> 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/ingested-data.md). # Ingested Data The Ingested Data tab applies to source and target datasets that Upsolver ingests and transforms. ## Table Last Update The timestamp of the last update by jobs writing to the table, indicating when the table was last modified. This only applies to datasets in your data lake:

Discover when your data was last updated by the job.

*** ## Overview The **Overview** card provides instant visibility into the following: * **Number of Fields**: the total count of all fields in the dataset, including system columns that are automatically added by Upsolver. * **Number of Unique Fields**: the number of columns containing unique values. This number maps to the **Has Value Repetition** column in the **Ingested Data Statistics** table. * **Number of Arrays Fields**: the count of columns that contain array data out of the total count.

The **Overview** card displays the number of fields, keys, and arrays.

## Written Rows Over Time This graph is a visual display to show the number of rows ingested over the selected timespan, enabling you to quickly discover spikes or drops in your pipeline and troubleshoot unexpected volumes. By default, the chart shows written rows from the initial ingestion through to now., however you can change this. Click on the **Time View** button, which displays **Lifetime** by default, to change the timespan displayed in the dataset report:

**Written Rows Over Time** visually displays the flow of data ingested to the dataset.

*** You can select an area on the chart to drill into the statistics for a deeper dive into your data. If you need to travel back and find an issue, you can keep selecting the date and time to view the data exactly as it was at a specific point:

Select a time span and drill into the data at a specific point in time

## Written Data Statistics This table provides powerful metadata about your dataset. You can click on a column name in the table to drill deeper into the [Column](/content/reference-1/monitoring/datasets/ingested-data/column.md) details, which includes stats for the Upsolver system columns appended to your dataset. To filter the columns in the dataset, type your search string into the **Search** box. Click the **x** icon in the **Search** box to clear the results. The system columns that Upsolver appends to your datasets are included in the column list:

The **Ingested Data Statistics** table provides observability on staged and target schema data.

The **Written Data Statistics** table provides the following information:

Measurement	Description
Column	The name of the ingested column. Click on the column name to drill through to the Column page.
Type	The column data type, e.g. `string`, `date`, `long`. This is the type that Upsolver automatically inferred on ingestion.
Appears Unique	Returns a Boolean value displayed as a tick icon for `TRUE` if all values are distinct, or a cross icon for `FALSE` if there are repeated values.
Top Values	The most common three data values within this column.
Distinct Values	The count of distinct values within the column.
Density	The percentage of columns that have a value.
Min	The minimum data value in this column.
Max	The maximum data value in this column.
First Seen	The first date and time that data was seen in this column.
Last Seen	The last date and time that data was seen in this column.

For datasets written to the data lake, you can download the statistics to a table inspection report in CSV format for your own consumption. Click the **Download** icon to the right of the **Search** box to download the contents of the **Written Data Statistics** table to CSV. You can then perform your own discovery using this data. --- # 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/ingested-data.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.