Merge pull request #189152 from whhender/catalog-updates-6

Updating author, date, acrolinx

Author: ShannonLeavitt

articles/data-catalog/data-catalog-how-to-annotate.md

@@ -1,11 +1,11 @@
 ---
 title: How to annotate data sources in Azure Data Catalog
 description: How-to article highlighting how to annotate data assets in Azure Data Catalog, including friendly names, tags, descriptions, and experts.
-author: JasonWHowell
-ms.author: jasonh
+author: ChandraKavya
+ms.author: kchandra
 ms.service: data-catalog
 ms.topic: how-to
-ms.date: 08/01/2019
+ms.date: 02/18/2022
 ---
 # How to annotate data sources in Azure Data Catalog
 
@@ -16,6 +16,7 @@ ms.date: 08/01/2019
 **Microsoft Azure Data Catalog** is a fully managed cloud service that serves as a system of registration and system of discovery for enterprise data sources. In other words, Data Catalog is all about helping people discover, understand, and use data sources, and helping organizations to get more value from their existing data. When a data source is registered with Data Catalog, its metadata is copied and indexed by the service, but the story doesn’t end there. Data Catalog allows users to provide their own descriptive metadata – such as descriptions and tags – to supplement the metadata extracted from the data source, and to make the data source more understandable to more people.
 
 ## Annotation and crowdsourcing
+
 Everyone has an opinion. And this is a good thing.
 Data Catalog recognizes that different users have different perspectives on enterprise data sources, and that each of these perspectives can be valuable. Consider the following scenario:
 
@@ -25,36 +26,39 @@ Data Catalog recognizes that different users have different perspectives on ente
 * The data steward knows how the assets and attributes in the data source map to the enterprise data model.
 * The analyst knows how the data is used in the context of the business processes they support.
 
-Each of these perspectives is valuable, and Data Catalog uses a crowdsourcing approach to metadata that allows each one to be captured and used to provide a complete picture of registered data sources. Using the Data Catalog portal, each user can add and edit their own annotations, while being able to view annotations provided by other users.
+Each of these perspectives is valuable, and Data Catalog uses a crowdsourcing approach to metadata that allows each one to be captured and used to provide a complete picture of registered data sources. Each user can add and edit their own annotations in the Data Catalog portal, while being able to view annotations provided by other users.
 
 ## Different types of annotations
+
 Data Catalog supports the following types of annotations:
 
 | Annotation | Notes |
 | --- | --- |
 | Friendly name |Friendly names can be supplied at the data asset level, to make the data assets more easily understood. Friendly names are most useful when the underlying object name is cryptic, abbreviated or otherwise not meaningful to users. |
 | Description |Descriptions can be supplied at the data asset and attribute / column levels. Descriptions are free-form short text annotations that describe the user’s perspective on the data asset or its use. |
 | Tags (user tags) |Tags can be supplied at the data asset and attribute / column levels. User tags are user-defined labels that can be used to categorize data assets or attributes. |
-| Tags (glossary tags) |Tags can be supplied at the data asset and attribute / column levels. Glossary tags are centrally-defined glossary terms that can be used to categorize data assets or attributes using a common business taxonomy. For more information see  [How to set up the Business Glossary for Governed Tagging](data-catalog-how-to-business-glossary.md) |
-| Experts |Experts can be supplied at the data asset level. Experts identify users or groups with expert perspectives on the data and can serve as points of contact for users who discover the registered data sources and have questions that are not answered by the existing annotations. |
-| Request access |Request access information can be supplied at the data asset level. This information is for users who discover a data source that they do not yet have permissions to access. Users can enter the email address of the user or group who grants access, the URL of the process or tool that users need to gain access, or can enter the process itself as text. |
+| Tags (glossary tags) |Tags can be supplied at the data asset and attribute / column levels. Glossary tags are centrally defined glossary terms that can be used to categorize data assets or attributes using a common business taxonomy. For more information, see  [How to set up the Business Glossary for Governed Tagging](data-catalog-how-to-business-glossary.md) |
+| Experts |Experts can be supplied at the data asset level. Experts identify users or groups with expert perspectives on the data and can serve as points of contact for users who discover the registered data sources and have questions that aren't answered by the existing annotations. |
+| Request access |Request access information can be supplied at the data asset level. This information is for users who discover a data source that they don't yet have permissions to access. Users can enter the email address of the user or group who grants access, the URL of the process or tool that users need to gain access, or can enter the process itself as text. |
 | Documentation |Documentation can be supplied at the data asset level. Asset documentation is rich text information that can include links and images, and which can provide any information not conveyed through descriptions and tags. |
 
 ## Annotating multiple assets
-When selecting multiple data assets in the Data Catalog portal, users can annotate all selected assets in a single operation. Annotations will apply to all selected assets, making it easy to select and provide a consistent description and sets of tags and experts for related data assets.
+
+Users can select multiple data assets in the Data Catalog portal, and annotate all selected assets in a single operation. Annotations will apply to all selected assets, making it easy to select and provide a consistent description and sets of tags and experts for related data assets.
 
 > [!NOTE]
 > Tags and experts can also be provided when registering data assets using the Data Catalog data source registration tool.
->
->
 
-When selecting multiple tables and views, only columns that all selected data assets have in common will be displayed in the Data Catalog portal. This allows users to provide tags and descriptions for all columns with the same name for all selected assets.
+When multiple tables and views are selected, only columns that all selected data assets have in common will be displayed in the Data Catalog portal. This allows users to provide tags and descriptions for all columns with the same name for all selected assets.
 
 ## Annotations and discovery
+
 Just as the metadata extracted from the data source during registration is added to the Data Catalog search index, user-supplied metadata is also indexed. This means that not only do annotations make it easier for users to understand the data they discover, annotations also make it easier for users to discover the annotated data assets by searching using the terms that make sense to them.
 
 ## Summary
+
 Registering a data source with Data Catalog makes that data discoverable by copying structural and descriptive metadata from the data source into the Catalog service. Once a data source has been registered, users can provide annotations to make easier to discover and understand from within the Data Catalog portal.
 
 ## See also
+
 * [Get Started with Azure Data Catalog](data-catalog-get-started.md) tutorial for step-by-step details about how to annotate data sources.

articles/data-catalog/data-catalog-how-to-data-profile.md

@@ -1,11 +1,11 @@
 ---
 title: How to use data profiling data sources in Azure Data Catalog
 description: How-to article highlighting how to include table- and column-level data profiles when registering data sources in Azure Data Catalog, and how to use data profiles to understand data sources.
-author: JasonWHowell
-ms.author: jasonh
+author: ChandraKavya
+ms.author: kchandra
 ms.service: data-catalog
 ms.topic: how-to
-ms.date: 08/01/2019
+ms.date: 02/18/2022
 ---
 # How to data profile data sources in Azure Data Catalog
 
@@ -21,8 +21,6 @@ The **Data Profiling** feature of **Azure Data Catalog** examines the data from
 
 Data profiling examines the data in the data source being registered, and collects statistics and information about that data. During data source discovery, these statistics can help you determine the suitability of the data to solve their business problem.
 
-<!-- In [How to discover data sources](data-catalog-how-to-discover.md), you learn about **Azure Data Catalog's** extensive search capabilities including searching for data assets that have a profile. See [How to include a data profile when registering a data source](#howto). -->
-
 The following data sources support data profiling:
 
 * SQL Server (including Azure SQL DB and Azure Synapse Analytics) tables and views
@@ -39,15 +37,12 @@ Including data profiles when registering data assets helps users answer question
 
 > [!NOTE]
 > You can also add documentation to an asset to describe how data could be integrated into an application. See [How to document data sources](data-catalog-how-to-documentation.md).
->
-
-<a name="howto"></a>
 
 ## How to include a data profile when registering a data source
 
 It's easy to include a profile of your data source. When you register a data source, in the **Objects to be registered** panel of the data source registration tool, choose **Include Data Profile**.
 
-![Include Data Profile checkbox](media/data-catalog-data-profile/data-catalog-register-profile.png)
+:::image type="content" source="media/data-catalog-data-profile/data-catalog-register-profile.png" alt-text="The Include Data Profile box is checked at the bottom of the Objects to be registered window.":::
 
 To learn more about how to register data sources, see [How to register data sources](data-catalog-how-to-register.md) and [Get started with Azure Data Catalog](data-catalog-get-started.md).
 
@@ -57,13 +52,12 @@ To discover data assets that include a data profile, you can include `has:tableD
 
 > [!NOTE]
 > Selecting **Include Data Profile** in the data source registration tool includes both table and column-level profile information. However, the Data Catalog API allows data assets to be registered with only one set of profile information included.
->
 
 ## Viewing data profile information
 
 Once you find a suitable data source with a profile, you can view the data profile details. To view the data profile, select a data asset and choose **Data Profile** in the Data Catalog portal window.
 
-![Data Profile tab](media/data-catalog-data-profile/data-catalog-view.png)
+:::image type="content" source="media/data-catalog-data-profile/data-catalog-view.png" alt-text="The data profile tab is selected at the top of the page, between columns and documentation.":::
 
 A data profile in **Azure Data Catalog** shows table and column profile information including: