データ品質ルールを再利用する

このドキュメントでは、Knowledge Catalog（旧 Dataplex Universal Catalog）のデータ品質ルールを再利用して、標準化されたビジネスルールを定義して管理する方法について説明します。

ルールを再利用すると、ルールテンプレートを使用して、複数のデータ品質ルールとスキャンで複雑なビジネスルール定義や標準化されたビジネスルール定義を共有できます。このドキュメントでは、再利用可能なルールテンプレートの設定、作成、管理の方法と、データ品質ルールをメタデータアスペクトとしてカタログエントリにアタッチする方法についても説明します。

ユースケース

データ品質ルールの再利用は、次のような場合に使用できます。

ルール定義の標準化と共有: カスタムルールテンプレートを使用して、複雑なビジネスルール定義や標準化されたビジネスルール定義を保存します。これにより、テンプレート化された SQL 式を使用して、共通の定義を配布する時間と労力を削減できます。たとえば、中央のデータガバナンスチームは、組織全体で再利用される標準の有効なメールまたは有効な SSNテンプレートを定義し、整合性を確保して、重複するルールを管理する運用上のオーバーヘッドを削減できます。
ガバナンス主導の品質の実装: BigQuery テーブルとビジネス用語集の用語エントリで Knowledge Catalog アスペクトを使用して、データルールをメタデータとして宣言します。これにより、ルールを検索して再利用できるようになります。たとえば、列を用語集の用語にリンクすると、その用語に定義された検証ルールを自動的に継承し、セマンティックメタデータの継承を通じて自動化されたガバナンスポリシーを有効にできます。
再利用可能なルールの検索と検出: セマンティック検索を使用して、組織内の既存のルールを見つけます。これにより、データアナリストとエンジニアは、検証済みの標準化されたルールセット（「ベースラインの財務定数」など）を検出して、SQL を一から記述することなく、新しいプロジェクトのデータ品質をブートストラップできます。
コールドスタートの問題の解消: null チェックや範囲の期待値など、頻繁に使用される評価にはシステムルールテンプレートを活用します。これらの組み込みテンプレートを使用すると、カスタム SQL を記述しなくても、一般的なシナリオのデータ品質モニタリングをすばやく設定できます。
関心の分離の有効化: 中央のガバナンスチームが検証済みのルールテンプレートを作成し、エンジニアリングチームが複雑な SQL を記述または維持することなく、これらのルールをデータアセットに適用できるようにします。責任を明確に分担することで、組織の俊敏性が向上し、データ品質基準が企業全体で一貫して適用されるようになります。

始める前に

Dataplex API を有効にする
API を有効にするために必要なロール
API を有効にするには、serviceusage.services.enable 権限を含む Service Usage 管理者 IAM ロール（roles/serviceusage.serviceUsageAdmin）が必要です。詳しくは、ロールを付与する方法をご覧ください。
API の有効化

データ品質ルールの再利用を使用する前に、次の要件を満たしていることを確認してください。

Dataplex API 環境を設定する

このドキュメントの REST API の例を使用するには、gcurl のエイリアスを設定し、${DATAPLEX_API} 環境変数を構成します。

gcurl のエイリアスを設定します。これにより、認証トークンを含むショートカットが作成され、API リクエストの JSON コンテンツタイプが設定されます。
```
alias gcurl='curl -H "Authorization: Bearer $(gcloud auth print-access-token)" -H "Content-Type: application/json"'
```
DATAPLEX_API 変数を設定します。
```
DATAPLEX_API="dataplex.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION"
```
次のように置き換えます。
- PROJECT_ID: プロジェクト ID。
- LOCATION: スキャンまたはリソースがあるロケーション（us-central1 など）。

サービスアカウントを設定する

再利用可能なルールでデータ品質スキャンを実行するには、サービスアカウントが必要です。サービスアカウントを作成します。このサービスアカウントには、次の Identity and Access Management のロールと権限があります。

サービスアカウントをホストするプロジェクトに対する iam.serviceAccounts.actAs 権限が必要です（通常は roles/iam.serviceAccountUser ロールを使用します）。
スキャンプロジェクトの Dataplex サービスエージェント（service-PROJECT_ID@gcp-sa-dataplex.iam.gserviceaccount.com）に、サービスアカウントに対する iam.serviceAccounts.getAccessToken 権限を付与します（たとえば、roles/iam.serviceAccountTokenCreator ロールを使用します）。
サービスアカウントには次の権限が必要です。
- スキャンするテーブルに対する bigquery.tables.getData（roles/bigquery.dataViewer を使用するなど）。
- スキャンプロジェクトの bigquery.jobs.insert（roles/bigquery.jobUser を使用するなど）。
- エクスポートデータセットに対する roles/bigquery.dataEditor（エクスポートを使用する場合）。

必要なロールと権限

特定のタスクに必要な次の IAM ロールがあることを確認します。

データスキャン管理: データスキャンのロールデータスキャンリソースを管理するために必要。
ルールテンプレート管理: ルールテンプレートを作成または更新するには、ルールテンプレートのエントリグループまたはプロジェクト内のエントリを管理するために必要な権限を持つ必要があります。具体的には、roles/dataplex.catalogEditor または roles/dataplex.entryOwner を使用すると、これらの権限が付与されます。
ルールからのルールテンプレートの参照: ルールで参照されるルールテンプレートのエントリグループまたはプロジェクトに対する dataplex.entries.get 権限と dataplex.entries.getData権限が必要です。
データ品質ルールを BigQuery テーブルにアタッチする: データ品質ルールを Knowledge Catalog メタデータとしてアタッチするには、次のいずれかが必要です:
- テーブルに対する bigquery.tables.update または roles/bigquery.dataEditor と、テーブルロケーションの @bigquery エントリグループに対する dataplex.entryGroups.useDataRulesAspect。
- @bigquery エントリグループに対する roles/dataplex.catalogEditor。
データ品質ルールをビジネス用語集の用語にアタッチする: データ品質ルールを Knowledge Catalog メタデータとしてアタッチするには、次のいずれかが必要です。
- 用語に対する dataplex.glossaryTerms.update と、@dataplex エントリグループに対する dataplex.entryGroups.useDataRulesAspect。
- @dataplex エントリグループに対する roles/dataplex.catalogEditor。
- bigquery.tables.get と bigquery.tables.getData。
- テーブルロケーションの @bigquery エントリグループに対する dataplex.entries.get と dataplex.entries.getData。

ルールテンプレートの SQL クエリ構文

ルールテンプレートの SQL ロジックを記述する場合は、無効な行を返すステートメントを指定する必要があります。クエリが行を返すと、ルールは失敗します。詳細については、SqlAssertion をご覧ください。

ルールテンプレートの SQL を記述する際は、次のガイドラインに従ってください。

SQL ステートメントの末尾のセミコロンを省略します。
${param(name)} を使用して入力パラメータを参照します（例: ${param(min_value)}）。
Use $${...} to escape a literal ${...} and prevent it from being replaced as a parameter.
Parameter variables are case-sensitive.

System-supported parameters

You can use the following system-supported parameters in your rule template SQL:

${project()}: The project ID of the resource being scanned.
${dataset()}: The BigQuery dataset ID of the resource being scanned, formatted as PROJECT_ID.DATASET_ID.
${table()}: The BigQuery table ID of the resource being scanned, formatted as PROJECT_ID.DATASET_ID.TABLE_ID.
${column()}: The column the rule is evaluated on. An error occurs during rule evaluation if the rule is attached to the table level but references ${column()}.
${data()}: A reference to the data source table and all of its precondition filters like row filters, sampling percentages, and incremental filters defined in the scan specification. For more information, see Data reference parameter.

Example 1: Validate column values are between two values

The following example validates that all values in a column are between a minimum and maximum value:

SELECT *
FROM ${data()}
WHERE
  NOT ((${column()}>=${param(min_value)} AND ${column()}<=${param(max_value)}) IS TRUE)

Note the following:

Using NOT(condition) IS TRUE returns invalid rows, including rows with NULL values in the column.
Using ${data()} limits the scope of rows evaluated to the source table and its filters, such as row filters, sampling percentages, and incremental filters.
Using ${column()} lets you reference the column that the rule using this template is evaluated on.

Example 2: Foreign key validation

The following example verifies that each value in a column exists in a primary key column of another table:

SELECT t.*
FROM ${data()} AS t
LEFT JOIN `${param(reference_table)}` AS s
  ON t.${column()} = s.`${param(reference_column)}`
WHERE s.`${param(reference_column)}` IS NULL

Input parameters for this template are as follows:

reference_table: The name of the reference table containing the primary keys. Use the format PROJECT_ID.DATASET_ID.TABLE_ID.
reference_column: The name of the primary key column in the reference table.

System rule templates

Knowledge Catalog provides system rule templates that can be used in any region. Knowledge Catalog manages these templates in the dataplex-templates project under the rule-library entry group. An example of a full resource name is projects/dataplex-templates/locations/global/entryGroups/rule-library/entries/non_null_expectation.

To view the list of all the available system rule templates, see System rule templates list.

To find the available list of system rule templates, select one of the following options:

Console

In the Google Cloud console, go to the Data profiling & quality page.

Go to Data profiling & quality
Click Rule libraries > System.
To see the list of available system rule templates, click rule-library.

When creating a new rule, you can select the system rule templates in the Choose rule types menu.

REST

To find the available list of system rule templates, use the entries.list method:

gcurl "https://dataplex.googleapis.com/v1/projects/dataplex-templates/locations/global/entryGroups/rule-library/entries"

Known differences between system rule templates and built-in rules

The following table describes the differences between system rule templates and built-in rules:

Feature	System rule templates	Built-in rules
Source	Reusable templates in the catalog	Built-in in the API
Referencing	Can be referenced by catalog entries and scans	Can only be used in scans

The following list describes additional differences in how metrics are calculated for system rule templates:

Assertion Row Count metric: This metric is populated for all template reference rules, not just SQL assertion rules.
Statistic Range Expectation rule template: Rule metrics from evaluation of rules referencing this template wouldn't contain the nullCount metric. Because it is an aggregate rule, the ignore null capability isn't supported, and rule success is determined by the aggregate statistic being within the defined range.
Uniqueness Expectation rule template: This template calculates passedCount differently than the built-in UniquenessExpectation rule. The rule template returns all rows for which duplicate values or null rows exist, which can result in fewer passing rows if duplicates are present.

For example, if a column contains the values (a, a, b, b, c, d, e):
- Built-in uniqueness rule: Returns 5 passing rows: (a, b, c, d, e).
- Uniqueness rule template: Returns 4 failing rows: (a, a, b, b). The number of passing rows is 3 (7 total rows minus 4 failed rows): (c, d, e).

Metadata aspects

This section describes the fields and values for the data-rules and data-quality-rule-template aspect types.

`data-rules` aspect fields

To define data rules, use the dataplex-types.global.data-rules aspect. The following table describes the fields for this aspect.

Field	Type	Description
`rules`	Array	Required. A list of data quality rules.
`rules[].name`	String	Required. A name for the rule.
`rules[].dimension`	String	Optional. The data quality dimension for the rule.
`rules[].description`	String	Optional. The description of the rule.
`rules[].suspended`	Boolean	Optional. Whether the rule is active or suspended. Default is `false`.
`rules[].threshold`	Double	Optional. The passing threshold for the rule, from `0.0` to `1.0`. Default is `1.0`.
`rules[].type`	Enum	Required. The type of the rule. The only supported value is `TEMPLATE_REFERENCE`.
`rules[].ignore_null`	Boolean	Optional. If `true`, rows with null values in the column are ignored when determining the success criteria.
`rules[].attributes`	Map	Optional. Custom key-value pairs associated with the rule.
`rules[].templateReference`	Object	Required. A reference to the rule template.
`rules[].templateReference.name`	String	Required. The resource name of the rule template.
`rules[].templateReference.values`	Map	Optional. The parameter names and values for the rule template.
`rules[].templateReference.values[].parameterValue.value`	String	Required. The value for the parameter.

The following example shows a data-rules aspect in a payload.json file:

{
  "aspects": {
    "dataplex-types.global.data-rules": {
      "data": {
        "rules": [
          {
            "name": "valid-email",
            "dimension": "VALIDITY",
            "type": "TEMPLATE_REFERENCE",
            "templateReference": {
              "name": "projects/my-project/locations/us-central1/entryGroups/my-rules/entries/email-check",
              "values": {
                "column_name": {
                    "value": "email"
                }
              }
            }
          }
        ]
      }
    }
  }
}

`data-quality-rule-template` aspect fields

Use the data-quality-rule-template aspect to define a custom data quality rule template. The following table describes the fields for the dataplex-types.global.data-quality-rule-template aspect.

Field	Type	Description
`dimension`	String	Required. The dimension for the rule template.
`sqlCollection`	Array	Required. A list of SQL queries for the rule template.
`sqlCollection[].sql.query`	String	Required. The SQL query that returns invalid rows.
`inputParameters`	Map	Optional. A map of input parameters for the rule template.
`inputParameters[].parameterDescription.description`	String	Optional. The description of the input parameter.
`inputParameters[].parameterDescription.defaultValue`	String	Optional. The default value for the parameter if no value is provided.
`capabilities`	Array	Optional. A list of template capabilities, such as `THRESHOLD` or `IGNORE_NULL`.

The following example displays the structure of a data-quality-rule-template aspect:

{
  "entryType": "projects/dataplex-types/locations/global/entryTypes/data-quality-rule-template",
  "aspects": {
    "dataplex-types.global.data-quality-rule-template": {
      "data": {
        "dimension": "COMPLETENESS",
        "sqlCollection": [
          {
              "query": "SELECT * FROM ${data()} WHERE ${column()} > ${param(p1)}"
          }
        ],
        "inputParameters": {
          "p1": {
              "description": "The parameter description"
          }
        },
        "capabilities": [
          "THRESHOLD",
          "IGNORE_NULL"
        ]
      }
    }
  }
}

Manage data quality rule templates

This section describes how to create, edit, and delete rule templates.

Create a rule library

To create a rule library, you must create a Knowledge Catalog entry group.

Console

In the Google Cloud console, go to the Data profiling & quality page.

Go to Data profiling & quality
Go to Rule libraries > Custom, and click Create.
In the Create rule library window, fill in the following fields:
1. Optional: Enter a display name.
2. In Rule library ID, enter an ID. For more information, see the resource naming conventions.
3. Optional: Enter a description.
4. In the Location menu, select a location. It can't be changed later.
5. Optional: Add labels. Labels are key-value pairs that let you group related objects together or with other Google Cloud resources.
6. Click Save.

REST

To create a rule library by using the API, you must create an entry group with the required label goog-dataplex-entry-group-type: rule_library:

gcurl -X POST "https://${DATAPLEX_API}/entryGroups?entryGroup_id=RULE_LIBRARY_ID" \
--data @- << EOF
{
"labels": {
  "goog-dataplex-entry-group-type": "rule_library"
},
"description": "DESCRIPTION"
}
EOF

Replace the following:

RULE_LIBRARY_ID: a unique ID for your rule library.
DESCRIPTION: an optional description for the rule library.

Terraform

To create a rule library, use the google_dataplex_entry_group resource:

resource "google_dataplex_entry_group" "rule_library" {
project        = "PROJECT_ID"
location       = "LOCATION"
entry_group_id = "RULE_LIBRARY_ID"
description    = "DESCRIPTION"

labels = {
"goog-dataplex-entry-group-type" = "rule_library"
}
}

Replace the following:

PROJECT_ID: your project ID.
LOCATION: the location for your rule library (for example, us-central1).
RULE_LIBRARY_ID: a unique ID for your rule library.
DESCRIPTION: an optional description for the rule library.

Create a rule template

To create a custom rule template, select one of the following:

Console

In the Google Cloud console, go to the Data profiling & quality page.

Go to Data profiling & quality
Go to Rule libraries > Custom.
Click the rule library where you want to add a template, and then click Create.
In the Create rule template window, fill in the following fields:
1. Optional: Enter a name for the template.
2. In Template ID, enter an ID. For more information, see the resource naming conventions.
3. Optional: Enter a description.
4. In the Dimension menu, select a dimension. For more information, see Dimensions.
5. In the SQL query field, enter the following example query that validates each column value is between two values:
```
SELECT * FROM ${data()} WHERE NOT(${column()}>=${param(min_value)} AND ${column()}<=${param(max_value)}) IS TRUE
```
6. Optional: To enable the rule referencing this template to specify a threshold for success criteria, select Support threshold.
7. Optional: To allow rules referencing this template to ignore null values in the column for determining success criteria, select Support ignore null.
8. In Input Parameters, click Add input parameter, and then for each parameter used in the SQL query, enter an input name, description, and default value. In the preceding example, the names would be min_value and max_value.
9. Click Save.

REST

To create a custom rule template, create an entry of type data-quality-rule-template:

gcurl -X POST "https://${DATAPLEX_API}/entryGroups/ENTRY_GROUP_ID/entries?entry_id=TEMPLATE_ID" \
--data @- << EOF
{
"entryType": "projects/dataplex-types/locations/global/entryTypes/data-quality-rule-template",
"entrySource": {
  "displayName": "DISPLAY_NAME",
  "description": "DESCRIPTION"
},
"aspects": {
  "dataplex-types.global.data-quality-rule-template": {
     "data": {
       "dimension": "VALIDITY",
       "sqlCollection": [
         {
           "query": "SELECT t.* FROM ${data()} AS t LEFT JOIN `${param(reference_table)}` AS s ON t.${column()} = s.`${param(reference_column)}` WHERE s.`${param(reference_column)}` IS NULL"
         }
       ],
       "inputParameters": {
         "PARAMETER_NAME": { "description": "PARAMETER_DESCRIPTION" }
       },
       "capabilities": ["THRESHOLD"]
     }
  }
}
}
EOF

Replace the following:

ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: a unique ID for your rule template.
DISPLAY_NAME: a display name for the rule template.
DESCRIPTION: a description of the rule template.
PARAMETER_NAME: the name of an input parameter used in the SQL query.
PARAMETER_DESCRIPTION: a description of the input parameter.

Terraform

To create a custom rule template, use the google_dataplex_entry resource:

resource "google_dataplex_entry" "rule_template" {
project        = "PROJECT_ID"
location       = "LOCATION"
entry_id       = "TEMPLATE_ID"
entry_group_id = "ENTRY_GROUP_ID"

entry_type = "projects/dataplex-types/locations/global/entryTypes/data-quality-rule-template"

entry_source {
display_name = "DISPLAY_NAME"
description  = "DESCRIPTION"
}

aspects {
aspect_key = "dataplex-types.global.data-quality-rule-template"
aspect {
  data = jsonencode({
    dimension = "VALIDITY"
    sqlCollection = [
      {
        query = "SELECT t.* FROM $${data()} AS t LEFT JOIN `$${param(reference_table)}` AS s ON t.$${column()} = s.`$${param(reference_column)}` WHERE s.`$${param(reference_column)}` IS NULL"
      }
    ]
    inputParameters = {
      "PARAMETER_NAME" = { description = "PARAMETER_DESCRIPTION" }
    }
    capabilities = ["THRESHOLD"]
  })
}
}
}

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your rule template (for example, us-central1).
ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: a unique ID for your rule template.
DISPLAY_NAME: a display name for the rule template.
DESCRIPTION: a description of the rule template.
PARAMETER_NAME: the name of an input parameter used in the SQL query.
PARAMETER_DESCRIPTION: a description of the input parameter.


Update a rule template

To update an existing rule template, select one of the following options:

Console

In the Google Cloud console, go to the Data profiling & quality page.



Go to Data profiling & quality

Go to Rule libraries > Custom.
Click the rule library that contains the template you want to update.
In the Rule templates list, click the template that you want to update.
On the rule template details page, click
editEdit.
Update the fields, and then click Save.

REST
To update a custom rule template, patch the entry or specific aspect:

gcurl -X PATCH "https://${DATAPLEX_API}/entryGroups/ENTRY_GROUP_ID/entries/TEMPLATE_ID?updateMask=aspects" \
--data @- << EOF
{
 "aspects": {
   "dataplex-types.global.data-quality-rule-template": {
     "data": {
       "dimension": "VALIDITY",
       "sqlCollection": [
         {
           "query": "SELECT * FROM ${data()} WHERE ${column()} IS NOT NULL"
         }
       ]
     }
   }
 }
}
EOF

Replace the following:


ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the rule template that you want to update.

Terraform
To update a custom rule template, use the
google_dataplex_entry
resource:

resource "google_dataplex_entry" "rule_template" {
project        = "PROJECT_ID"
location       = "LOCATION"
entry_id       = "TEMPLATE_ID"
entry_group_id = "ENTRY_GROUP_ID"

entry_type = "projects/dataplex-types/locations/global/entryTypes/data-quality-rule-template"

aspects {
aspect_key = "dataplex-types.global.data-quality-rule-template"
aspect {
  data = jsonencode({
    dimension = "VALIDITY"
    sqlCollection = [
      {
        query = "SELECT * FROM $${data()} WHERE $${column()} IS NOT NULL"
      }
    ]
  })
}
}
}

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your rule template (for example, us-central1).
ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the rule template that you want to update.


Delete a rule template

To delete an existing rule template, select one of the following options:

Console

In the Google Cloud console, go to the Data profiling & quality page.



Go to Data profiling & quality

Go to Rule libraries > Custom.
Click the rule library that contains the template you want to delete.
In the Rule templates list, click the template that you want to delete.
Click Delete, and then click Delete again to confirm.

REST
To delete a custom rule template, delete the entry:

gcurl -X DELETE \
"https://${DATAPLEX_API}/entryGroups/ENTRY_GROUP_ID/entries/TEMPLATE_ID"

Replace the following:


ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the rule template that you want to delete.


Create a data quality scan using template rules

Use your custom templates to define rules for a data quality scan.

Console

In the Google Cloud console, go to the Data profiling & quality page.



Go to Data profiling & quality

Follow the steps to create a data quality scan, but update the following:


In the Define scan window, in the Credential type menu, select Service account, and then enter a service account. A service account is mandatory for using rule templates.
In the Data quality rules window, define the rules to configure for this data quality scan:

Click Add rules > Template rules.
You can either select Attach rule to entire table, or in
Choose columns, browse and select the columns
to apply rules for.
In Choose rule templates, select the rule templates to use.
Only the rule templates in the same location as the scan or in a
global location can be used.
Alternatively, you can also select
system rule templates from the list.
Click Ok.
Click editEdit rule, and then add rule specific parameters.
Click Save.
Select the rules that you want to add, and then click Select. The rules are now added to your current rules list.
Optional: Repeat the previous steps to add additional rules to the data quality scan.
Click Continue.

Proceed with the remaining scan configuration.
Click Create to only create the scan, or click Run scan to create and immediately run the scan.


REST
To create a data quality scan that references a rule template, specify the
templateReference. Custom rule templates use project-specific paths, while
system rule templates use a global path:
projects/dataplex-templates/locations/global/entryGroups/rule-library/entries/<var>SYSTEM_TEMPLATE_ID</var>.

The following example creates a scan that uses a custom rule template and
includes a filter to selectively run rules:

gcurl -X POST "https://${DATAPLEX_API}/dataScans?data_scan_id=DATASCAN_ID" \
--data @- << EOF
{
"data": {
  "resource": "//bigquery.googleapis.com/projects/BIGQUERY_PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID"
},
"executionIdentity": {
  "serviceAccount": { "email": "SERVICE_ACCOUNT_EMAIL" }
},
"executionSpec": { "trigger": { "onDemand": {} } },
"type": "DATA_QUALITY",
"dataQualitySpec": {
  "rules": [
    {
      "templateReference": {
        "name": "projects/PROJECT_ID/locations/LOCATION/entryGroups/ENTRY_GROUP_ID/entries/TEMPLATE_ID",
        "values": { "PARAMETER_NAME": { "value" : "PARAMETER_VALUE" } }
      },
      "column": "COLUMN_NAME",
      "name": "RULE_NAME"
    }
  ],
  "filter": "FILTER_CONDITION"
}
}
EOF

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your rule template and scan (for example, us-central1).
DATASCAN_ID: the ID of the data quality scan.
BIGQUERY_PROJECT_ID: the project ID of the BigQuery table.
DATASET_ID: the BigQuery dataset ID.
TABLE_ID: the BigQuery table ID.
SERVICE_ACCOUNT_EMAIL: the email ID of the service account to run the scan.
ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the custom rule template.
SYSTEM_TEMPLATE_ID: the ID of the system rule template (for example, non_null_expectation).
PARAMETER_NAME: the name of an input parameter for the rule template.
PARAMETER_VALUE: the value for the input parameter.
COLUMN_NAME: the column to apply the rule to.
RULE_NAME: a name for the rule instance.
FILTER_CONDITION: an optional AIP-160 filter string to selectively run rules (for example, name = \"RULE_NAME\").

Terraform
To create a data quality scan that references a rule template, use the
google_dataplex_datascan
resource:

resource "google_dataplex_datascan" "scan" {
data_scan_id = "DATASCAN_ID"
location     = "LOCATION"
project      = "PROJECT_ID"

data {
resource = "//bigquery.googleapis.com/projects/BIGQUERY_PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID"
}

execution_spec {
service_account = "SERVICE_ACCOUNT_EMAIL"
trigger {
  on_demand {}
}
}

data_quality_spec {
rules {
  column    = "COLUMN_NAME"
  name      = "RULE_NAME"
  dimension = "VALIDITY"

  template_reference {
    name = "projects/PROJECT_ID/locations/LOCATION/entryGroups/ENTRY_GROUP_ID/entries/TEMPLATE_ID"
    values = {
      "PARAMETER_NAME" = { value = "PARAMETER_VALUE" }
    }
  }
}
filter = "FILTER_CONDITION"
}
}

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your rule template and scan (for example, us-central1).
DATASCAN_ID: the ID of the data quality scan.
BIGQUERY_PROJECT_ID: the project ID of the BigQuery table.
DATASET_ID: the BigQuery dataset ID.
TABLE_ID: the BigQuery table ID.
SERVICE_ACCOUNT_EMAIL: the email ID of the service account to run the scan.
ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the custom rule template.
PARAMETER_NAME: the name of an input parameter for the rule template.
PARAMETER_VALUE: the value for the input parameter.
COLUMN_NAME: the column to apply the rule to.
RULE_NAME: a name for the rule instance.
FILTER_CONDITION: an optional AIP-160 filter string to selectively run rules.


Run and monitor data quality scans

After you create a data quality scan, you must run it to validate your data.
For more information, see Run a data quality scan.

You can then monitor the scan jobs and view the results. For more information,
see View the data quality scan results.

Attach data quality rules to catalog entries

You can declare data quality rules as aspects in
Knowledge Catalog to make them searchable and reusable across
scans.

BigQuery table

To define rules directly on a BigQuery table entry, select
one of the following:

Console

In the Google Cloud console, go to the Knowledge Catalog Search page.



Go to Search

Search for and select the table that you want to attach rules to.
Click Data quality > Rules management > Create rules.
In the Create rules window, do the following:


In the Choose create option menu, select Create new rule.
In Choose columns, click Browse. Select the columns to
apply rules for.
In the Choose rule types menu, select the rule templates to use. Only
the rule templates in the same location as the scan can be used.
Click editEdit rule, and then add rule specific parameters.
Click Save.

The Rules management page displays all entry rules.


REST
To attach rules to a specific column using the API, patch the @bigquery
entry with a data-rules aspect targeted to that column:

gcurl -X PATCH "https://${DATAPLEX_API}/entryGroups/@bigquery/entries/projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/tables/TABLE_ID?updateMask=aspects&aspect_keys=projects/dataplex-types/locations/global/aspectTypes/data-rules@Schema.COLUMN_NAME" \
--data @- << EOF
{
"aspects": {
  "dataplex-types.global.data-rules@Schema.COLUMN_NAME": {
    "aspectType": "projects/dataplex-types/locations/global/aspectTypes/data-rules",
    "data": {
      "rules": [
        {
          "templateReference": "projects/PROJECT_ID/locations/LOCATION/entryGroups/ENTRY_GROUP_ID/entries/TEMPLATE_ID",
          "column": "COLUMN_NAME",
            "values": { "PARAMETER_NAME": { "value" : "PARAMETER_VALUE" } }
          }
      ]
    }
  }
}
}
EOF

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your rule template and aspect.
DATASET_ID: the BigQuery dataset ID.
TABLE_ID: the BigQuery table ID.
COLUMN_NAME: the column to apply the rule to.
ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the rule template.
PARAMETER_NAME: the name of an input parameter for the rule template.
PARAMETER_VALUE: the value for the input parameter.

Terraform
To attach rules to a specific column, use the
google_dataplex_entry
resource:

resource "google_dataplex_entry" "bq_table_metadata" {
  project        = "PROJECT_ID"
  location       = "LOCATION"
  entry_id       = "bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID"
  entry_group_id = "@bigquery"

  aspects {
    aspect_key = "dataplex-types.global.data-rules@Schema.COLUMN_NAME"
    aspect {
      data = jsonencode({
        rules = [
          {
            name      = "RULE_NAME"
            dimension = "VALIDITY"
            templateReference = "projects/PROJECT_ID/locations/LOCATION/entryGroups/ENTRY_GROUP_ID/entries/TEMPLATE_ID"
               values = {
                 "PARAMETER_NAME" = { value = "PARAMETER_VALUE" }
               }
            }
        ]
      })
    }
  }
}

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your rule template and aspect.
DATASET_ID: the BigQuery dataset ID.
TABLE_ID: the BigQuery table ID.
COLUMN_NAME: the column to apply the rule to.
ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the rule template.
PARAMETER_NAME: the name of an input parameter for the rule template.
PARAMETER_VALUE: the value for the input parameter.
RULE_NAME: a unique name for the rule.


Business glossary terms

You can attach rules to business glossary terms. Rules attached to terms are
automatically inherited by linked BigQuery tables.

Console

In the Google Cloud console, go to the Knowledge Catalog Glossaries page.



Go to Glossaries

Search for and select the business glossary term.
In the Data quality rules section, click add Add.
In the Create rules window, do the following:


In the Choose create option menu, select Create new rule.
In the Choose rule types menu, select the rule templates to
use. Only the rule templates in the same location as the scan can be used.
Click editEdit rule,
and then add rule specific parameters.
Click Save.

Attach the term to a BigQuery table or columns. For
more information, see Manage links between terms and data assets.

REST
To attach rules to a term using the API, patch the @dataplex entry for the
glossary term:

gcurl -X PATCH "https://${DATAPLEX_API}/entryGroups/@dataplex/entries/projects/PROJECT_ID/locations/LOCATION/glossaries/GLOSSARY_ID/terms/TERM_ID?updateMask=aspects&aspect_keys=projects/dataplex-types/locations/global/aspectTypes/data-rules" \
--data @- << EOF
{
"aspects": {
  "dataplex-types.global.data-rules": {
    "aspectType": "projects/dataplex-types/locations/global/aspectTypes/data-rules",
    "data": {
      "rules": [
        {
          "templateReference": "projects/PROJECT_ID/locations/LOCATION/entryGroups/ENTRY_GROUP_ID/entries/TEMPLATE_ID",
          "column": "COLUMN_NAME",
            "values": { "PARAMETER_NAME": { "value" : "PARAMETER_VALUE" } }
          }
      ]
    }
  }
}
}
EOF

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your rule template and aspect.
GLOSSARY_ID: the ID of the business glossary.
TERM_ID: the ID of the glossary term.
ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the rule template.
COLUMN_NAME: the column to apply the rule to.
PARAMETER_NAME: the name of an input parameter for the rule template.
PARAMETER_VALUE: the value for the input parameter.

Terraform
To attach rules to a business glossary term, use the
google_dataplex_entry
resource:

resource "google_dataplex_entry" "glossary_term_rules" {
  project        = "PROJECT_ID"
  location       = "LOCATION"
  entry_id       = "projects/PROJECT_ID/locations/LOCATION/glossaries/GLOSSARY_ID/terms/TERM_ID"
  entry_group_id = "@dataplex"

  aspects {
    aspect_key = "dataplex-types.global.data-rules"
    aspect {
      data = jsonencode({
        rules = [
          {
            name      = "RULE_NAME"
            dimension = "VALIDITY"
            templateReference = "projects/PROJECT_ID/locations/LOCATION/entryGroups/ENTRY_GROUP_ID/entries/TEMPLATE_ID"
              values = {
                "PARAMETER_NAME" = { value = "PARAMETER_VALUE" }
              }
            }
        ]
      })
    }
  }
}

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your rule template and aspect.
GLOSSARY_ID: the ID of the business glossary.
TERM_ID: the ID of the glossary term.
ENTRY_GROUP_ID: the ID of the entry group that stores your rule template.
TEMPLATE_ID: the ID of the rule template.
PARAMETER_NAME: the name of an input parameter for the rule template.
PARAMETER_VALUE: the value for the input parameter.
RULE_NAME: a unique name for the rule.


Import rules from another table

You can import data quality rules from an existing BigQuery table
entry to your current table.

Console

In the Google Cloud console, go to the Knowledge Catalog Search page.



Go to Search

Select the table you want to manage rules for.
Click Data quality > Rules management.
Click Create rules.
In the Create rules window, do the following:


In the Choose create option menu, select Import rules from another table.
In Table, click Browse. Search for and select the
source table containing the rules that you want to copy.
Select the rules. You can also edit the rules.
Click Save.

The Rules management tab displays the new rules.


REST
To import rules, you must fetch the data-rules aspect from the source entry
and apply it to the target entry.


Get the data-rules aspect from the source entry:

gcurl "https://dataplex.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/entryGroups/ENTRY_GROUP_ID/entries/SOURCE_ENTRY_ID?view=FULL"
Extract the rules list from the dataplex-types.global.data-rules aspect.
Attach the rules to a target entry.

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location of the source entry.
ENTRY_GROUP_ID: the ID of the entry group for the source entry.
SOURCE_ENTRY_ID: the ID of the source entry.



View data quality rules for BigQuery table

You can view all rules applicable to a table, including rules
attached directly and rules inherited from linked glossary terms.

Console

In the Google Cloud console, go to the Knowledge Catalog Search page.



Go to Search

Search for and select the table.
Click Data quality > Rules management to view all rules.


Create a data quality scan using rules from catalog

You can selectively run rules declared on catalog entries in a scan.

Console

In the Google Cloud console, go to the Data profiling & quality page.



Go to Data profiling & quality

Follow the steps to create a data quality scan, but update the following:


In the Define scan window, do the following:

In the Credential type menu, select Service account, and then enter a service account. A service account is mandatory for using rule templates.
For Rule type, select Create with entry based rule.

In the Data quality rules section, rules
applicable to the table entry are displayed, including rules inherited
from linked glossary terms. To filter the rules, do the following:

In the Filter items field, filter items to selectively run rules.
Click Apply. Filtered rules are displayed.

Proceed with the remaining scan configuration.
Click Create to only create the scan, or click Run scan to
create and immediately run the scan.


Subsequent runs evaluate rules attached to the entry or inherited from glossary terms as observed at the time of execution.

REST
To run rules from catalog entries, set enableCatalogBasedRules to true.
You can also specify a filter.

To create the scan, use the following code:

gcurl -X POST "https://${DATAPLEX_API}/dataScans?data_scan_id=DATASCAN_ID" \
--data @- << EOF
{
"type": "DATA_QUALITY",
"data": {
  "resource": "//bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID"
},
"executionIdentity": {
  "serviceAccount": { "email": "SERVICE_ACCOUNT_EMAIL" }
},
"executionSpec": { "trigger": { "onDemand": {} } },
"dataQualitySpec": {
  "enableCatalogBasedRules": true,
  "filter": "FILTER_CONDITION"
}
}
EOF

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your data scan.
DATASCAN_ID: the ID of the data quality scan.
DATASET_ID: the BigQuery dataset ID.
TABLE_ID: the BigQuery table ID.
SERVICE_ACCOUNT_EMAIL: the email ID of the service account to run the scan.
FILTER_CONDITION: an AIP-160 filter string to selectively run rules (for example, attributes.environment = \"prod\").

Terraform
To run rules from catalog entries, use the
google_dataplex_datascan
resource:

resource "google_dataplex_datascan" "scan" {
data_scan_id = "DATASCAN_ID"
location     = "LOCATION"
project      = "PROJECT_ID"

data {
resource = "//bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID"
}

execution_spec {
service_account = "SERVICE_ACCOUNT_EMAIL"
trigger {
  on_demand {}
}
}

data_quality_spec {
enable_catalog_based_rules = true
filter = "FILTER_CONDITION"
}
}

Replace the following:


PROJECT_ID: your project ID.
LOCATION: the location for your data scan.
DATASCAN_ID: the ID of the data quality scan.
DATASET_ID: the BigQuery dataset ID.
TABLE_ID: the BigQuery table ID.
SERVICE_ACCOUNT_EMAIL: the email ID of the service account to run the scan.
FILTER_CONDITION: an AIP-160 filter string to selectively run rules.


Pricing

Using Knowledge Catalog rule reusability involves the following pricing
elements:


BigQuery charges: BigQuery charges for the job that runs in the scan
project. For more information, see BigQuery pricing.
Knowledge Catalog data quality scan: There's no charge for
processing since BigQuery charges for the job.
Metadata storage: data-rules aspect and data-quality-rule-template
aspect storage is charged as metadata storage. For more information, see
Knowledge Catalog pricing.


What's next


Learn more about auto data quality overview.
Learn how to use auto data quality scans.
View a complete list of system rule templates.
Learn about metadata management.

データ品質ルールを再利用する コレクションでコンテンツを整理 必要に応じて、コンテンツの保存と分類を行います。

ユースケース

始める前に

Dataplex API 環境を設定する

サービス アカウントを設定する

必要なロールと権限

ルール テンプレートの SQL クエリ構文

System-supported parameters

Example 1: Validate column values are between two values

Example 2: Foreign key validation

System rule templates

Console

REST

Known differences between system rule templates and built-in rules

Metadata aspects

data-rules aspect fields

data-quality-rule-template aspect fields

Manage data quality rule templates

Create a rule library

Console

REST

Terraform

Create a rule template

Console

REST

Terraform

Update a rule template

Console

REST

Terraform

Delete a rule template

Console

REST

Create a data quality scan using template rules

Console

REST

Terraform

Run and monitor data quality scans

Attach data quality rules to catalog entries

BigQuery table

Console

REST

Terraform

Business glossary terms

Console

REST

Terraform

Import rules from another table

Console

REST

View data quality rules for BigQuery table

Console

Create a data quality scan using rules from catalog

Console

REST

Terraform

Pricing

What's next

データ品質ルールを再利用する

サービスアカウントを設定する

ルールテンプレートの SQL クエリ構文

`data-rules` aspect fields

`data-quality-rule-template` aspect fields