Regeln für Datenqualität wiederverwenden

In diesem Dokument wird beschrieben, wie Sie Datenqualitätsregeln aus Knowledge Catalog (ehemals Dataplex Universal Catalog) wiederverwenden, um standardisierte Geschäftsregeln zu definieren und zu verwalten.

Durch die Wiederverwendbarkeit von Regeln können Sie komplexe oder standardisierte Geschäftsregeldefinitionen mithilfe von Regelvorlagen für mehrere Datenqualitätsregeln und ‑scans freigeben. In diesem Dokument wird auch beschrieben, wie Sie wiederverwendbare Regelvorlagen einrichten, erstellen und verwalten und wie Sie Datenqualitätsregeln als Metadatenaspekte an Katalogeinträge anhängen.

Anwendungsfälle

Sie können die Wiederverwendbarkeit von Datenqualitätsregeln in den folgenden Szenarien verwenden:

• Regeldefinitionen standardisieren und freigeben: Verwenden Sie benutzerdefinierte Regelvorlagen , um komplexe oder standardisierte Geschäftsregeldefinitionen zu speichern. Dadurch wird der Zeit- und Arbeitsaufwand für die Verteilung allgemeiner Definitionen durch die Verwendung von SQL-Ausdrücken in Vorlagen reduziert. Beispielsweise kann ein zentrales Data Governance-Team eine Standardvorlage für gültige E-Mail-Adressen oder gültige Sozialversicherungsnummern definieren, die in der gesamten Organisation wiederverwendet wird. So wird die Konsistenz gewährleistet und der Betriebsaufwand für die Verwaltung doppelter Regeln reduziert.
• Governance-gesteuerte Qualität implementieren: Deklarieren Sie Datenregeln als Metadaten , indem Sie Knowledge Catalog-Aspekte für BigQuery-Tabelle- und Unternehmensglossarbegriffseinträge verwenden. Dadurch werden Ihre Regeln durchsuchbar und wiederverwendbar. Wenn Sie beispielsweise eine Spalte mit einem Glossarbegriff verknüpfen, kann sie automatisch die für diesen Begriff definierten Validierungsregeln übernehmen. So können Sie automatisierte Governance-Richtlinien durch semantische Metadatenvererbung implementieren.
• Wiederverwendbare Regeln suchen und finden: Suchen Sie über die semantische Suche nach vorhandenen Regeln in Ihrer Organisation. So können Datenanalysten und ‑techniker geprüfte, standardisierte Regelsätze (z. B. „Finanzielle Baseline-Konstanten“) finden und die Datenqualität für neue Projekte verbessern, ohne SQL von Grund auf neu schreiben zu müssen.
• Cold-Start-Probleme vermeiden: Nutzen Sie Systemregelvorlagen für häufig verwendete Auswertungen wie Null-Prüfungen oder Bereichserwartungen. Mit diesen integrierten Vorlagen können Sie die Datenqualitätsüberwachung für häufige Szenarien schnell einrichten, ohne benutzerdefiniertes SQL schreiben zu müssen.
• Trennung von Zuständigkeiten ermöglichen: Ermöglichen Sie zentralen Governance-Teams, geprüfte Regelvorlagen zu erstellen, während sich Engineering-Teams darauf konzentrieren, diese Regeln auf ihre Daten-Assets anzuwenden, ohne komplexes SQL schreiben oder verwalten zu müssen. Diese klare Aufteilung der Verantwortlichkeiten verbessert die Agilität der Organisation und sorgt dafür, dass Datenqualitätsstandards im gesamten Unternehmen einheitlich angewendet werden.

Hinweis

1. Dataplex API aktivieren

   Rollen, die zum Aktivieren von APIs erforderlich sind

   Zum Aktivieren von APIs benötigen Sie die IAM-Rolle „Service Usage-Administrator“ (roles/serviceusage.serviceUsageAdmin), die die Berechtigung serviceusage.services.enable enthält. Weitere Informationen zum Zuweisen von Rollen.

   API aktivieren

Bevor Sie die Wiederverwendbarkeit von Datenqualitätsregeln verwenden, müssen Sie die folgenden Anforderungen erfüllen.

Dataplex API-Umgebung einrichten

Wenn Sie die REST API-Beispiele in diesem Dokument verwenden möchten, richten Sie einen Alias für gcurl ein und konfigurieren Sie die Umgebungsvariable ${DATAPLEX_API}.

1. Legen Sie einen Alias für gcurl fest. Dadurch wird eine Verknüpfung erstellt, die Ihr Authentifizierungstoken enthält und den JSON-Content-Type für API-Anfragen festlegt:

   alias gcurl='curl -H "Authorization: Bearer $(gcloud auth print-access-token)" -H "Content-Type: application/json"'
   

2. Legen Sie die Variable DATAPLEX_API fest:

   DATAPLEX_API="dataplex.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION"

   Ersetzen Sie Folgendes:

   • PROJECT_ID: Ihre Projekt-ID.
   • LOCATION: der Standort, an dem sich Ihr Scan oder Ihre Ressourcen befinden (z. B. us-central1).

Dienstkonto einrichten

Ein Dienstkonto ist für die Ausführung von Datenqualitätsscans mit wiederverwendbaren Regeln erforderlich. Erstellen Sie ein Dienstkonto mit den folgenden Identity and Access Management-Rollen und ‑Berechtigungen:

• Sie benötigen die Berechtigung iam.serviceAccounts.actAs für das Projekt, in dem das Dienstkonto gehostet wird (in der Regel über die Rolle roles/iam.serviceAccountUser).
• Gewähren Sie dem Dataplex-Dienst-Agent ([e-mail]) für das Scanprojekt die Berechtigung iam.serviceAccounts.getAccessToken für das Dienstkonto (z. B. über die Rolle roles/iam.serviceAccountTokenCreator).
• Das Dienstkonto muss die folgenden Berechtigungen haben:
  • bigquery.tables.getData für die zu scannende Tabelle (z. B. über roles/bigquery.dataViewer).
  • bigquery.jobs.insert im Scanprojekt (z. B. über roles/bigquery.jobUser).
  • roles/bigquery.dataEditor für das Export-Dataset (falls Sie den Export verwenden).

Erforderliche Rollen und Berechtigungen

Prüfen Sie, ob Sie die folgenden IAM-Rollen für Ihre jeweiligen Aufgaben haben:

• Datenqualitäts-Scanverwaltung: Erforderliche Rollen für Datenqualitätsscans zum Verwalten von Ressourcen für Datenqualitätsscans.
• Regelvorlagenverwaltung: Zum Erstellen oder Aktualisieren von Regelvorlagen müssen Sie die erforderlichen Berechtigungen zum Verwalten von Einträgen in der Eintragsgruppe oder im Projekt der Regelvorlage haben. Insbesondere gewähren roles/dataplex.catalogEditor oder roles/dataplex.entryOwner diese Berechtigungen.
• Regelvorlagen aus Regeln referenzieren: Sie benötigen die Berechtigungen dataplex.entries.get und dataplex.entries.getData für die Eintragsgruppe oder das Projekt der Regelvorlage, auf die von einer Regel verwiesen wird.
• Datenqualitätsregeln an BigQuery-Tabellen anhängen: Wenn Sie Daten qualitätsregeln als Knowledge Catalog-Metadaten anhängen möchten, benötigen Sie eine der folgenden Berechtigungen:
  • bigquery.tables.update oder roles/bigquery.dataEditor für die Tabelle und dataplex.entryGroups.useDataRulesAspect für die Eintragsgruppe @bigquery am Tabellenstandort.
  • roles/dataplex.catalogEditor für die Eintragsgruppe @bigquery.
• Datenqualitätsregeln an Unternehmensglossarbegriffe anhängen: Wenn Sie Datenqualitätsregeln als Knowledge Catalog-Metadaten anhängen möchten, benötigen Sie eine der folgenden Berechtigungen:
  • dataplex.glossaryTerms.update für den Begriff und dataplex.entryGroups.useDataRulesAspect für die Eintragsgruppe @dataplex.
  • roles/dataplex.catalogEditor für die Eintragsgruppe @dataplex.
• Datenqualitätsscans mit eintragbasierten Regeln erstellen: Sie benötigen eine der folgenden Berechtigungen:
  • bigquery.tables.get und bigquery.tables.getData für die Tabelle.
  • dataplex.entries.get und dataplex.entries.getData für die @bigquery Eintragsgruppe am Tabellenstandort.

SQL-Abfragesyntax für Regelvorlagen

Wenn Sie die SQL-Logik für eine Regelvorlage schreiben, müssen Sie eine Anweisung angeben, die ungültige Zeilen zurückgibt. Wenn die Abfrage Zeilen zurückgibt, schlägt die Regel fehl. Weitere Informationen finden Sie unter SqlAssertion.

Beachten Sie beim Schreiben von SQL für Regelvorlagen die folgenden Richtlinien:

• Lassen Sie das abschließende Semikolon in der SQL-Anweisung weg.
• Verwenden Sie ${param(name)}, um auf Eingabeparameter zu verweisen, z. B. ${param(min_value)}.
• Verwenden Sie $${...} 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

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

   Go to Data profiling & quality

2. Click Rule libraries > System.

3. 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.

Note: The column that a rule applies to isn't specified as a field in the aspect data. Instead, it's determined by the path where the aspect is attached. For BigQuery table entries, this is the column path. For glossary terms, it's the column mapped through the term's entryLink.

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

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

   Go to Data profiling & quality

2. Go to Rule libraries > Custom, and click Create.

3. 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

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

   Go to Data profiling & quality

2. Go to Rule libraries > Custom.

3. Click the rule library where you want to add a template, and then click Create.

4. 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
Edit.
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 Edit 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 Edit 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/[e-mail]_NAME" \
--data @- << EOF
{
"aspects": {
  "[e-mail]_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 = "[e-mail]_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.
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 Edit 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.


  

  

  
    
  

  

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