# Working with datasets in an Quick Sight topic
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|  Applies to:  Enterprise Edition  | 


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|    Intended audience:  Amazon Quick administrators and authors  | 

When you create a topic, you can add additional datasets to it or import datasets from existing dashboards. At any time, you can edit metadata for a dataset and set a data refresh schedule. You can also add new fields to a dataset in a topic by creating calculated fields, filters, or named entities.

**Topics**
+ [Adding datasets to a topic in Amazon Quick Sight](topics-data-add.md)
+ [Adding datasets with row-level security (RLS) to a Amazon Quick Sight topic](topics-data-rls.md)
+ [Refreshing datasets in a Quick Sight topic](topics-data-refresh.md)
+ [Removing datasets from a Amazon Quick Sight topic](topics-data-remove.md)
+ [Adding calculated fields to a Amazon Quick Sight topic dataset](topics-data-calculated-fields.md)
+ [Adding filters to a Amazon Quick Sight topic dataset](topics-data-filters.md)
+ [Adding named entities to a Amazon Quick Sight topic dataset](topics-data-entities.md)

# Adding datasets to a topic in Amazon Quick Sight
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At any time, you can add datasets to a topic. Use the following procedure to learn how.

**To add datasets to a topic**

1. Open the topic that you want to add one or more datasets to.

1. On the **Summary** page, choose **Data**. Then, under **Datasets**, choose **Add datasets**.

1. On the **Add datasets** page that opens, choose the dataset or datasets that you want to add, and then choose **Add datasets**.

   The dataset is added to the topic and the dataset's unique string values are indexed. You can edit the field configurations right away. For more information, see [Refreshing Quick Sight topic indexes](topics-index.md). For more information about editing field configurations , see [Making Quick Sight topics natural-language-friendly](topics-natural-language.md).

# Adding datasets with row-level security (RLS) to a Amazon Quick Sight topic
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You can add datasets that contain row-level security (RLS) to topics. All fields in a topic respect the RLS rules applied to your dataset. For example, if a user asks, "show me sales by region," the data that is returned is based on the user's access to the underlying data. So, if they're only allowed to see the East region, only data for the East region appears in the answer.

RLS rules are applied to automatic suggestions when users are asking questions. As users enter questions, only the values that they have access to are suggested to them. If a user enters a question about a dimensional value that they don't have access to, they do not get an answer for that value. For example, suppose that the same user is entering the question, "show me sales in the West region." In this case, they do not get a suggestion or an answer for it, even if they ask, because they don't have RLS access to that region.

By default, Quick Sight allows users to ask questions regarding fields based on the user's permissions in RLS. Continue to use this option if your field contains sensitive data that you want to restrict access to. If your fields don't contain sensitive information and you want all users to see the information in suggestions, then you can choose to allow questions for all values in the field.

**To allow questions for all fields**

1. From the Quick homepage, choose **Data**.

1. Under the **Datasets** tab, choose the dataset that you added RLS to, and then choose **Edit dataset**.

   For more information about adding RLS to a dataset, see [Using row-level security in Amazon Quick](row-level-security.md).

1. On the data preparation page, choose the field menu (the three dots) for a field that you want to allow , and then choose **Row level security **.

1. On the **Row level security for Quick** page that opens, choose **Allow users to ask questions regarding all values on this field**.

1. Choose **Apply**.

1. When finished editing the dataset, choose **Save & publish** in the blue toolbar at upper right.

1. Add the dataset to your topic. For more information, see the previous section, [Adding datasets to a topic in Amazon Quick Sight](topics-data-add.md).

If you currently allow users to ask questions regarding all values, but want to implement the dataset's RLS rules to protect sensitive information, then repeat steps 1–4 and choose **Allow users to ask questions regarding this field based on their permissions**. When you are done, refresh the dataset in your topic. For more information, see [Refreshing datasets in a Quick Sight topic](topics-data-refresh.md).

# Refreshing datasets in a Quick Sight topic
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When you add a dataset to a topic, you can specify how often you want that dataset to refresh. When you refresh datasets in a topic, the index is refreshed for that topic with any new and updated information. 

Your datasets aren't replicated when you add them to a topic. An index of unique string values is created and metrics are not indexed. For example, measures stored as integers are not indexed. Questions asked always fetch the latest sales metrics based on data in your dataset.

For more information about refreshing the topic index, see [Refreshing Quick Sight topic indexes](topics-index.md)

You can set a refresh schedule for a dataset in a topic, or refresh the dataset manually. You can also see when the data was last refreshed. 

**To set a refresh schedule for a topic dataset**

1. Open the topic that you want to change.

1. On the **Summary** page, choose **Data**. Then, under **Datasets**, expand the dataset that you want to set a refresh schedule for.

1. Choose **Add schedule**, and then do one of the following in the **Add refresh schedule** page that opens.
   + If the dataset is a SPICE dataset, select **Refresh topic when dataset is imported into SPICE**.

     Currently, hourly refresh SPICE datasets aren't supported. SPICE datasets that are set to refresh every hour are automatically converted to a daily refresh. For more information about setting refresh schedules for SPICE datasets, see [Refreshing SPICE data](refreshing-imported-data.md).
   + If the dataset is a direct query dataset, do the following:

     1. For **Timezone**, choose a time zone.

     1. For **Repeats**, choose how often you want the refresh to happen. You can choose to refresh the dataset daily, weekly, or monthly.

     1. For **Refresh time**, enter the time that you want the refresh to start.

     1. For **Start first refresh on**, choose a date that you want start refreshing the dataset on.

1. Choose **Save**.

**To manually refresh a dataset**

1. On the topic **Summary** page, choose **Data**. Then, under **Datasets**, choose the dataset that you want to refresh.

1. Choose **Refresh now**.

**To view refresh history for a dataset**

1. On the topic **Summary** page, choose **Data**. Then, under **Datasets**, choose the dataset that you want to see refresh history for.

1. Choose **View history**.

   The **Update history** page opens with a list of the times the dataset was refreshed.

# Removing datasets from a Amazon Quick Sight topic
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You can remove datasets from a topic. Removing datasets from a topic doesn't delete them from Quick Sight. 

Use the following procedure to remove a dataset from a topic.

**To remove a dataset from a topic**

1. Open the topic that you want to change.

1. On the **Summary** page, choose **Data**. Then, under **Datasets**, choose the dataset menu (the three dots) at right, and then choose **Remove from topic**.

1. On the **Are you sure you want to delete?** page that opens, choose **Delete** to remove the dataset from the topic. Choose **Cancel** if you don't want to remove the dataset from the topic.

# Adding calculated fields to a Amazon Quick Sight topic dataset
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You can create new fields in a topic by creating calculated fields. *Calculated fields* are fields that use a combination of one or two fields from a dataset with a supported function to create new data. 

For example, if your dataset contains columns for sales and expenses, you can combine them in a calculated field with a simple function to create a profit column. The function might look like the following: `sum({Sales}) - sum({Expenses})`.

**To add a calculated field to a topic**

1. Open the topic that you want to change.

1. In the topic, choose the **Data** tab.

1. For **Actions**, choose **Add calculated field**.

1. In the calculations editor that opens, do the following:

   1. Give the calculated field a friendly name.

   1. For **Datasets** at right, choose a dataset that you want to use for the calculated field.

   1. Enter a calculation in the calculation editor at left.

      You can see a list of fields in the dataset in the **Fields** pane at right. You can also see a list of supported functions in the **Functions** pane at right.

      For more information about the functions and operators you can use to create calculations in Quick Sight, see the [Calculated field function and operator reference for Amazon QuickFunctions and operators](calculated-field-reference.md).

1. When finished, choose **Save**.

   The calculated field is added to the list of fields in the topic. You can add a description to it and configure metadata for it to make it more natural language friendly.

# Adding filters to a Amazon Quick Sight topic dataset
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Sometimes your business users (readers) might ask questions that contain terms that map to multiple cells of values in the data. For example, let's say one of your readers asks, "Show me weekly sales trend in the west." *West* in this instance refers to both the `Northwest` and `Southwest` values in the `Region` field, and requires the data to be filtered to generate an answer. You can add filters to a topic to support requests like these.

**To add a filter to a topic**

1. Open the topic that you want to add a filter to.

1. In the topic, choose the **Data** tab.

1. For **Actions**, choose **Add filter**.

1. In the **Filter configuration** page that opens, do the following:

   1. For **Name**, enter a friendly name for the filter.

   1. For **Dataset**, choose a dataset that you want to apply the filter to.

   1. For **Field**, choose the field that you want to filter.

      Depending on the type of field you choose, you're offered different filtering options.
      + If you chose a text field (for example, `Region`), do the following:

        1. For **Filter type**, choose the type of filter that you want.

           For more information about filter text fields, see [Adding text filters](add-a-text-filter-data-prep.md).

        1. For **Rule**, choose a rule.

        1. For **Value**, enter one or more values.
      + If you chose a date field (for example, `Date`), do the following:

        1. For **Filter type**, choose the type of filter that you want, and then enter the date or dates that you want to apply the filter to.

           For more information about filtering dates, see [Adding date filters](add-a-date-filter2.md).
      + If you chose a numeric field (for example, `Compensation`), do the following:

        1. For **Aggregation**, choose how you want to aggregate the filtered values.

        1. For **Rule**, choose a rule for the filter, and then enter a value for that rule.

        For more information about filtering numeric fields, see [Adding numeric filters](add-a-numeric-filter-data-prep.md).

   1. (Optional) To specify when the filter is applied, choose **Apply the filter anytime the dataset is used**, and then choose one of the following:
      + **Apply always** – When you choose this option, the filter is applied whenever any column from the dataset you specified is linked to a question.
      + **Apply always, unless a question results in an explicit filter from the dataset** – When you choose this option, the filter is applied whenever any column from the dataset you specified is linked to a question. However, if the question mentions an explicit filter on the same field, the filter isn't applied.

   1. When finished, choose **Save**.

      The filter is added to the list of fields in the topic. You can edit the description for it or adjust when the filter is applied.

# Adding named entities to a Amazon Quick Sight topic dataset
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When asking questions about your topic, your readers might refer to multiple columns of data without stating each column explicitly. For example, they might ask for the address of a transaction. What they actually mean is that they want the branch name, state, and city of where the transaction was made. To support requests like this, you can create a named entity.

A *named entity* is a collection of fields that display together in an answer. For example, using the transaction address example, you can create a named entity called `Address`. You can then add the `Branch Name`, `State`, and `City` columns to it, which already exist in the dataset. When someone asks a question about address, the answer displays the branch, state, and city where a transaction took place.

**To add a named entity to a topic**

1. Open the topic that you want to change.

1. In the topic, choose the **Data** tab.

1. For **Actions**, choose **Add named entity**.

1. In the **Named entity** page that opens, do the following:

   1. For **Dataset**, choose a dataset.

   1. For **Name**, enter a friendly name for the named entity.

   1. For **Description**, enter a description of the named entity.

   1. (Optional) For **Synonyms**, add any alternate names that you think your readers might use to refer to the named entity or the data it contains.

   1. Choose **Add field**, and then choose a field from the list.

      Choose **Add field** again to add another field.

      The ordering of the fields listed here are the order they appear in answers. To move a field, choose the six dots at left of the field name and drag and drop the field to the order that you want.

   1. When finished, choose **Save**.

   The named entity is added to the list of fields in the topic. You can add edit the description for it and add synonyms to it to make it more natural language friendly.