When a data source is exposed, policies are dynamically enforced on the data, appropriately redacting and masking information depending on the attributes or groups of the user accessing the data. Once the data source is exposed and subscribed to, the data can be accessed in a consistent manner, allowing reproducibility and collaboration.

This section includes how-to guides for registering data sources in Immuta:

• Register an Amazon S3 data source

• Register an Azure Synapse Analytics data source

• Register a Databricks data source

• Register a Google BigQuery data source

• Register a Redshift data source

• Register a Snowflake data source

• Bulk create Snowflake data sources

• Register a Starburst (Trino) data source

Create a New Data Source

1. Navigate to the My Data Sources page.

2. Click the New Data Source button in the top right corner.

Connect Azure Synapse Analytics Data

1. Select the Azure Synapse Analytics tile in the Data Platform section.

2. Complete these fields in the Connection Information box:

   • Server: hostname or IP address

   • Port: port configured for Azure Synapse Analytics

   • SSL: when enabled, ensures communication between Immuta and the remote database is encrypted

   • Database: the remote database

   • Username: the username to use to connect to the remote database and retrieve records for this data source

   • Password: the password to use with the above username to connect to the remote database

3. You can then choose to enter Additional Connection String Options or Upload Certificates to connect to the database.

4. Click the Test Connection button.

   If the connection is successful, a check mark and successful connection notification will appear and you will be able to proceed. If an error occurs when attempting to connect, the error will be displayed in the UI. In order to proceed to the next step of data source creation, you must be able to connect to this data source using the connection information that you just entered.

Select Virtual Population

Decide how to virtually populate the data source by selecting one of the options:

• Create sources for all tables in this database: This option will create data sources and keep them in sync for every table in the dataset. New tables will be automatically detected and new Immuta views will be created.

• Schema / Table: This option will allow you to specify tables or datasets that you want Immuta to register.

  1. Opt to Edit in the table selection box that appears.

  2. By default, all schemas and tables are selected. Select and deselect by clicking the checkbox to the left of the name in the Import Schemas/Tables menu. You can create multiple data sources at one time by selecting an entire schema or multiple tables.

  3. After making your selection(s), click Apply.

Enter Basic Information

1. Enter the SQL Schema Name Format to be the SQL name that the data source exists under in Immuta. It must include a schema macro but you may personalize it using lowercase letters, numbers, and underscores to personalize the format. It may have up to 255 characters.

2. Enter the Schema Project Name Format to be the name of the schema project in the Immuta UI. If you enter a name that already exists, the name will automatically be incremented. For example, if the schema project Customer table already exists and you enter that name in this field, the name for this second schema project will automatically become Customer table 2 when you create it.

   1. When selecting Create sources for all tables in this database and monitor for changes you may personalize this field as you wish, but it must include a schema macro.

   2. When selecting Schema/Table this field is prepopulated with the recommended project name and you can edit freely.

3. Select the Data Source Name Format, which will be the format of the name of the data source in the Immuta UI.

   • <Tablename>: The data source name will be the name of the remote table, and the case of the data source name will match the case of the macro.

   • <Schema><Tablename>: The data source name will be the name of the remote schema followed by the name of the remote table, and the case of the data source name will match the cases of the macros.

   • Custom: Enter a custom template for the Data Source Name. You may personalize this field as you wish, but it must include a tablename macro. The case of the macro will apply to the data source name (i.e., <Tablename> will result in "Data Source Name," <tablename> will result in "data source name," and <TABLENAME> will result in "DATA SOURCE NAME").

4. Enter the SQL Table Name Format, which will be the format of the name of the table in Immuta. It must include a table name macro, but you may personalize the format using lowercase letters, numbers, and underscores. It may have up to 255 characters.

Enable or Disable Schema Monitoring

When selecting the Schema/Table option, you can opt to enable Schema Monitoring by selecting the checkbox in this section.

Note: This step will only appear if all tables within a server have been selected for creation.

Opt to configure advanced settings

Although not required, completing these steps will help maximize the utility of your data source. Otherwise, skip to the next step.

Column detection

This setting monitors when remote tables' columns have been changed, updates the corresponding data sources in Immuta, and notifies Data Owners of these changes.

To enable, select the checkbox in this section.

See the Schema projects overview page to learn more about column detection.

Event time

An Event Time column denotes the time associated with records returned from this data source. For example, if your data source contains news articles, the time that the article was published would be an appropriate Event Time column.

1. Click the Edit button in the Event Time section.

2. Select the column(s).

3. Click Apply.

Selecting an Event Time column will enable

• more statistics to be calculated for this data source including the most recent record time, which is used for determining the freshness of the data source.

• the creation of time-based restrictions in the policy builder.

Latency

1. Click Edit in the Latency section.

2. Complete the Set Time field, and then select MINUTES, HOURS, or DAYS from the subsequent dropdown menu.

3. Click Apply.

This setting impacts how often Immuta checks for new values in a column that is driving row-level redaction policies. For example, if you are redacting rows based on a country column in the data, and you add a new country, it will not be seen by the Immuta policy until this period expires.

Data source tags

Adding tags to your data source allows users to search for the data source using the tags and Governors to apply Global policies to the data source. Note if Schema Detection is enabled, any tags added now will also be added to the tables that are detected.

To add tags,

1. Click the Edit button in the Data Source Tags section.

2. Begin typing in the Search by Tag Name box to select your tag, and then click Add.

Tags can also be added after you create your data source from the data source details page on the overview tab or the data dictionary tab.

Create the Data Source

Click Create to save the data source(s).

Data owners expose their data across their organization to other users by registering that data in Immuta as a data source.

By default, data owners can register data in Immuta without affecting existing policies on those tables in their remote system, so users who had access to a table before it was registered can still access that data without interruption. If this default behavior is disabled on the app settings page, a subscription policy that requires data owners to manually add subscribers to data sources will automatically apply to new data sources (unless a global policy you create applies), blocking access to those tables.

For information about the default subscription policy and how to manage it, see the Subscription policies guide.

Click a link below to navigate to a tutorial that details how to create a data source:

• Snowflake data sources

• Bulk create Snowflake data sources

• Databricks data sources

• Google BigQuery data sources

• Starburst data sources

• Redshift data sources

• Azure Synapse Analytics data sources

• Amazon S3 data sources

Data sources with nested columns

You can create Databricks data sources with nested columns when you enable complex data types. When complex types are enabled, Databricks data sources can have columns that are arrays, maps, or structs that can be nested. These columns get parsed into a nested data dictionary.

Data source user roles

There are various roles users and groups can play relating to each data source. These roles are managed through the members tab of the data source. Roles include the following types:

• Owners: Those who create and manage new data sources and their users, documentation, and data dictionaries.

• Subscribers: Those who have access to the data source data. With the appropriate data accesses and attributes, these users and groups can view files, run queries, and generate analytics against the data source data. All users and groups granted access to a data source have subscriber status.

• Experts: Those who are knowledgeable about the data source data and can elaborate on it. They are responsible for managing the data source's documentation and data dictionary tags and descriptions.

See Manage data source members for a tutorial on modifying user roles.

Data dictionary

The data dictionary provides information about the columns within the data source, including column names and value types.

Dictionary columns are automatically generated when the data source is created. However, data owners and experts can tag columns in the data dictionary and add descriptions to these entries.

Requirements

• CREATE_DATA_SOURCE Immuta permission

• Google BigQuery roles:

  • roles/bigquery.metadataViewer on the source table (if managed at that level) or dataset

  • roles/bigquery.dataViewer (or higher) on the source table (if managed at that level) or dataset

  • roles/bigquery.jobUser on the project

Prerequisite

Configure the Google BigQuery integration

Create a Google Cloud service account for creating Google BigQuery data sources

Google BigQuery data sources in Immuta must be created using a Google Cloud service account rather than a Google Cloud user account. If you do not currently have a service account for the Google Cloud project separate from the Google Cloud service account you created when configuring the Google BigQuery integration, you must create a Google Cloud service account with privileges to view and run queries against the tables you are protecting.

You have two options to create the required Google Cloud service account:

• Create a service account by using Google Cloud Console.

• Create a service account by using gcloud.

Create a service account using the Google Cloud web console

1. Using the Google Cloud documentation, create a service account with the following roles:

   • BigQuery User

   • BigQuery Data Viewer

2. Using the Google Cloud documentation, generate a service account key for the account you just created.

Create a service account using gcloud

1. Copy the script below and update the SERVICE_ACCOUNT, PROJECT_ID, and IMMUTA_GCP_KEY_FILE values.

   • SERVICE_ACCOUNT is the name for the new service account.

   • PROJECT_ID is the project ID for the Google Cloud Project that is integrated with Immuta.

   • IMMUTA_GCP_KEY_FILE is the path to a new output file for the private key.

2. Use the script below in the gcloud command line. This script is a template; change values as necessary:

   Copy

   # Fill these out
   # Please use .json extension for key
   export SERVICE_ACCOUNT=datasource-account
   export PROJECT_ID=project123
   export IMMUTA_GCP_KEY_FILE=~/GCP_${SERVICE_ACCOUNT}_key.json
   
   # Create service account for creating data sources
   gcloud iam service-accounts create ${SERVICE_ACCOUNT} --project ${PROJECT_ID}
   
   # Generate keyfile
   gcloud iam service-accounts keys create ${IMMUTA_GCP_KEY_FILE} --iam-account=${SERVICE_ACCOUNT}@${PROJECT_ID}.iam.gserviceaccount.com
   
   # Allow account to execute queries
   #gcloud projects add-iam-policy-binding ${PROJECT_ID} \
   #--member="serviceAccount:${SERVICE_ACCOUNT}@${PROJECT_ID}.iam.gserviceaccount.com" --role=projects/${PROJECT_ID}/roles/bigquery.user
   gcloud projects add-iam-policy-binding ${PROJECT_ID} \
   --member="serviceAccount:${SERVICE_ACCOUNT}@${PROJECT_ID}.iam.gserviceaccount.com" --role=roles/bigquery.user
   
   # Allow account to view data
   gcloud projects add-iam-policy-binding ${PROJECT_ID} \
   --member="serviceAccount:${SERVICE_ACCOUNT}@${PROJECT_ID}.iam.gserviceaccount.com" --role=roles/bigquery.dataViewer
   
   echo if something went wrong and you want to delete the service account, run:
   echo gcloud iam service-accounts delete ${SERVICE_ACCOUNT}@${PROJECT_ID}.iam.gserviceaccount.com --project ${PROJECT_ID}

Register data sources in Immuta

1. Click the + button in the top-left corner of the screen and select New Data Source.

2. Select the Google BigQuery tile in the Data Platform section.

3. Complete these fields in the Connection Information box:

   • Account Email Address: Enter the email address of a user with access to the dataset and tables. This is the account created in the Google BigQuery configuration guide.

   • Project: Enter the name of the project that has been integrated with Immuta.

   • Dataset: Enter the name of the dataset with the tables you want Immuta to ingest.

4. Upload a BigQuery Key File in the modal. Note that the account in the key file must match the account email address entered in the previous step.

5. Click the Test Connection button. If the connection is successful, a check mark and successful connection notification will appear and you will be able to proceed. If an error occurs when attempting to connect, the error will be displayed in the UI. In order to proceed to the next step of data source creation, you must be able to connect to this data source using the connection information that you just entered.

6. Decide how to virtually populate the data source by selecting one of the options:

   • Create sources for all tables in this database: This option will create data sources and keep them in sync for every table in the dataset. New tables will be automatically detected and new Immuta views will be created.

   • Schema / Table: This option will allow you to specify tables or datasets that you want Immuta to register.

7. Provide basic information about your data source to make it discoverable to users.

   • Enter the SQL Schema Name Format to be the SQL name that the data source exists under in Immuta. For BigQuery the schema will be the BigQuery dataset. The format must include a schema macro but you may personalize it using lowercase letters, numbers, and underscores to personalize the format. It can have up to 255 characters.

   • Enter the Schema Project Name Format to be the name of the schema project in the Immuta UI. This is an Immuta project that will hold all of the metadata for the tables in a single dataset.

     • When selecting Create sources for all tables in this database and monitor for changes, you may personalize this field as you wish, but it must include a schema macro to represent the dataset name.

     • When selecting Schema/Table, this field is pre-populated with the recommended project name and you can edit freely.

   • Select the Data Source Name Format, which will be the format of the name of the data source in the Immuta UI.

     • <Tablename>: The Immuta data source will have the same name as the original table.

     • <Schema><Tablename>: The Immuta data source will have both the dataset and original table name.

     • Custom: This is a template you create to make the data source name. You may personalize this field as you wish, but it must include a tablename macro. The case of the macro will apply to the data source name (i.e., <Tablename> will result in "Data Source Name," <tablename> will result in "data source name," and <TABLENAME> will result in "DATA SOURCE NAME").

   • Enter the SQL Table Name Format, which will be the format of the name of the table in Immuta. It must include a table name macro, but you may personalize the format using lowercase letters, numbers, and underscores. It may have up to 255 characters.

8. When selecting the Schema/Table option, you can opt to enable schema monitoring by selecting the checkbox in this section. This step will only appear if all tables within a server have been selected for creation.

9. Optional Advanced Settings:

   • Column Detection: To enable, select the checkbox in this section. This setting monitors when remote tables' columns have been changed, updates the corresponding data sources in Immuta, and notifies data owners of these changes. See schema projects overview to learn more about column detection.

   • Data Source Tags: Adding tags to your data source allows users to search for the data source using the tags and governors to apply global policies to the data source. Note if schema detection is enabled, any tags added now will also be added to the tables that are detected.

     • Click the Edit button in the Data Source Tags section.

     • Begin typing in the Search by Tag Name box to select your tag, and then click Add.

10. Click Create to save the data source(s).

Next steps

With data sources registered in Immuta, your organization can now start

• building global subscription and data policies to govern data.

• creating projects to collaborate.

Requirement

The enable_case_sensitive_identifier parameter must be set to false (default setting) for your Redshift cluster.

Create a New Data Source

1. Navigate to the My Data Sources page.

2. Click the New Data Source button in the top right corner.

Connect Redshift Data

1. Select the Redshift tile in the Data Platform section.

2. Complete these fields in the Connection Information box:

   • Server: hostname or IP address

   • Port: port configured for Redshift, typically port 5439

   • SSL: when enabled, ensures communication between Immuta and the remote database is encrypted

   • Database: the remote database

   • Username: the username to use to connect to the remote database and retrieve records for this data source

   • Password: the password to use with the above username to connect to the remote database

3. You can then choose to enter Additional Connection String Options or Upload Certificates to connect to the database.

4. Click the Test Connection button.

   If the connection is successful, a check mark and successful connection notification will appear and you will be able to proceed. If an error occurs when attempting to connect, the error will be displayed in the UI. In order to proceed to the next step of data source creation, you MUST be able to connect to this data source using the connection information that you just entered.

Select Virtual Population

Decide how to virtually populate the data source by selecting one of the options:

• Create sources for all tables in this database: This option will create data sources and keep them in sync for every table in the dataset. New tables will be automatically detected and new Immuta views will be created.

• Schema / Table: This option will allow you to specify tables or datasets that you want Immuta to register.

  1. Opt to Edit in the table selection box that appears.

  2. By default, all schemas and tables are selected. Select and deselect by clicking the checkbox to the left of the name in the Import Schemas/Tables menu. You can create multiple data sources at one time by selecting an entire schema or multiple tables.

  3. After making your selection(s), click Apply.

Enter Basic Information

1. Enter the SQL Schema Name Format to be the SQL name that the data source exists under in Immuta. It must include a schema macro but you may personalize it using lowercase letters, numbers, and underscores to personalize the format. It may have up to 255 characters.

2. Enter the Schema Project Name Format to be the name of the schema project in the Immuta UI. If you enter a name that already exists, the name will automatically be incremented. For example, if the schema project Customer table already exists and you enter that name in this field, the name for this second schema project will automatically become Customer table 2 when you create it.

   1. When selecting Create sources for all tables in this database and monitor for changes you may personalize this field as you wish, but it must include a schema macro.

   2. When selecting Schema/Table this field is prepopulated with the recommended project name and you can edit freely.

3. Select the Data Source Name Format, which will be the format of the name of the data source in the Immuta UI.

   • <Tablename>: The data source name will be the name of the remote table, and the case of the data source name will match the case of the macro.

   • <Schema><Tablename>: The data source name will be the name of the remote schema followed by the name of the remote table, and the case of the data source name will match the cases of the macros.

   • Custom: Enter a custom template for the Data Source Name. You may personalize this field as you wish, but it must include a tablename macro. The case of the macro will apply to the data source name (i.e., <Tablename> will result in "Data Source Name," <tablename> will result in "data source name," and <TABLENAME> will result in "DATA SOURCE NAME").

4. Enter the SQL Table Name Format, which will be the format of the name of the table in Immuta. It must include a table name macro, but you may personalize the format using lowercase letters, numbers, and underscores. It may have up to 255 characters.

Enable or Disable Schema Monitoring

When selecting the Schema/Table option, you can opt to enable Schema Monitoring by selecting the checkbox in this section.

Note: This step will only appear if all tables within a server have been selected for creation.

Opt to configure advanced settings

Although not required, completing these steps will help maximize the utility of your data source. Otherwise, skip to the next step.

Column detection

This setting monitors when remote tables' columns have been changed, updates the corresponding data sources in Immuta, and notifies Data Owners of these changes.

To enable, select the checkbox in this section.

See the Schema projects overview page to learn more about column detection.

Event time

An Event Time column denotes the time associated with records returned from this data source. For example, if your data source contains news articles, the time that the article was published would be an appropriate Event Time column.

1. Click the Edit button in the Event Time section.

2. Select the column(s).

3. Click Apply.

Selecting an Event Time column will enable

• more statistics to be calculated for this data source including the most recent record time, which is used for determining the freshness of the data source.

• the creation of time-based restrictions in the policy builder.

Latency

1. Click Edit in the Latency section.

2. Complete the Set Time field, and then select MINUTES, HOURS, or DAYS from the subsequent dropdown menu.

3. Click Apply.

This setting impacts how often Immuta checks for new values in a column that is driving row-level redaction policies. For example, if you are redacting rows based on a country column in the data, and you add a new country, it will not be seen by the Immuta policy until this period expires.

Sensitive data discovery

Data owners can disable sensitive data discovery for their data sources in this section.

1. Click Edit in this section.

2. Select Enabled or Disabled in the window that appears, and then click Apply.

Data source tags

Adding tags to your data source allows users to search for the data source using the tags and Governors to apply Global policies to the data source. Note if Schema Detection is enabled, any tags added now will also be added to the tables that are detected.

To add tags,

1. Click the Edit button in the Data Source Tags section.

2. Begin typing in the Search by Tag Name box to select your tag, and then click Add.

Tags can also be added after you create your data source from the data source details page on the overview tab or the data dictionary tab.

Create the Data Source

Click Create to save the data source(s).

This page details how to register Databricks data sources using the existing workflow. To register data sources using , see this .

Requirements

Databricks Spark integration

When exposing a table or view from an Immuta-enabled Databricks cluster, be sure that at least one of these traits is true:

• The user exposing the tables has READ_METADATA and SELECT permissions on the target views/tables (specifically if Table ACLs are enabled).

• The user exposing the tables is listed in the immuta.spark.acl.whitelist configuration on the target cluster.

• The user exposing the tables is a Databricks workspace administrator.

Databricks Unity Catalog integration

When exposing a table from Databricks Unity Catalog, be sure the credentials used to register the data sources have the Databricks privileges listed below.

• The following privileges on the parent catalogs and schemas of those tables:

  • SELECT

  • USE CATALOG

  • USE SCHEMA

• USE SCHEMA on system.information_schema

Enter connection information

1. Navigate to the My Data Sources page.

2. Click New Data Source.

3. Select the Databricks tile in the Data Platform section. When exposing a table or view from an Immuta-enabled Databricks cluster, be sure that at least one of these traits is true:

   • The user exposing the tables has READ_METADATA and SELECT permissions on the target views/tables (specifically if Table ACLs are enabled).

   • The user exposing the tables is listed in the `immuta.spark.acl.whitelist` configuration on the target cluster.

   • The user exposing the tables is a Databricks workspace administrator.

4. Complete the first four fields in the Connection Information box:

   • Server: hostname or IP address

   • Port: port configured for Databricks, typically port 443

   • SSL: when enabled, ensures communication between Immuta and the remote database is encrypted

   • Database: the remote database

5. Select your authentication method from the dropdown:

   • Access Token:

     1. Enter your Databricks API Token. Use a non-expiring token so that access to the data source is not lost unexpectedly.

     2. Enter the HTTP Path of your Databricks cluster or SQL warehouse.

   • OAuth machine-to-machine (M2M):

     1. Enter the HTTP Path of your Databricks cluster or SQL warehouse.

     2. Fill out the Token Endpoint with the full URL of the identity provider. This is where the generated token is sent. The default value is https://<your workspace name>.cloud.databricks.com/oidc/v1/token.

     5. Enter the Client Secret. Immuta uses this secret to authenticate with the authorization server when it requests a token.

6. If you are using a proxy server with Databricks, specify it in the Additional Connection String Options:
7. Click Test Connection.

Select virtual population

Decide how to virtually populate the data source by selecting one of the options:

• Create sources for all tables in this database: This option will create data sources and keep them in sync for every table in the dataset. New tables will be automatically detected and new Immuta views will be created.

• Schema / Table: This option will allow you to specify tables or datasets that you want Immuta to register.

  1. Opt to Edit in the table selection box that appears.

  2. By default, all schemas and tables are selected. Select and deselect by clicking the checkbox to the left of the name in the Import Schemas/Tables menu. You can create multiple data sources at one time by selecting an entire schema or multiple tables.

  3. After making your selection(s), click Apply.

Enter basic information

1. Enter the SQL Schema Name Format to be the SQL name that the data source exists under in Immuta. It must include a schema macro but you may personalize it using lowercase letters, numbers, and underscores to personalize the format. It may have up to 255 characters.

2. Enter the Schema Project Name Format to be the name of the schema project in the Immuta UI. If you enter a name that already exists, the name will automatically be incremented. For example, if the schema project Customer table already exists and you enter that name in this field, the name for this second schema project will automatically become Customer table 2 when you create it.

   1. When selecting Create sources for all tables in this database and monitor for changes you may personalize this field as you wish, but it must include a schema macro.

   2. When selecting Schema/Table this field is prepopulated with the recommended project name and you can edit freely.

3. Select the Data Source Name Format, which will be the format of the name of the data source in the Immuta UI.

   • <Tablename>: The data source name will be the name of the remote table, and the case of the data source name will match the case of the macro.

   • <Schema><Tablename>: The data source name will be the name of the remote schema followed by the name of the remote table, and the case of the data source name will match the cases of the macros.

   • Custom: Enter a custom template for the Data Source Name. You may personalize this field as you wish, but it must include a tablename macro. The case of the macro will apply to the data source name (i.e., <Tablename> will result in "Data Source Name," <tablename> will result in "data source name," and <TABLENAME> will result in "DATA SOURCE NAME").

4. Enter the SQL Table Name Format, which will be the format of the name of the table in Immuta. It must include a table name macro, but you may personalize the format using lowercase letters, numbers, and underscores. It may have up to 255 characters.

Enable or disable schema monitoring

Note: This step will only appear if all tables within a server have been selected for creation.

Create a Schema Detection Job in Databricks

2. Click Download Schema Job Detection Template.

3. Click the Click Here To Download text.

4. Before you can run the script, create the correct scope and secret by running these commands in the CLI using the Immuta API Key generated on your user profile page:
6. Schedule the script as part of a notebook job to run as often as required. Each time the job runs, it will make an API call to Immuta to trigger schema detection queries, and these queries will run on the cluster from which the request was made. Note: Use the api_immuta cluster for this job. The job in Databricks must use an Existing All-Purpose Cluster so that Immuta can connect to it over ODBC. Job clusters do not support ODBC connections.

Opt to configure advanced settings

Column detection

This setting monitors when remote tables' columns have been changed, updates the corresponding data sources in Immuta, and notifies Data Owners of these changes.

To enable, select the checkbox in this section.

Event time

An Event Time column denotes the time associated with records returned from this data source. For example, if your data source contains news articles, the time that the article was published would be an appropriate Event Time column.

1. Click the Edit button in the Event Time section.

2. Select the column(s).

3. Click Apply.

Selecting an Event Time column will enable

• more statistics to be calculated for this data source including the most recent record time, which is used for determining the freshness of the data source.

• the creation of time-based restrictions in the policy builder.

Latency

1. Click Edit in the Latency section.

2. Complete the Set Time field, and then select MINUTES, HOURS, or DAYS from the subsequent dropdown menu.

3. Click Apply.

This setting impacts how often Immuta checks for new values in a column that is driving row-level redaction policies. For example, if you are redacting rows based on a country column in the data, and you add a new country, it will not be seen by the Immuta policy until this period expires.

Sensitive data discovery

Data owners can disable sensitive data discovery for their data sources in this section.

1. Click Edit in this section.

2. Select Enabled or Disabled in the window that appears, and then click Apply.

Data source tags

Adding tags to your data source allows users to search for the data source using the tags and Governors to apply Global policies to the data source. Note if Schema Detection is enabled, any tags added now will also be added to the tables that are detected.

To add tags,

1. Click the Edit button in the Data Source Tags section.

2. Begin typing in the Search by Tag Name box to select your tag, and then click Add.

Tags can also be added after you create your data source from the data source details page on the overview tab or the data dictionary tab.

Create the data source

Click Create to save the data source(s).

Requirement

CREATE_S3_DATA_SOURCE Immuta permission

Prerequisite

Create a data source

1. Navigate to the My Data Sources page in Immuta.

2. Click New Data Source.

3. Select the Native S3 tile in the data platform section.

4. Select your AWS Account/Region from the dropdown menu.

5. Opt to select a to which data sources will be assigned.

6. Opt to add default tags to the data sources.

7. Click Next.

8. The prefix field is populated with the base path. Add to this prefix to create a data source for a prefix, bucket, or object.

   • If the data source prefix ends in a wildcard (*), it protects all items starting with that prefix. For example, a base location of s3:// and a data source prefix surveys/2024* would protect paths like s3://surveys/2024-internal/research-dept.txt or s3://surveys/2024-customer/april/us.csv.

   • If the data source prefix ends without a wildcard (*), it protects a single object. For example, a base location path of s3:// and a data source prefix of research-data/demographics would only protect the object that exactly matches s3://research-data/demographics.

9. Click Add Prefix, and then click Next.

10. Verify that your prefixes are correct and click Complete Setup.

This page details how to register Snowflake data sources using the existing workflow. To register data sources using the , see this .

Requirements

• CREATE_DATA_SOURCE Immuta permission

• USAGE Snowflake privilege on the schema and database

• REFERENCES Snowflake privilege on the tables

Enter connection information

1. Click the plus button in the top left of the Immuta console.

2. Select New Data Source.

3. Select the Snowflake tile in the Data Platform section.

4. Complete these fields in the Connection Information box:

   • Server: hostname or IP address

   • Port: port configured for Snowflake, typically port 443

   • SSL: when enabled, ensures communication between Immuta and the remote database is encrypted

   • Warehouse: Snowflake warehouse that contains the remote database

   • Database: remote database

5. From the Select Authentication Method Dropdown, select either Username and Password, Key Pair Authentication or Snowflake External OAuth:

   • Username and Password

     1. Enter a Username. This username will be used to connect to the remote database and retrieve records for this data source.

     2. Enter a Password. This password will be used with the above username to connect to the remote database.

     3. You can then choose to enter Additional Connection String Options or Upload Certificates to connect to the database.

   • Key Pair Authentication

     1. Enter a Username. This username will be used to connect to the remote database and retrieve records for this data source.

     2. Opt to enter the private key file password in the Additional Connection String Options. Use the following format: PRIV_KEY_FILE_PWD=<your_pw>.

     3. Click Select a File, and upload a Snowflake key pair file.

   • Snowflake External OAuth

     1. Fill out the Token Endpoint, which is where the generated token is sent.

     2. Fill out the Client ID, which is the subject of the generated token.

     3. To use a certificate, keep the Use Certificate checkbox enabled and complete the steps below. You cannot pass a client secret if you use this method for obtaining the access token.

        1. Opt to fill out the Resource field with a URI of the resource where the requested token will be used.

        2. Enter the x509 Certificate Thumbprint. This identifies the corresponding key to the token and is often abbreviated as x5t or is called sub (Subject).

        3. Upload the PEM Certificate, which is the client certificate that is used to sign the authorization request.

     4. To pass a client secret, uncheck the Use Certificate checkbox and complete the fields below. You cannot use a certificate if you use this method for obtaining the access token.

        1. Scope (string): The scope limits the operations and roles allowed in Snowflake by the access token. See the for details about creating scopes for External OAuth.

        2. Client Secret (string): Immuta uses this secret to authenticate with the authorization server when it requests a token.

6. Click the Test Connection button.

   If the connection is successful, a check mark and successful connection notification will appear and you can proceed. You must be able to connect to this data source using the connection information that you just entered to proceed.

Select virtual population

Decide how to virtually populate the data source by selecting one of the options:

• Create sources for all tables in this database: This option will create data sources and keep them in sync for every table in the dataset. New tables will be automatically detected and new Immuta views will be created.

• Schema / Table: This option will allow you to specify tables or datasets that you want Immuta to register.

  1. Opt to Edit in the table selection box that appears.

  2. By default, all schemas and tables are selected. Select and deselect by clicking the checkbox to the left of the name in the Import Schemas/Tables menu. You can create multiple data sources at one time by selecting an entire schema or multiple tables.

  3. After making your selection(s), click Apply.

Enter basic information

1. Enter the SQL Schema Name Format to be the SQL name that the data source exists under in Immuta. It must include a schema macro but you may personalize it using lowercase letters, numbers, and underscores to personalize the format. It may have up to 255 characters.

2. Enter the Schema Project Name Format to be the name of the schema project in the Immuta UI. If you enter a name that already exists, the name will automatically be incremented. For example, if the schema project Customer table already exists and you enter that name in this field, the name for this second schema project will automatically become Customer table 2 when you create it.

   1. When selecting Create sources for all tables in this database and monitor for changes you may personalize this field as you wish, but it must include a schema macro.

   2. When selecting Schema/Table this field is prepopulated with the recommended project name and you can edit freely.

3. Select the Data Source Name Format, which will be the format of the name of the data source in the Immuta UI.

   • <Tablename>: The data source name will be the name of the remote table, and the case of the data source name will match the case of the macro.

   • <Schema><Tablename>: The data source name will be the name of the remote schema followed by the name of the remote table, and the case of the data source name will match the cases of the macros.

   • Custom: Enter a custom template for the Data Source Name. You may personalize this field as you wish, but it must include a tablename macro. The case of the macro will apply to the data source name (i.e., <Tablename> will result in "Data Source Name," <tablename> will result in "data source name," and <TABLENAME> will result in "DATA SOURCE NAME").

Enable or disable schema monitoring

Note: This step will only appear if all tables within a server have been selected for creation.

Opt to configure advanced settings

Although not required, completing these steps will help maximize the utility of your data source.

Column detection

This setting monitors when remote tables' columns have been changed, updates the corresponding data sources in Immuta, and notifies Data Owners of these changes.

To enable, select the checkbox in this section.

Event time

An Event Time column denotes the time associated with records returned from this data source. For example, if your data source contains news articles, the time that the article was published would be an appropriate Event Time column.

1. Click the Edit button in the Event Time section.

2. Select the column(s).

3. Click Apply.

Selecting an Event Time column will enable

• more statistics to be calculated for this data source including the most recent record time, which is used for determining the freshness of the data source.

• the creation of time-based restrictions in the policy builder.

Latency

1. Click Edit in the Latency section.

2. Complete the Set Time field, and then select MINUTES, HOURS, or DAYS from the subsequent dropdown menu.

3. Click Apply.

This setting impacts how often Immuta checks for new values in a column that is driving row-level redaction policies. For example, if you are redacting rows based on a country column in the data, and you add a new country, it will not be seen by the Immuta policy until this period expires.

Sensitive data discovery

Data owners can disable sensitive data discovery for their data sources in this section.

1. Click Edit in this section.

2. Select Enabled or Disabled in the window that appears, and then click Apply.

Data source tags

Adding tags to your data source allows users to search for the data source using the tags and Governors to apply Global policies to the data source. Note if Schema Detection is enabled, any tags added now will also be added to the tables that are detected.

To add tags,

1. Click the Edit button in the Data Source Tags section.

2. Begin typing in the Search by Tag Name box to select your tag, and then click Add.

Tags can also be added after you create your data source from the data source details page on the overview tab or the data dictionary tab.

Create the data source

Click Create to register your data source.

Create a New Data Source

1. Navigate to the My Data Sources page.

2. Click the New Data Source button in the top right corner.

Connect Starburst (Trino) Data

1. Select the Starburst (Trino) tile in the Data Platform section.

2. Complete these fields in the Connection Information box:

   • Server: hostname or IP address

   • Port: port configured for Starburst (Trino)

   • SSL: when enabled, ensures communication between Immuta and the remote database is encrypted

   • Catalog: the remote catalog

   • Username: the username to use to connect to the remote database and retrieve records for this data source

   • Password: the password to use with the above username to connect to the remote database

3. You can then choose to enter Additional Connection String Options or Upload Certificates to connect to the database.

4. Click the Test Connection button.

   If the connection is successful, a check mark and successful connection notification will appear and you will be able to proceed. If an error occurs when attempting to connect, the error will be displayed in the UI. In order to proceed to the next step of data source creation, you must be able to connect to this data source using the connection information that you just entered.

Select Virtual Population

Decide how to virtually populate the data source by selecting one of the options:

• Create sources for all tables in this database: This option will create data sources and keep them in sync for every table in the dataset. New tables will be automatically detected and new Immuta views will be created.

• Schema / Table: This option will allow you to specify tables or datasets that you want Immuta to register.

  1. Opt to Edit in the table selection box that appears.

  2. By default, all schemas and tables are selected. Select and deselect by clicking the checkbox to the left of the name in the Import Schemas/Tables menu. You can create multiple data sources at one time by selecting an entire schema or multiple tables.

  3. After making your selection(s), click Apply.

Enter Basic Information

1. Enter the SQL Schema Name Format to be the SQL name that the data source exists under in Immuta. It must include a schema macro but you may personalize it using lowercase letters, numbers, and underscores to personalize the format. It may have up to 255 characters.

2. Enter the Schema Project Name Format to be the name of the schema project in the Immuta UI. If you enter a name that already exists, the name will automatically be incremented. For example, if the schema project Customer table already exists and you enter that name in this field, the name for this second schema project will automatically become Customer table 2 when you create it.

   1. When selecting Create sources for all tables in this database and monitor for changes you may personalize this field as you wish, but it must include a schema macro.

   2. When selecting Schema/Table this field is prepopulated with the recommended project name and you can edit freely.

3. Select the Data Source Name Format, which will be the format of the name of the data source in the Immuta UI.

   • <Tablename>: The data source name will be the name of the remote table, and the case of the data source name will match the case of the macro.

   • <Schema><Tablename>: The data source name will be the name of the remote schema followed by the name of the remote table, and the case of the data source name will match the cases of the macros.

   • Custom: Enter a custom template for the Data Source Name. You may personalize this field as you wish, but it must include a tablename macro. The case of the macro will apply to the data source name (i.e., <Tablename> will result in "Data Source Name," <tablename> will result in "data source name," and <TABLENAME> will result in "DATA SOURCE NAME").

4. Enter the SQL Table Name Format, which will be the format of the name of the table in Immuta. It must include a table name macro, but you may personalize the format using lowercase letters, numbers, and underscores. It may have up to 255 characters.

Enable or Disable Schema Monitoring

Note: This step will only appear if all tables within a server have been selected for creation.

Opt to configure advanced settings

Column detection

This setting monitors when remote tables' columns have been changed, updates the corresponding data sources in Immuta, and notifies Data Owners of these changes.

To enable, select the checkbox in this section.

Event time

An Event Time column denotes the time associated with records returned from this data source. For example, if your data source contains news articles, the time that the article was published would be an appropriate Event Time column.

1. Click the Edit button in the Event Time section.

2. Select the column(s).

3. Click Apply.

Selecting an Event Time column will enable

• more statistics to be calculated for this data source including the most recent record time, which is used for determining the freshness of the data source.

• the creation of time-based restrictions in the policy builder.

Latency

1. Click Edit in the Latency section.

2. Complete the Set Time field, and then select MINUTES, HOURS, or DAYS from the subsequent dropdown menu.

3. Click Apply.

This setting impacts how often Immuta checks for new values in a column that is driving row-level redaction policies. For example, if you are redacting rows based on a country column in the data, and you add a new country, it will not be seen by the Immuta policy until this period expires.

Sensitive data discovery

Data owners can disable sensitive data discovery for their data sources in this section.

1. Click Edit in this section.

2. Select Enabled or Disabled in the window that appears, and then click Apply.

Data source tags

Adding tags to your data source allows users to search for the data source using the tags and Governors to apply Global policies to the data source. Note if Schema Detection is enabled, any tags added now will also be added to the tables that are detected.

To add tags,

1. Click the Edit button in the Data Source Tags section.

2. Begin typing in the Search by Tag Name box to select your tag, and then click Add.

Tags can also be added after you create your data source from the data source details page on the overview tab or the data dictionary tab.

Create the Data Source

Click Create to save the data source(s).

In addition to creating and managing data sources, data owners can add and manage data source members manually. While this is supported, it is not recommended and instead it is much more scalable to manage user access through

For other guides related to data source members and management, see the .

Add members to a data source

1. Navigate to the data source and click the Members tab.

2. Click Add Members and enter the group name or username.

3. Select their Role:

   • Subscriber: The role can have read or write access to the table. This role is only available if there are on the data source.

   • Owner: The role can manage data source members and policies and have read or write access to the table.

   • Expert: The role can manage the data dictionary descriptions and have read or write access to the table. This role is only available if there are on the data source.

   You can also opt to for when the user’s access should expire.

4. Select Read or Write from the Access Grant dropdown. This option is only available if have been enabled.

5. Click Add.

Bulk add users to multiple data sources

1. Navigate to the data sources list page.

2. Select the data sources you want to add users to by clicking the checkbox next to the data source.

3. Select Add Users.

4. In the modal, type the user name or group name and select the user or group you want to add from the dropdown menu.

5. Opt to set an Expiration for the users' subscriptions. Additionally, you can change the role from Subscriber to Expert or Owner for the users or groups using the dropdown menu in the Role column.

6. Click Add. All users and groups will be added to the data sources you selected.

Set user access expiration date for a data source

As a data owner, you can limit the amount of time a user or group has access to your data source by setting an access expiration date.

1. Navigate to the Members tab.

2. Adjust the number of days under the Expires column for the user/group whose access you want to limit (the limit is counting from today, so users/groups with 0 days left means their access will be revoked by the end of today and users with 1 day left means their access will be revoked by the end of tomorrow).

3. Save your changes.

To remove the limit (or set the limit to Never), delete the number from the field and save your changes.

Modify user or group roles within a data source

1. Navigate to the Members tab.

2. Click the drop-down arrow under the Role column next to the user/group whose role you’d like to change.

3. Select another role (subscribed, expert, owner or ingest user, if applicable).

Notifications about the change will be sent to the affected users and groups (as well as alternative Owners).

View user or group subscription history

1. Navigate to the Members tab.

2. Click the Name of the user or group whose history you want to review.

Remove users or groups from a data source

As a data owner, you can deny access to any users or groups at any time.

1. Navigate to the Members tab.

2. To remove a user or group from a data source, click Deny in the Actions column next to the user or group you want to remove.

3. Complete the Deny Access form, including a reason for revoking the access.

This action will immediately update users' or groups' subscription status, and they will no longer have any access to the data source. Notifications will be sent to the affected users (as well as alternative data owners) informing them of the change in subscription status.

Related guides

Reference guide

How-to guides

In addition to adding and managing data source members as outlined above, data owners can manage data source

Requirements

• Snowflake Enterprise Edition

• Snowflake X-Large or Large warehouse is strongly recommended

Create Snowflake data sources

1. Set the to None for bulk data source creation. This will simplify the data source creation process by not automatically applying policies.

2. Make a request to the Immuta V2 API , as the Immuta UI does not support creating more than 1000 data sources. The following options must be specified in your request to ensure the maximum performance benefits of bulk data source creation. The Skip Stats Job tag is only required if you are using ; otherwise, Snowflake data sources automatically skip the stats job.

Specifying disableSensitiveDataDiscovery as true ensures that will not be applied when the new data sources are created in Immuta, regardless of how it is configured for the Immuta tenant. Disabling sensitive data discovery improves performance during data source creation.

Applying the Skip Stats Job tag using the tableTag value will ensure that some jobs that are not vital to data source creation are skipped, specifically the fingerprint and high cardinality check jobs.

When the Snowflake bulk data source creation feature is configured, the create data source endpoint operates asynchronously and responds immediately with a bulkId that can be used for monitoring progress.

Monitor progress

To monitor the progress of the background jobs for the bulk data source creation, make the following request using the bulkId from the response of the previous step:

The response will contain a list of job states and the number of jobs currently in each state. If errors were encountered during processing, a list of errors will be included in the response:

With these recommended configurations, bulk creating 100,000 Snowflake data sources will take between six and seven hours for all associated jobs to complete.

As a data owner, you can and , , and a data source.

For other guides related to data source members and management, see the .

Edit a data source

1. Navigate to the Overview tab.

2. Click the more actions icon in the upper right corner of the page and select Edit.

3. Change your settings in the data source workflow.

   Note: Some settings cannot be changed once the data source has been created. In these cases, simply create a new data source with the new settings.

4. When completed, navigate to the end of the workflow and click Save.

   Note: Some data sources may require the data owner to reconnect to the remote database before any changes to the data source can be saved.

For information on specific data source settings, see the guides below:

Bulk edit data sources

Data owners can bulk edit data sources.

1. Navigate to the data sources list page.

2. Select the checkboxes for the data sources you want to edit. Note that when editing a connection string using bulk edit, all data sources from that connection must be selected.

3. Select the action you want or click More Actions for additional options.

4. Confirm your edits by following the prompts in the modals that appear.

Disable a data source

Disabling a data source hides it and its data from all users except the data owner. While in this state, the data source will display as disabled in the console for the data owner and other users will not be able to see it at all.

1. Navigate to the Overview tab.

2. Click on the more actions icon in the upper right corner and select Disable.

A label will appear next to the data source indicating it is now disabled, and a notification will be sent to all users of the data source informing them that the data source has been disabled.

Enable a disabled data source

1. Navigate to the Overview tab.

2. Click on the more actions icon in the upper right corner and select Enable.

A notification will be sent out to all users of the data source informing them that the data source has been enabled.

Delete a data source

Deleting a data source permanently removes it from Immuta. Data sources must first be disabled before they can be deleted.

2. Navigate to the Overview tab and click the more actions icon and select Delete.

3. Confirm that the data source should be deleted by clicking Delete.

A notification will be sent out to all users of the data source informing them that the data source has been deleted.

Related guides

Reference guides

For information about data sources and policies, see the following guides:

How-to guides

In addition to adding and managing data source settings as outlined above, data owners can manage data source

Once a data source is created, the data owner can manage data source policies, members, data dictionary, and tags.

The reference and how-to guides in this section cover topics related to managing existing data sources.

How-to guides

• : Edit data source settings or disable and delete a data source.

• : Add, remove, or modify users on a data source.

• : Approve and deny subscriptions requests on data source.

• : Disable metadata collection that requires sampling data.

• : Manage the data dictionary descriptions and tags.

Reference guide

: This reference guide defines the data source health check jobs that are run when a data source is created.

A data source is how data owners expose their data across their organization to other Immuta users. Throughout this process, the data is not copied. Instead, Immuta uses metadata from the data source to determine how to expose the data. An Immuta data source is a virtual representation of data that exists in a remote .

This section includes reference and how-to guides for registering and managing data sources.

This reference guide describes Immuta data sources and their major components.

These how-to guides illustrate how to register data in Immuta.

The guides in this section illustrate how to manage and edit data sources and data dictionaries.

The reference and how-to guides in this section describe schema monitoring and illustrate how to configure it for your integration.

When an Immuta data source is created, background jobs use the connection information provided to compute health checks dependent on the type of data source created and how it was configured. These data source health checks include the

• blob crawl status: indicates whether the blob was successfully crawled.

• column detection status: indicates whether the job run to determine if a column was added or removed from the remote table registered as an Immuta data source was successful.

• external catalog link status: indicates whether or not the external catalog was successfully linked to the data source.

• fingerprint generation status: indicates whether or not the data source fingerprint was successfully generated.

• framework classification status: indicates whether classification was successfully run on the data source to determine the sensitivity of the data source.

• global policy applied status: indicates whether global policies were successfully applied to the data source.

• high cardinality calculation status: indicates whether the data source's high cardinality column was successfully calculated.

• native SQL sync status (for Snowflake data sources): indicates whether Snowflake governance policies have been successfully synced.

• native SQL view creation status (for Redshift data sources): indicates whether native views were properly created for Redshift tables registered in Immuta.

• row count status: indicates whether the number of rows in the data source was successfully calculated.

• schema detection status: indicates whether the job run to determine if a remote table was added or removed from the schema was successful.

• sensitive data discovery status: indicates whether sensitive data discovery was successfully run on the data source.

After these jobs complete, the health status for each is updated to indicate whether the status check passed, was skipped, is unknown, or failed.

These background jobs can be disabled during data source creation by adding a specific tag to prevent automatic table statistics. This prevent statistics tag can be set on the by a system administrator. However, with automatic table statistics disabled these policies will be unavailable until the data source owner :

• Masking with format preserving masking

• Masking with k-anonymization

• Masking using randomized response

Unhealthy Databricks data sources

Unhealthy data sources may fail their row count queries if they run against a cluster that has the Databricks query watchdog enabled.

Limitations

Data sources with over 1600 columns will not have health checks run, but will still appear as healthy. The health check cannot be run automatically or manually.

The data dictionary provides information about the columns within the data source, including column names and value types.

As a data owner, you can manage data dictionary descriptions and column tags. For other guides related to the data dictionary, see the Related guides section.

Manage data dictionary descriptions

1. Navigate to the Data Dictionary tab.

2. To add or edit column descriptions, click the menu icon in the Actions column next to the entry you want to change and select Edit.

3. Complete the fields in the form that appears, and then click Save.

Related guides

Reference guide

For information about the data dictionary, see the Data sources in Immuta overview.

How-to guide

In addition to managing data dictionary descriptions as outlined above, data owners or experts can also manage column tags.

Your outgoing and incoming requests are consolidated on the requests tab on your user profile page. Similar to notifications, a red dot displays on the request icon whenever you have pending requests. The sections below guide you through managing these requests.

Manage access requests

1. Navigate to your Profile page, and then click the Requests tab. The names of the users who have submitted requests are displayed in the left pane. Once a user is selected, the corresponding pending requests are displayed on the right.

2. To view more information about the request, click the Details button in the Actions column of a request.

3. Click the Approve or Deny button in the Actions column of the request.

Bulk approvals

To approve or deny multiple access requests simultaneously,

1. Navigate to your Profile page, and then click the Requests tab.

2. Select the checkbox next to each request you want to address, and then click the Approve Selected or Deny Selected button.

Manage data source requests

If a policy that includes the New tag is active and schema monitoring is enabled or you have registered a connection, Immuta applies a New tag to new data sources, new columns, or changed columns and sends data owners a request to validate those changes.

1. Navigate to your Profile page, and then click the Requests tab.

2. Click the approvals count in the Request Information column to view information about the change to the data source. The change will be one of the following:

   • Column added

   • Column changed

   • Column deleted

   • Data source created

3. After verifying the change, click Validate.

For more information about these requests, see the Schema monitoring guide or the Connections guide.

Manage unmask requests

If users make an unmask request, a tasks tab will appear on the data source overview page for the user making the request and the user receiving the request. From this tab, users can view and manage two different task views:

• Your Created Tasks: This page lists the status and information of the unmask requests you've submitted.

• Tasks For You: This page lists the status and information of the unmask requests that have been submitted to you.

To complete a task,

1. Navigate to the Tasks tab from the Data Source Overview page, and then click the Tasks For You toggle.

2. Click the Unmask Values icon in the Actions column of the task.

3. A dialog box will appear with the masked and unmasked value. Note: You can view information about this request, including the reason for the request and the date is was created, by clicking the Task Info button in the Actions column.

To delete a task, click Delete Task in the Actions column of the relevant task.

Related guides

Reference guides

• Data sources in Immuta overview

• Connections

• Schema monitoring

How-to guides

In addition to managing data source requests as outlined above, data owners can manage data source

• column tags

• data dictionaries

• policies

• members

• settings

If you want to disable the metadata collection that requires sampling data, you must

1. Disable Immuta fingerprinting.

2. Stop all data source health checks.

3. Add the Skip Stats Job tag to all data sources.

These steps will ensure that Immuta queries no data, under any circumstances. Without this sample data, some Immuta features will be unavailable. Sensitive data discovery (SDD) cannot be used to automatically detect sensitive data in your data sources, and the following masking policies will not work:

• Masking with format preserving masking

• Masking with k-anonymization

• Masking using randomized response

Fingerprinting

Reach out to your Immuta representative to disable fingerprinting.

Data Source Health Checks

Reach out to your Immuta representative to disable health checks on all data sources.

Skip Stats Job Tag

Tag each data source with the seeded Skip Stats Job tag to stop Immuta from collecting a sample and running table stats on the sample. You can tag data sources as you create them in the UI or via the Immuta API.

Note that data sources automatically skip the stats job upon registration, without the Skip Stats Job tag, as long as there are no active policies requiring them. The following policies require stats:

• Column masking with randomized response

• Column masking with format preserving masking

• Column masking with k-anonymization

• Column masking with rounding

• Column masking with reversibility

• Row minimization

With schema monitoring enabled, Immuta monitors your organization's servers to find when new tables or columns are created or deleted and automatically registers (or disables) those tables in Immuta.

How-to guides

• Manage schema monitoring: Edit connection information, schema project owner, or the naming conventions of data registered in the schema.

• Run schema monitoring jobs: Manually trigger schema monitoring.

Reference guides

• Schema monitoring: This reference guide describes the design and components of schema monitoring.

• Schema projects: This reference guide describes schema projects, which group all the data sources of a schema.

Concept guide

Why use schema monitoring?: This explanatory guide provides a conceptual overview of schema monitoring. It offers a discussion of the benefits of the feature, context for why it was developed, and insights into the features schema monitoring pairs with. This guide is designed to deepen your understanding of schema monitoring's purpose as you implement it.

Schema projects are automatically created and managed by Immuta. They group all the data sources of the schema, and when new data sources are created, manually or with schema monitoring, they are automatically added to the schema project. They work as a tool to organize all the data sources within a schema, which is particularly helpful with schema monitoring enabled.

Schema projects are created when tables are registered as data sources in Immuta. The user creating the data source does not need the CREATE_PROJECT permission to have the project auto-create because no data sources can be added by the owner. Instead, new data sources are managed by Immuta. The user can manage Subscription policies for schema projects, but they cannot apply Data policies or purposes to them.

The schema settings, such as schema evolution and connection information, can be edited from the project overview tab. Note: Deleting the project will delete all of the data sources within it as well.

Schema Project Actions

Schema settings are edited from the project overview tab:

• Schema Project Connection Details: Editing these details will update them for all the data sources within the schema project.

• Data Source Naming Convention: When schema monitoring is enabled, new data sources will be automatically detected and added to the schema project. Updating the naming convention will change how these newly detected data sources are named by Immuta.

• Schema Detection Owner: When schema monitoring is enabled, a user is assigned to be the owner of any detected and Immuta created data source.

• Disable or delete your schema project: Deleting the project will delete all of the data sources within it as well.

Schema monitoring allows organizations to monitor their data environments. When it is enabled, Immuta monitors the organization's servers to detect when new tables or columns are created or deleted, and automatically registers (or disables) those tables in Immuta. These newly updated data sources will then have any global policies and tags that are set in Immuta applied to them. The Immuta data dictionary will be updated with any column changes, and the Immuta environment will be in sync with the organization's data environment. This automated process helps organizations keep compliant without the need to manually keep data sources up to date.

Schema monitoring is enabled while creating or editing a data source and only registers new tables and columns within known schemas. It does not register new schemas. Data owners or governors can edit the naming convention for newly detected data sources and the schema detection owner from the schema project page after it has been enabled.

See the Register a data source guides for instructions on enabling schema monitoring or Manage schema monitoring for instructions on editing the schema monitoring settings.

Column detection

Column detection is a part of schema monitoring, but can also be enabled on its own to detect the column changes of a select group of tables. Column detection monitors when columns are added or removed from a table and when column types are changed and updates those changes in the appropriate Immuta data source's data dictionary.

See one of the Register a data source guides for instructions on enabling column detection.

Tracking new data sources and columns

When new data sources and columns are detected and added to Immuta, or when column types have changed, they will always automatically be tagged with the New tag. This allows governors to use the seeded New Column Added global policy to mask columns with the New tag, since they could contain sensitive data.

The New Column Added global policy is staged (inactive) by default.

See the Clone, activate, or stage a global policy guide to activate this seeded global policy if you want any columns with the New tag to be automatically masked.

Data source requests

When schema monitoring is enabled and there is an active policy that targets the New tag, Immuta sends validation requests to data owners for the following changes made in the remote data platform:

• Column added: Immuta applies the New tag on the column that has been added and sends a request to the data owner to validate if the new column contains sensitive data. Once the data owner confirms they have validated the content of the column, Immuta removes the New tag from it and as a result any policy that targets the New column tag no longer applies.

• Column data type changed: Immuta applies the New tag on the column where the data type has been changed and sends a request to the data owner to validate if the column contains sensitive data. Once the data owner confirms they have validated the content of the column, Immuta removes the New tag from it and as a result any policy that targets the New column tag no longer applies.

• Column deleted: Immuta deletes the column from the data source's data dictionary in Immuta. Then, Immuta sends a request to the data owner to validate the deleted column.

• Data source created: Immuta applies the New tag on the data source that has been newly created and sends a request to the data owner to validate if the new data source contains sensitive data. Once the data owner confirms they have validated the content of the data source, Immuta removes the New tag from it and as a result any policy that targets the New data source tag no longer applies.

For instructions on how to view and manage your assigned tasks in the Immuta UI, see the Manage data source requests guide. To view and manage your assigned tasks via the Immuta API, see the Manage data source requests section of the API documentation.

Workflow

1. Immuta user registers a data source with schema monitoring enabled.

2. Every 24 hours, at 12:30 a.m. UTC by default, Immuta checks the servers for any changes to tables and columns.

3. If Immuta finds a change, it will update the appropriate Immuta data source or column:

   1. If Immuta finds a new table, then Immuta creates an Immuta data source for that table and tags it New.

   2. If Immuta finds a table has been deleted, then Immuta disables that table's data source.

   3. If Immuta finds a previously deleted table has been re-created, then Immuta restores that table's data source and tags it New.

   4. If Immuta finds that the backing object type of a data source has been changed (for example, from a TABLE to a VIEW) in Snowflake or Databricks Unity Catalog, Immuta will reapply existing policies on the data source. Note that because of policy limitations on Unity Catalog views, changing a Databricks Unity Catalog object type from a table to a view could result in some types of data policies being removed. See the Databricks Unity Catalog integration reference guide for a list of data policies that are not supported for views.

   5. If Immuta finds a new column within a table, then Immuta adds that column to the data dictionary and tags it New.

   6. If Immuta finds a column has been deleted, then Immuta deletes that column from the data dictionary.

   7. If Immuta finds a column type has changed, then Immuta updates the column type in the data dictionary and tags it New.

4. Active policies that target the New data source or column tag will be applied until a data owner validates the changes.

To run schema monitoring or column detection manually, see the Run schema monitoring and column detection jobs page.

Schedule

The default schedule for schema monitoring to run is every 24 hours. Some organizations may need to schedule it to run more often; however, this needs careful consideration as it can impact performance and compute costs.

Schema monitoring best practices

• Manually trigger schema monitoring (filtered down to the database) after your dbt or other transform workflows run. For more information, see the dbt and transform workflow for limited policy downtime guide.

• When manually triggering schema monitoring, specify a table or database for maximum performance efficiency and to reduce data or policy downtime. For more information on triggering schema monitoring, see the Manually run schema monitoring guide.

• If you are manually managing data tags, activate the "New Column Added" global policy to protect newly found and potentially sensitive data. This policy sets all columns with the tag New to NULL until a data owner reviews and validates their content. Using this workflow protects your data and avoids data leaks on new columns getting automatically added. This recommendation is unnecessary for users leveraging sensitive data discovery (SDD) or using an external data catalog.

Edit Schema Project Connection

Requirement: Must be an owner of the schema project

1. Navigate to the Project Overview tab.

2. Click Edit Connection.

3. Use the Connection Information modal to make any necessary changes.

4. Click Save.

Edit Schema Monitoring Naming Convention

Requirement: Must be an owner of the schema project

1. Navigate to the Project Overview tab.

2. Click Edit Schema Monitoring.

3. Use the Basic Information modal to make any necessary changes to naming formats.

4. Click Save.

Add New Schema Monitoring Owner

Requirement: Must be an owner of the schema project

1. Navigate to the Project Overview tab.

2. Click Edit Schema Monitoring.

3. Use the dropdown menu in the Schema Monitoring modal to select a new schema detection owner. The new owner must be an owner of one or more of the data sources belonging to that schema.

4. Click Save.

Immuta is a live metadata aggregator - metadata about your data and your users. With data metadata specifically, Immuta can monitor changes in your database and reflect those changes in your Immuta tenant through .

When schema monitoring is enabled, Immuta monitors your organization's servers to identify when new tables or columns are created or deleted, and automatically registers (or disables) those tables in Immuta. The newly updated data sources then have global policies and tags applied to them, and the Immuta data dictionary is updated with column changes.

Schema monitoring keeps Immuta in sync with your data environment, helping you remain compliant without having to manually update individual data sources.

Anti-patterns: Using Immuta without schema monitoring

Without schema monitoring, data owners have to manually add and remove Immuta data sources when users add or remove tables from databases in their data platforms. At worst, data owners are not aware of these changes; at best they are aware of the changes and have to manually update Immuta with those changes, which is a time-consuming, error-prone process.

Beyond draining data owners' time, manually updating data sources to reflect the state of the data platform also complicates the process: not only must they understand when a new table is present, but they then must remember to tag it and protect it appropriately. This leaves organizations ripe for data leaks as new data is created across the business, perhaps daily.

Schema monitoring, by contrast, is scalable and accounts for the evolution of your schemas and policies. Instead of manually managing access to these tables or adding and removing data sources, you are empowered to register a schema, create policies, and allow Immuta to manage those policies and changes to your schema for you to keep your data in sync and restrict access appropriately.

Business value

Both monitoring for new data and align with the , removing redundant and arduous work. Once tables are registered and tagged, policies can immediately be applied - this means humans can be completely removed from the process by creating tag-based policies that dynamically apply themselves to new tables.

Then, your business reaps the following benefits:

• Increased revenue: Accelerate data access and time-to-data access because where sensitive data lives is well understood.

• Decreased cost: Operate efficiently and move with agility at scale.

• Decreased risk: Discover and protect sensitive data immediately.

What features does it pair with?

Schema monitoring pairs with the following features:

Manually Run Schema Monitoring Jobs

Manually Run Schema Monitoring Job for All Data Sources

Requirement: Immuta permission USER_ADMIN

You can manually run a schema monitoring job globally using the /dataSource/detectRemoteChanges endpoint of the Immuta API with an empty payload.

Manually Run Schema Monitoring Job as a Data Owner

You can manually run a schema monitoring job for all data sources that you own using the /dataSource/detectRemoteChanges endpoint of the Immuta API with a payload containing the hostname for your data sources or their individual IDs.

Manually Run Schema Monitoring Job as a Data User

You can manually run a schema monitoring job for data sources you are subscribed to using the /dataSource/detectRemoteChanges endpoint of the Immuta API with a payload containing the hostname for your data source and the table name or data source ID.

Manually Run a Column Detection Job

1. Navigate to the data source overview page.

2. Click on the health check icon.

3. Scroll to Column Detection, and click Trigger Detection.