# Creating a schema mapping
<a name="create-schema-mapping"></a>

This procedure describes the process of creating a schema mapping using the [AWS Entity Resolution console](https://console.aws.amazon.com/entityresolution/home). 

There are three ways to create a schema mapping:
+ Import existing input data using the **Import from AWS Glue** option – Use this creation method to define input fields starting with pre-populated columns from an AWS Glue table using a guided flow.
+ Manually defining input data using the **Build custom schema** option – Use this creation method to manually define the input fields using a guided flow.
+ Manually create using the **Use JSON editor** option – Use a JSON editor to manually create, use a sample, or import existing input data.
**Note**  
The **Unique ID** and **Input fields** aren't available with this option.

------
#### [ Import from AWS Glue ]

**To create schema mapping by importing existing input data from AWS Glue**

1. Sign in to the AWS Management Console and open the AWS Entity Resolution console at [https://console.aws.amazon.com/entityresolution/](https://console.aws.amazon.com/entityresolution/).

1. In the left navigation pane, under **Data preparation**, choose **Schema mappings**.

1. On the **Schema mappings** page, in the upper right corner, choose **Create schema mapping**.

1. For **Step 1: Specify schema details**, do the following: 

   1. For **Name and creation method**, enter a **Schema mapping name** and an optional **Description**.

   1. For **Creation method**, choose **Import from AWS Glue**.

   1. Choose the **AWS Region**.

   1. Choose the **AWS Glue database**.

   1. Choose the **AWS Glue table**.

      To create a new table, go to the AWS Glue console [https://console.aws.amazon.com/glue/](https://console.aws.amazon.com/glue/). For more information, see [AWS Glue tables](https://docs.aws.amazon.com/glue/latest/dg/tables-described.html) in the *AWS Glue User Guide*.

   1. For **Unique ID**, specify the column that distinctly references each row of your data.   
**Example**  

      For example: **Primary\$1key**, **Row\$1ID**, or **Record\$1ID**.
**Note**  
The **Unique ID** column is required. The **Unique ID** must be a unique identifier within a single table. However, across different tables, the **Unique ID** can have duplicate values. If the **Unique ID** isn't specified, isn't unique within the same source, or overlaps in terms of attribute names across sources, then AWS Entity Resolution rejects the record when the matching workflow is run. If you are using this schema mapping in a rule-based matching workflow, the **Unique ID** must not exceed 38 characters.

   1. For **Input fields**, choose the columns you want to use for matching and for optional pass through. 

      You can choose a maximum of 34 columns total for both matching and pass through. 

      1. Under **Matching**, choose the columns you to use as input fields for matching.

         You can choose a maximum of 24 columns total for matching. 

      1. Select **Add columns for pass through** if you want to specify the columns that aren't used for matching. 

      1. (Optional) Under **Pass through**, choose the columns to include as pass through columns.
**Note**  
Do not use any of the following reserved names as a column name in your data when running machine learning-based matching workflows: "`MatchId`", "`MatchRule`", `RecordId`, `SourceId`", " and `TargetId`". Using any of these reserved names will result in naming conflicts and failed ML-based matching workflows.

   1. (Optional) If you want to enable **Tags** for the resource, choose **Add new tag**, and then enter the **Key** and **Value** pair.

   1. Choose **Next**.

1. For **Step 2: Map input fields**, define the input fields you want to use for matching and for optional pass through.

   1. For **Input fields for matching**, for each **Input field**, 
      + Specify the **Attribute type** to classify the data.
      + Specify the **Match key name** to enable input field comparison to your matching workflow. Certain match key names are automatically associated with specific attribute types by default. 
      + Select the **Hashed** checkbox if the column value for that input field is hashed or leave the checkbox blank if the value is cleartext.
**Note**  
If you're creating a schema mapping to use with the LiveRamp provider service-based matching technique, then you can:   
Specify the **Attribute type** for the Provider ID as **LiveRamp ID**.
Specify the **Attribute type** for the **name** field as either multiple fields (such as **First name**, **Last name**) or in one field.
Specify the **Attribute type** for the **street address** field as either multiple fields (such as **Street address 1**, **Street address 2**, ) or in one field (**Full address**).  
If matching against an address, a zip code (**Postal code**) is required.
If you include email (**Email address**) or phone (**Phone number**) with a name, those fields can match against the street address.
**Note**  
If you're creating a schema mapping to use with the TransUnion provider service-based matching technique, then you can specify any of the following **Attribute types**:  
**Full name**, **First name**, **Last name**
**Full address**, **Street address 1**, **City**, **State**, **Country**, **Postal code**
**Phone number**
**Email address**
**Date**
**Digital Identifiers**: **IPV4**, **IPV6**, or **MAID**
**Note**  
If you're creating a schema mapping to use with the machine learning-based matching workflow, your dataset must contain at least one of the following **Attribute types**:   
**Full name**
**Full address**
**Full phone**
**Email address**
**Date** with a **Match key name** of **Date of birth**
Don't specify the **Attribute type** for any of these attributes as a **Custom string**.

   1. (Optional) For **Input fields for pass through**, add the input fields that won't be matched and their corresponding **Hashing status**. 

      The **Hashing status** indicates if the column value for that input field is hashed or cleartext. 

   1. Choose **Next**.

1. For **Step 3: Group data**, you can group the **Name**, **Address**, and **Phone number** input fields if they have been separated into multiple fields.

   This step concatenates the related input fields into one field, which enables you to compare them as one field in a matching workflow.

   If you don't have any data mapped to the **Name**, **Address**, or **Phone number** input fields, then this section will be blank.

   You can also add more groups if you have more types of data. 

   1. If you want to group **Name** input data: 

      For **Full name**, choose two or more **Input fields** you want to group. 

      The **Group name** and **Match key** are automatically associated with the data type. 

      You can update the **Group name** and the **Match key** with a custom match key can contain up to 255 characters, including letters, numbers, underscores (\$1), or hyphens (-).

      Choose **Add group** to add another group.
**Note**  
Normalization is only supported for **Full name**.  
If you want to normalize the **Full name** subtypes, then assign the following subtypes to the **Full name** group: **First name**, **Middle name**, and **Last name**.

   1. If you want to group **Address** input data: 

      For **Full address**, choose two or more **Input fields** fields you want to group. 

      The **Group name** and **Match key**. are automatically associated with the data type. 

      You can update the **Group name** and the **Match key** with a custom match key can contain up to 255 characters, including letters, numbers, underscores (\$1), or hyphens (-).

      Choose **Add group** to add another group.
**Note**  
Normalization is only supported for **Full address**.  
If you want to normalize the **Full address** subtypes, then assign the following subtypes to the **Full address** group: **Street address 1**, **Street address 2**: **Street address 3 name**, **City name**, **State**, **Country**, and **Postal code**.

   1. If you want to group **Phone** input data: 

      For **Full phone**, choose two or more **Input fields** fields you want to group. 

      The **Group name** and **Match key**. are automatically associated with the data type.

      You can update the **Group name** and the **Match key** with a custom match key can contain up to 255 characters, including letters, numbers, underscores (\$1), or hyphens (-).

      Choose **Add group** to add another group.
**Note**  
Normalization is only supported for **Full phone**.  
If you want to normalize the **Full phone** subtypes, then assign the following subtypes to the **Full phone** group: **Phone number**, and **Phone country code**.

   1. Choose **Next**.

1. For **Step 4: Review and create**, do the following:

   1. Review the selections that you made for the previous steps and edit if necessary.

   1. Choose **Create schema mapping**.
**Note**  
You can’t modify a schema mapping after you associate it to a workflow. You can clone a schema mapping if you want to use an existing configuration to create a new schema mapping.

After you create the schema mapping, you're ready to [create a matching workflow](create-matching-workflow.md) or [create an ID namespace](id-namespace.md).

------
#### [ Build custom schema ]

**To create a schema mapping using the **Build custom schema** option**

1. Sign in to the AWS Management Console and open the AWS Entity Resolution console at [https://console.aws.amazon.com/entityresolution/](https://console.aws.amazon.com/entityresolution/).

1. In the left navigation pane, under **Data preparation**, choose **Schema mappings**.

1. On the **Schema mappings** page, in the upper right corner, choose **Create schema mapping**.

1. For **Step 1: Specify schema details**, do the following: 

   1. For name and creation method, enter a **Schema mapping name** and an optional **Description**.

   1. For **Creation method**, choose **Build custom schema**.

   1. For **Unique ID**, enter a unique ID to identify each row of your data.   
**Example**  

      For example: **Primary\$1key**, **Row\$1ID**, or **Record\$1ID**.
**Note**  
The **Unique ID** column is required. The **Unique ID** must be a unique identifier within a single table. However, across different tables, the **Unique ID** can have duplicate values. If the **Unique ID** isn't specified, isn't unique within the same source, or overlaps in terms of attribute names across sources, then AWS Entity Resolution rejects the record when the matching workflow is run. If you are using this schema mapping in a rule-based matching workflow, the **Unique ID** must not exceed 38 characters.

   1. (Optional) If you want to enable **Tags** for the resource, choose **Add new tag**, and then enter the **Key** and **Value** pair.

   1. Choose **Next**.

1. For **Step 2: Map input fields**, define the input fields you want to use for matching and for optional pass through.

   You can define a maximum of 34 columns total for both matching and pass through. 

   1. For **Input fields for matching**, enter an **Input field**.
**Note**  
Do not use any of the following reserved names as a column name in your data when running machine learning-based matching workflows: "`MatchId`", "`MatchRule`", `RecordId`, `SourceId`", " and `TargetId`". Using any of these reserved names will result in naming conflicts and failed ML-based matching workflows.

   1. Select the **Attribute type** to classify the data. 
**Note**  
If you're creating a schema mapping to use with the [LiveRamp provider service-based matching technique](create-matching-workflow-provider.md#create-mw-liveramp), then you can specify the providerID **Attribute type** as **LiveRamp ID**. If you want to include PII data in the output, then you must specify the **Attribute type** as **Custom string**.
**Note**  
If you're creating a schema mapping to use with the TransUnion provider service-based matching technique, then you can specify any of the following **Attribute types**:  
**Full name**, **First name**, **Last name**
**Full address**, **Street address 1**, **City**, **State**, **Country**, **Postal code**
**Phone number**
**Email address**
**Date**
**Digital Identifiers**: **IPV4**, **IPV6**, or **MAID**
**Note**  
If you're creating a schema mapping to use with the [machine learning-based matching workflow](create-matching-workflow-ml.md), your dataset must contain at least one of the following **Attribute types**:   
**Full name**
**Full address**
**Full phone**
**Email address**
**Date** with a **Match key name** of **Date of birth**
Don't specify the **Attribute type** for any of these attributes as a **Custom string**.

   1. Select the **Match key name** to enable input field comparison to your matching workflow. 

      Certain match key names are automatically associated with specific attribute types by default. 

   1. Select the **Hashed** checkbox if the column value for that input field is hashed or leave the checkbox blank if the value is cleartext.

   1. Choose **Add input field** to add more input fields.

      You can add a maximum of 24 input fields total for matching. 

   1. (Optional) For **Input fields for pass through**, add the input fields that won't be matched and their corresponding **Hashing status**. 

   1. Choose **Next**.

1. For **Step 3: Group data**, you can group the **Name**, **Address**, **Phone number** input fields if they have been separated into multiple fields. 

   This step concatenates the related input fields into one field, which enables you to compare them as one field in a matching workflow.

   If you don't have any data mapped to **Name**, **Address**, **Phone number** input fields, then this section will be blank.

   You can also add more groups if you have more types of data. 

   1. If you want to group **Name** input data: 

      For **Full name**, choose two or more **Input fields** you want to group. 

      The **Group name** and **Match key** are automatically associated with the data type. 

      You can update the **Group name** and the **Match key** with a custom match key can contain up to 255 characters, including letters, numbers, underscores (\$1), or hyphens (-).

      Choose **Add group** to add another group.
**Note**  
Normalization is only supported for **Full name**.  
If you want to normalize the **Full name** subtypes, then assign the following subtypes to the **Full name** group: **First name**, **Middle name**, and **Last name**.

   1. If you want to group **Address** input data: 

      For **Full address**, choose two or more **Input fields** fields you want to group. 

      The **Group name** and **Match key**. are automatically associated with the data type. 

      You can update the **Group name** and the **Match key** with a custom match key can contain up to 255 characters, including letters, numbers, underscores (\$1), or hyphens (-).

      Choose **Add group** to add another group.
**Note**  
Normalization is only supported for **Full address**.  
If you want to normalize the **Full address** subtypes, then assign the following subtypes to the **Full address** group: **Street address 1**, **Street address 2**: **Street address 3 name**, **City name**, **State**, **Country**, and **Postal code**.

   1. If you want to group **Phone** input data: 

      For **Full phone**, choose two or more **Input fields** fields you want to group. 

      The **Group name** and **Match key**. are automatically associated with the data type. 

      You can update the **Group name** and the **Match key** with a custom match key can contain up to 255 characters, including letters, numbers, underscores (\$1), or hyphens (-).

      Choose **Add group** to add another group.
**Note**  
Normalization is only supported for **Full phone**.  
If you want to normalize the **Full phone** subtypes, then assign the following subtypes to the **Full phone** group: **Phone number**, and **Phone country code**.

   1. Choose **Next**.

1. For **Step 4: Review and create**, do the following:

   1. Review the selections that you made for the previous steps and edit if necessary.

   1. Choose **Create schema mapping**.
**Note**  
You can’t modify a schema mapping after you associate it with a workflow. You can clone a schema mapping if you want to use an existing configuration to create a new schema mapping.

After you create the schema mapping, you're ready to [create a matching workflow](create-matching-workflow.md) or [create an ID namespace](id-namespace.md).

------
#### [ Use JSON editor ]

**To create a schema mapping by using the JSON editor**

1. Sign in to the AWS Management Console and open the AWS Entity Resolution console at [https://console.aws.amazon.com/entityresolution/](https://console.aws.amazon.com/entityresolution/).

1. In the left navigation pane, under **Data preparation**, choose **Schema mappings**.

1. On the **Schema mappings** page, in the upper right corner, choose **Create schema mapping**.

1. For **Step 1: Specify schema details**, do the following: 

   1. For name and creation method, enter a **Schema mapping name** and an optional **Description**.

   1. For **Creation method**, choose **Use JSON editor**.

   1. (Optional) If you want to enable **Tags** for the resource, choose **Add new tag**, and then enter the **Key** and **Value** pair.

   1. Choose **Next**.

1. For **Step 2: Specify mapping**:

   1. Start building the schema in the JSON editor or choose one of the following options based on your goal:    
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/entityresolution/latest/userguide/create-schema-mapping.html)
**Note**  
Normalization is only supported for the following **types**: `NAME`, `ADDRESS`, `PHONE`, and `EMAIL_ADRESS`.  
If you want to normalize the `NAME` subtypes, then assign the following subtypes to the `NAME` **groupName**: `NAME_FIRST`, `NAME_MIDDLE`, and `NAME_LAST`  
If you want to normalize the `ADDRESS` subtypes, then assign the following subtypes to the `ADDRESS` **groupName**: `ADDRESS_STREET1`, `ADDRESS_STREET2`, `ADDRESS_STREET3`, `ADDRESS_CITY`, `ADDRESS_STATE`, `ADDRESS_COUNTRY`, and `ADDRESS_POSTALCODE`.  
If you want to normalize the `PHONE` subtypes, then assign the following subtypes to the `PHONE` **groupName**: `PHONE_NUMBER` and `PHONE_COUNTRYCODE`.

   1. Choose **Next**.

1. For **Step 3: Review and create**:

   1. Review the selections that you made for the previous steps and edit if necessary.

   1. Choose **Create schema mapping**.
**Note**  
You can’t modify a schema mapping after you associate it with a workflow. You can clone a schema mapping if you want to use an existing configuration to create a new schema mapping.

After you create the schema mapping, you're ready to [create a matching workflow](create-matching-workflow.md) or [create an ID namespace](id-namespace.md).

------