expectation is met: either warn or drop.
Job	The name of the job in which the expectation is included. Multiple jobs may be writing to the dataset. Click on the job name to view the Job Status page.
Violations Today	The count of violations that have been captured today (UTC time).
Total Violations	The total number of violations since the expectation was created.

You can filter the expectations that are visible on this tab, by switching between **All**, **Warn**, and **Drop**. Furthermore, use the **Search** textbox to filter expectations by name. {% hint style="success" %} **Learn More** To discover how expectations can define quality conditions on your data, please see the article [Managing Data Quality - Ingesting Data with Expectations](/content/how-to-guides-1/jobs/advanced-use-cases/managing-data-quality-ingesting-data-with-expectations.md). {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://upsolver.gitbook.io/content/reference-1/monitoring/datasets/data-violations.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.