Azure > Data Factory > Dataset

Azure > Data Factory > Dataset > Delete

Process events for Azure Data Factory service Dataset resources.

The router receives events from the event handler and creates, updates, or deletes Dataset resources in the CMDB.


Azure > Resource Group

Azure > Data Factory > Dataset > Router

Azure > Data Factory > Factory

Azure > Data Factory > Factory > Delete

Process events for Azure Data Factory service Factory resources.

The router receives events from the event handler and creates, updates, or deletes Factory resources in the CMDB.


Azure > Data Factory > Factory > Router

Update the tags for Azure Data Factory factory.


Azure > Data Factory > Factory > Set Tags

Azure > Data Factory > Pipeline

Azure > Data Factory > Pipeline > Delete

Process events for Azure Data Factory service Pipeline resources.

The router receives events from the event handler and creates, updates, or deletes Pipeline resources in the CMDB.


Azure > Data Factory > Pipeline > Router

Take an action when an Azure Data Factory dataset is not active based on the&#92;n`Azure &gt; Data Factory &gt; Dataset &gt; Active &gt; * policies`.&#92;n&#92;nThe Active control determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated compliance&#92;nenvironment, it&#39;s common to end up with a wide range of alarms that are difficult&#92;nand time consuming to clear. The Active control brings automated, well-defined&#92;ncontrol to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Dataset &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n&#92;n**Note:** In contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

Azure > Data Factory > Dataset > Active > Age

Azure > Data Factory > Dataset > Active > Last Modified

Azure > Data Factory > Dataset > Active

Take an action when an Azure Data Factory dataset is not approved based on `Azure &gt; Data Factory &gt; Dataset &gt; Approved &gt; * policies`.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Azure > Data Factory > Dataset > Approved > Custom

Azure > Data Factory > Dataset > Approved > Usage

Azure > Data Factory > Enabled

Azure > Data Factory > Dataset > Approved

Record and synchronize details for the Azure Data Factory dataset into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is&#92;nresponsible for populating and updating all the attributes for that&#92;nresource type in the Guardrails CMDB.&#92;n&#92;n**Note:** If CMDB is set to Skip for a resource, then it will not be added&#92;nto the CMDB, and no controls that target it will run.&#92;n

Azure > Data Factory > Dataset > CMDB

Discover all Azure Data Factory dataset resources and add them to the CMDB.&#92;n&#92;nThe [Discovery](https://turbot.com/guardrails/docs/concepts/guardrails/discovery) control is responsible for finding resources of a specific type. It periodically searches for new resources and saves them to the CMDB. Once discovered, resources are then responsible for tracking changes to themselves through the [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control.&#92;n

Azure > Data Factory > Dataset > Discovery

Take an action when an Azure Data Factory factory is not active based on the&#92;n`Azure &gt; Data Factory &gt; Factory &gt; Active &gt; * policies`.&#92;n&#92;nThe Active control determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated compliance&#92;nenvironment, it&#39;s common to end up with a wide range of alarms that are difficult&#92;nand time consuming to clear. The Active control brings automated, well-defined&#92;ncontrol to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Factory &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n&#92;n**Note:** In contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

Azure > Data Factory > Factory > Active > Age

Azure > Data Factory > Factory > Active > Last Modified

Azure > Data Factory > Factory > Active

Take an action when an Azure Data Factory factory is not approved based on `Azure &gt; Data Factory &gt; Factory &gt; Approved &gt; * policies`.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Azure > Data Factory > Factory > Approved > Custom

Azure > Data Factory > Factory > Approved > Usage

Azure > Data Factory > Factory > Approved > Regions

Azure > Data Factory > Factory > Approved

Record and synchronize details for the Azure Data Factory factory into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is&#92;nresponsible for populating and updating all the attributes for that&#92;nresource type in the Guardrails CMDB.&#92;n&#92;n**Note:** If CMDB is set to Skip for a resource, then it will not be added&#92;nto the CMDB, and no controls that target it will run.&#92;n&#92;nCMDB controls also use the Regions policy associated with the resource. If&#92;nregion is not in `Azure &gt; Data Factory &gt; Factory &gt; Regions` policy, the CMDB control will delete the&#92;nresource from the CMDB.&#92;n

Azure > Data Factory > Factory > Regions

Azure > Data Factory > Factory > CMDB

Discover all Azure Data Factory factory resources and add them to the CMDB.&#92;n&#92;nThe [Discovery](https://turbot.com/guardrails/docs/concepts/guardrails/discovery) control is responsible for finding resources of a specific type. It periodically searches for new resources and saves them to the CMDB. Once discovered, resources are then responsible for tracking changes to themselves through the [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control.&#92;n&#92;n**Note:** Discovery and CMDB controls also use the Regions policy associated with the resource. If the region is not in `Azure &gt; Data Factory &gt; Factory &gt; Regions` policy, the CMDB control will delete the resource from the CMDB.&#92;n

Azure > Data Factory > Factory > Discovery

Take an action when an Azure Data Factory factory tags is not updated based on the `Azure &gt; Data Factory &gt; Factory &gt; Tags &gt; *` policies.&#92;n&#92;nIf the resource is not updated with the tags defined in `Azure &gt; Data Factory &gt; Factory &gt; Tags &gt; Template`, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nSee [Tags](https://turbot.com/guardrails/docs/concepts/guardrails/tagging) for more information.&#92;n

Azure > Data Factory > Factory > Tags > Template

Azure > Data Factory > Factory > Tags

Take an action when an Azure Data Factory pipeline is not active based on the&#92;n`Azure &gt; Data Factory &gt; Pipeline &gt; Active &gt; * policies`.&#92;n&#92;nThe Active control determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated compliance&#92;nenvironment, it&#39;s common to end up with a wide range of alarms that are difficult&#92;nand time consuming to clear. The Active control brings automated, well-defined&#92;ncontrol to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Pipeline &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n&#92;n**Note:** In contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

Azure > Data Factory > Pipeline > Active > Age

Azure > Data Factory > Pipeline > Active > Last Modified

Azure > Data Factory > Pipeline > Active

Take an action when an Azure Data Factory pipeline is not approved based on `Azure &gt; Data Factory &gt; Pipeline &gt; Approved &gt; * policies`.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Azure > Data Factory > Pipeline > Approved > Custom

Azure > Data Factory > Pipeline > Approved > Usage

Azure > Data Factory > Pipeline > Approved

Record and synchronize details for the Azure Data Factory pipeline into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is&#92;nresponsible for populating and updating all the attributes for that&#92;nresource type in the Guardrails CMDB.&#92;n&#92;n**Note:** If CMDB is set to Skip for a resource, then it will not be added&#92;nto the CMDB, and no controls that target it will run.&#92;n

Azure > Data Factory > Pipeline > CMDB

Discover all Azure Data Factory pipeline resources and add them to the CMDB.&#92;n&#92;nThe [Discovery](https://turbot.com/guardrails/docs/concepts/guardrails/discovery) control is responsible for finding resources of a specific type. It periodically searches for new resources and saves them to the CMDB. Once discovered, resources are then responsible for tracking changes to themselves through the [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control.&#92;n

Azure > Data Factory > Pipeline > Discovery

dataFactory

dataFactoryAka

test0001

dataset

datasetAka

datasetId

datasetName

datasetServiceNow

factory

factoryAka

factoryId

factoryName

factoryServiceNow

Copy huge amount of data in bulk from database using external control table to store source table list with partitions for each table.

When you want to migrate data from your data warehouse like Oracle server, Netezza server, Teradata server or SQL Server to Azure, you have to load huge amount of data from multiple tables in data sources. In most cases, data has to be further partitioned in each table so that you can load rows with multiple threads in parallel from single table.

pipeline

pipelineAka

pipelineId

pipelineName

pipelineServiceNow

azure-datafactory

A calculated policy that Guardrails uses to create a compiled list of ALL&#92;npermissions for Azure Data Factory that is used as input to the control that manages&#92;nthe IAM stack.&#92;n

Azure > Turbot > Permissions > Compiled > Service Permissions

Azure > Subscription

Azure > Turbot > Permissions > Compiled > Service Permissions > @turbot/azure-datafactory

A calculated policy that Guardrails uses to create a compiled list of ALL&#92;npermission levels for Azure Data Factory that is used as input to the&#92;nstack that manages the Guardrails IAM permissions objects.&#92;n

Azure > Turbot > Permissions > Compiled > Levels

Azure > Turbot > Permissions > Compiled > Levels > @turbot/azure-datafactory

A list of Azure regions in which Azure Data Factory resources are approved for use.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and&#92;n&#39;?&#39; wildcard characters.&#92;n&#92;nThis policy is the default value for all Azure Data Factory resources&#39; Approved &gt; Regions policies.&#92;n

Azure > Data Factory

Azure > Data Factory > Approved Regions [Default]

Azure > Data Factory > Permissions > Levels

Configure whether permissions policies are in effect for Azure Data Factory&#92;nThis setting does not affect Subscription level permissions (Azure/Admin, Azure/Owner, etc)&#92;n

Azure > Data Factory > Permissions

Azure > Data Factory > Permissions > Levels > Modifiers

Define the permissions levels that can be used to grant access to Storage an&#92;nAzure Subscription.  Permissions levels defined will appear in the UI to assign&#92;naccess to Guardrails users.&#92;n

A map of Azure API to Guardrails Permission Level used to customize Guardrails&#39;&#92;nstandard permissions. You can add, remove or redefine the mapping of&#92;nAzure API operations to Guardrails permissions levels here.&#92;n&#92;n```&#92;nexample:&#92;n  - &quot;Microsoft.Data Factory/Data Factory/delete&quot;: operator&#92;n  - &quot;Microsoft.Data Factory/Data Factory/write&quot;: admin&#92;n  - &quot;Microsoft.Data Factory/Data Factory/read&quot;: readonly&#92;n```&#92;n

A list of Azure regions in which Azure Data Factory resources are supported for use.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and&#92;n&#39;?&#39; wildcard characters.&#92;n&#92;nThis policy is the default value for all Azure Data Factory resources&#39; Regions policies.&#92;n

Azure > Data Factory > Regions

A template used to generate the keys and values for Azure Data Factory resources.&#92;n&#92;nBy default, all Data Factory resource Tags &gt; Template policies will use this value.&#92;n

Azure > Data Factory > Tags Template [Default]

Determine the action to take when an Azure Data Factory dataset, based on the `Azure &gt; Data Factory &gt; Dataset &gt; Active &gt; *` policies.&#92;n&#92;nThe control determines whether the resource is in active use, and if not,&#92;nhas the ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Dataset &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n&#92;n**Note:** In contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The age after which the Azure Data Factory dataset&#92;nis no longer considered active. If a create time is unavailable, the time Guardrails discovered the resource is used.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Dataset &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n&#92;n**Note** In contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The number of days since the Azure Data Factory dataset was last modified before it is considered&#92;ninactive.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Dataset &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n&#92;n**Note** In contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n

Determine the action to take when an Azure Data Factory dataset is not approved based on `Azure &gt; Data Factory &gt; Dataset &gt; Approved &gt; *` policies.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Determine whether the Azure Data Factory dataset is allowed to exist.&#92;nThis policy will be evaluated by the Approved control. If an Azure Data Factory dataset is not approved, it will be subject to the action specified in the `Azure &gt; Data Factory &gt; Dataset &gt; Approved` policy.&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n&#92;n**Note**: The policy value must be a string with a value of `Approved`, `Not approved` or `Skip`, or in the form of YAML objects. The object(s) must contain the key `result` with its value as `Approved` or `Not approved`. A custom title and message can also be added using the keys `title` and `message` respectively.&#92;n

string

Determine whether the Azure Data Factory dataset is allowed to exist.&#92;n&#92;nThis policy will be evaluated by the Approved control. If an Azure Data Factory dataset is not approved, it will be subject to the action specified in the `Azure &gt; Data Factory &gt; Dataset &gt; Approved` policy.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Configure whether to record and synchronize details for the Azure Data Factory dataset into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is responsible for populating and updating all the attributes for that resource type in the Guardrails CMDB.&#92;nAll policies and controls in Guardrails are based around the resource, so usually the CMDB policy is set to **&quot;Enforce: Enabled&quot;**.&#92;n&#92;nIf set to Skip then all changes to the CMDB are paused - no new resources will be discovered, no updates will be made and deleted resources will not be removed.&#92;n&#92;nTo cleanup resources and stop tracking changes, set this policy to **&quot;Enforce: Disabled&quot;**.&#92;n

Determine the action to take when an Azure Data Factory factory, based on the `Azure &gt; Data Factory &gt; Factory &gt; Active &gt; *` policies.&#92;n&#92;nThe control determines whether the resource is in active use, and if not,&#92;nhas the ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Factory &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n&#92;n**Note:** In contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The age after which the Azure Data Factory factory&#92;nis no longer considered active. If a create time is unavailable, the time Guardrails discovered the resource is used.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Factory &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n&#92;n**Note** In contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The number of days since the Azure Data Factory factory was last modified before it is considered&#92;ninactive.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Factory &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n&#92;n**Note** In contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n

Determine the action to take when an Azure Data Factory factory is not approved based on `Azure &gt; Data Factory &gt; Factory &gt; Approved &gt; *` policies.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Determine whether the Azure Data Factory factory is allowed to exist.&#92;nThis policy will be evaluated by the Approved control. If an Azure Data Factory factory is not approved, it will be subject to the action specified in the `Azure &gt; Data Factory &gt; Factory &gt; Approved` policy.&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n&#92;n**Note**: The policy value must be a string with a value of `Approved`, `Not approved` or `Skip`, or in the form of YAML objects. The object(s) must contain the key `result` with its value as `Approved` or `Not approved`. A custom title and message can also be added using the keys `title` and `message` respectively.&#92;n

A list of Azure regions in which Azure Data Factory factorys are approved for use.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and &#39;?&#39; wildcard characters.&#92;n&#92;nThis policy will be evaluated by the Approved control. If an Azure Data Factory factory is created in a region that is not in the approved list, it will be subject to the action specified in the `Azure &gt; Data Factory &gt; Factory &gt; Approved` policy.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Determine whether the Azure Data Factory factory is allowed to exist.&#92;n&#92;nThis policy will be evaluated by the Approved control. If an Azure Data Factory factory is not approved, it will be subject to the action specified in the `Azure &gt; Data Factory &gt; Factory &gt; Approved` policy.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Configure whether to record and synchronize details for the Azure Data Factory factory into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is responsible for populating and updating all the attributes for that resource type in the Guardrails CMDB.&#92;nAll policies and controls in Guardrails are based around the resource, so usually the CMDB policy is set to **&quot;Enforce: Enabled&quot;**.&#92;n&#92;nIf set to Skip then all changes to the CMDB are paused - no new resources will be discovered, no updates will be made and deleted resources will not be removed.&#92;n&#92;nTo cleanup resources and stop tracking changes, set this policy to **&quot;Enforce: Disabled&quot;**.&#92;n&#92;nCMDB controls also use the Regions policy associated with the resource. If region is not in `Azure &gt; Data Factory &gt; Factory &gt; Regions` policy, the CMDB control will delete the resource from the CMDB.&#92;n&#92;n(**Note**: Setting CMDB to **&quot;Skip&quot;** will also pause these changes.)&#92;n

A list of Azure regions in which Azure Data Factory factorys are supported for use.&#92;n&#92;nAny factorys in a region not listed here will not be recorded in CMDB.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and&#92;n&#39;?&#39; wildcard characters.&#92;n

Determine the action to take when an Azure Data Factory factory tags are not updated based on the `Azure &gt; Data Factory &gt; Factory &gt; Tags &gt; *` policies.&#92;n&#92;nThe control ensure Azure Data Factory factory tags include tags defined in `Azure &gt; Data Factory &gt; Factory &gt; Tags &gt; Template`.&#92;n&#92;nTags not defined in Factory Tags Template will not be modified or deleted. Setting a tag value to `undefined` will result in the tag being deleted.&#92;n&#92;nSee [Tags](https://turbot.com/guardrails/docs/concepts/guardrails/tagging) for more information.&#92;n

The template is used to generate the keys and values for Azure Data Factory factory.&#92;n&#92;nTags not defined in Factory Tags Template will not be modified or deleted. Setting a tag value to `undefined` will result in the tag being deleted.&#92;n&#92;nSee [Tags](https://turbot.com/guardrails/docs/concepts/guardrails/tagging) for more information.&#92;n

Determine the action to take when an Azure Data Factory pipeline, based on the `Azure &gt; Data Factory &gt; Pipeline &gt; Active &gt; *` policies.&#92;n&#92;nThe control determines whether the resource is in active use, and if not,&#92;nhas the ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Pipeline &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n&#92;n**Note:** In contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The age after which the Azure Data Factory pipeline&#92;nis no longer considered active. If a create time is unavailable, the time Guardrails discovered the resource is used.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Pipeline &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n&#92;n**Note** In contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The number of days since the Azure Data Factory pipeline was last modified before it is considered&#92;ninactive.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`Azure &gt; Data Factory &gt; Pipeline &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n&#92;n**Note** In contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n

Determine the action to take when an Azure Data Factory pipeline is not approved based on `Azure &gt; Data Factory &gt; Pipeline &gt; Approved &gt; *` policies.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Determine whether the Azure Data Factory pipeline is allowed to exist.&#92;nThis policy will be evaluated by the Approved control. If an Azure Data Factory pipeline is not approved, it will be subject to the action specified in the `Azure &gt; Data Factory &gt; Pipeline &gt; Approved` policy.&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n&#92;n**Note**: The policy value must be a string with a value of `Approved`, `Not approved` or `Skip`, or in the form of YAML objects. The object(s) must contain the key `result` with its value as `Approved` or `Not approved`. A custom title and message can also be added using the keys `title` and `message` respectively.&#92;n

Determine whether the Azure Data Factory pipeline is allowed to exist.&#92;n&#92;nThis policy will be evaluated by the Approved control. If an Azure Data Factory pipeline is not approved, it will be subject to the action specified in the `Azure &gt; Data Factory &gt; Pipeline &gt; Approved` policy.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Configure whether to record and synchronize details for the Azure Data Factory pipeline into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is responsible for populating and updating all the attributes for that resource type in the Guardrails CMDB.&#92;nAll policies and controls in Guardrails are based around the resource, so usually the CMDB policy is set to **&quot;Enforce: Enabled&quot;**.&#92;n&#92;nIf set to Skip then all changes to the CMDB are paused - no new resources will be discovered, no updates will be made and deleted resources will not be removed.&#92;n&#92;nTo cleanup resources and stop tracking changes, set this policy to **&quot;Enforce: Disabled&quot;**.&#92;n

The Data Factory service provides scalable data integration and transformation capabilities in the Azure cloud. It allows users to create, schedule, and manage data pipelines, known as workflows, that can process and move data between various sources and destinations.&#92;n

Azure

Azure > Data Factory > Dataset > ServiceNow

The Dataset resource type is a named view of data that simply points or references the data you want to use in your activities as inputs or outputs. It could be used to create new pipelines for data transformation and integration tasks.&#92;n

Azure > Data Factory > Dataset > ServiceNow > Configuration Item

Azure > Data Factory > Dataset > ServiceNow > Configuration Item > Record

Azure > Data Factory > Factory > ServiceNow

The Factory resource type is a cloud-based data integration service that orchestrates and automates the movement and transformation of data from various sources. This can be used to create and manage data-driven workflows for ingesting, preparing, and transforming data at scale.&#92;n

Azure > Data Factory > Factory > ServiceNow > Configuration Item

Azure > Data Factory > Factory > ServiceNow > Configuration Item > Record

Azure > Data Factory > Pipeline > ServiceNow

The Pipeline resource type is a logical grouping of activities that together perform a task. The pipelines can be used to orchestrate data-driven workflows for data movement and data transformation.&#92;n

Mod: azure-datafactory

Recommended Version

Resource Types

Controls

IAM Roles

Policies