Supported technologies

• Databricks Unity Catalog

• Snowflake

Requirements

• A native integration enabled on the Immuta app settings page

• Data sources registered

To complete your upgrade,

1. Select Upgrade Manager in the navigation. This tab will only be available if you have integrations ready for upgrade.

2. Click Start Upgrade.

3. Enter a Connection Key. The connection key represents the unique name of your connection and will be used as prefix in the name for all data objects associated with this connection. It will also appear as the display name in the UI and will be used in all API calls made to update or delete the connection.

4. Click Next.

5. Ensure Immuta has the correct credentials to connect to Databricks Unity Catalog or Snowflake. Select the tab below for more information:

1. Click Next.

2. Click Upgrade Connection.

3. Click the link to the docs to understand the impacts of the upgrade.

4. Click the checkbox to confirm understanding of the upgrade effects, and click Yes, Upgrade Connection.

Immuta integrates with your data platforms and external catalogs so you can register your data and effectively manage access controls on that data.

This section includes concept, reference, and how-to guides for registering and managing data sources so that you can discover, monitor, and protect sensitive data.

Registering a connection

This section includes reference and how-to guides for configuring Immuta in order to manage data through a single connection between Immuta and your data platform.

Registering metadata

This section covers concepts related to registering your metadata with Immuta.

Domains

The how-to and references guides in this section illustrate how to use domains. Domains are containers of data sources that allow you to assign data ownership and access management to specific business units, subject matter experts, or teams at the nexus of cross-functional groups.

Tags

This section covers concepts related to tags and how to use them in Immuta.

Requirements

The following permissions and personas are used in the registration process:

• Immuta permission: CREATE_DATA_SOURCE

• Snowflake permissions for the user registering the connection and running the script:

  • CREATE DATABASE ON ACCOUNT WITH GRANT OPTION

  • CREATE ROLE ON ACCOUNT WITH GRANT OPTION

  • CREATE USER ON ACCOUNT WITH GRANT OPTION

  • MANAGE GRANTS ON ACCOUNT WITH GRANT OPTION

  • APPLY MASKING POLICY ON ACCOUNT WITH GRANT OPTION

  • APPLY ROW ACCESS POLICY ON ACCOUNT WITH GRANT OPTION

  • REFERENCES on all tables

  • USAGE on the schema and database to register data sources

• Snowflake permissions for the new Immuta system user that is created:

  • APPLY MASKING POLICY ON ACCOUNT

  • APPLY ROW ACCESS POLICY ON ACCOUNT

  • Additional grants associated with the IMMUTA database

Prerequisite

No Snowflake native integration configured in Immuta. If your Snowflake native integration is already configured on the app settings page, follow the Use the connection upgrade manager guide.

Register a connection

To register a Snowflake connection, follow the instructions below.

1. Click Data and select the Infrastructure tab in the navigation menu.

2. Click the + Add Host button.

3. Select the Snowflake data platform tile.

4. Enter the connection information:

   • Host: The URL of your Snowflake account.

   • Port: Your Snowflake port.

   • Warehouse: The warehouse the Immuta system account user will use to run queries and perform Snowflake operations.

   • Immuta Database: The new, empty database for Immuta to manage. This is where system views, user entitlements, row access policies, column-level policies, procedures, and functions managed by Immuta will be created and stored.

   • Role: The default Snowflake role for the Immuta system account user.

   • Connection Key: The connection key represents the unique name of your connection and will be used as prefix in the name for all data objects associated with this connection. It will also appear as the display name in the UI and will be used in all API calls made to update or delete the connection.

5. Click Next.

6. Select an authentication method from the dropdown menu. This authentication information will be included in the script populated later on the page.

   1. Username and password: Choose one of the following options.

      1. Select Immuta Generated to have Immuta populate the system account name and password.

      2. Select User Provided to enter your own name and password for the Immuta system account.

   2. Snowflake External OAuth:

      1. Fill out the Token Endpoint, which is where the generated token is sent. It is also known as aud (audience) and iss (issuer).

      2. Fill out the Client ID, which is the subject of the generated token. It is also known as sub (subject).

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

      4. Enter the x509 Certificate Thumbprint. This identifies the corresponding key to the token and is often abbreviated as x5t or is called kid (key identifier).

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

   3. Key Pair Authentication:

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

      2. If using a private key, enter the Private Key Password.

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

7. The Role is prepopulated from the entry on the previous page.

8. Copy the provided script and run it in Snowflake with the following Snowflake permissions:

   • CREATE DATABASE ON ACCOUNT WITH GRANT OPTION

   • CREATE ROLE ON ACCOUNT WITH GRANT OPTION

   • CREATE USER ON ACCOUNT WITH GRANT OPTION

   • MANAGE GRANTS ON ACCOUNT WITH GRANT OPTION

   • APPLY MASKING POLICY ON ACCOUNT WITH GRANT OPTION

   • APPLY ROW ACCESS POLICY ON ACCOUNT WITH GRANT OPTION

9. Click Test Connection.

10. If the connection is successful, click Next. If there are any errors, check the connection details and credentials to ensure they are correct and try again.

11. Ensure all the details are correct in the summary and click Complete Setup.

Requirement: Immuta permission CREATE_DATA_SOURCE

Prerequisite: A connection for Snowflake or Databricks Unity Catalog

Crawl a connection

1. Click Data and select the Infrastructure tab in the navigation menu.

2. Click the more actions menu for the connection you want and select Run Object Sync.

3. Opt to click the checkbox to Also scan inactive objects.

4. Click Run Object Sync.

Crawl a database

1. Click Data and select the Infrastructure tab in the navigation menu.

2. Select the connection.

3. Click the more actions menu in the Action column for the database you want to crawl and select Run Object Sync.

4. Opt to click the checkbox to Also scan inactive objects.

5. Click Run Object Sync.

Crawl a schema

1. Click Data and select the Infrastructure tab in the navigation menu.

2. Select the connection.

3. Select the database.

4. Click the more actions menu in the Action column for the schema you want to crawl and select Run Object Sync.

5. Opt to click the checkbox to Also scan inactive objects.

6. Click Run Object Sync.

Requirements

The following permissions and personas are used in the registration process:

• Immuta permission: CREATE_DATA_SOURCE

• Databricks privileges for the user registering the connection and running the script:

  • Account or workspace admin

  • CREATE CATALOG privilege on the Unity Catalog metastore to create an Immuta-owned catalog and tables

• Databricks privileges for the service principal you create:

  • OWNER privilege on the Immuta catalog you configure.

  • OWNER privilege on catalogs with schemas and tables registered as Immuta data sources so that Immuta can administer Unity Catalog row-level and column-level security controls. This privilege can be applied by granting OWNER on a catalog to a Databricks group that includes the Immuta service principal to allow for multiple owners. If the OWNER privilege cannot be applied at the catalog- or schema-level, each table registered as an Immuta data source must individually have the OWNER privilege granted to the Immuta service principal.

  • USE CATALOG and USE SCHEMA on parent catalogs and schemas of tables registered as Immuta data sources so that the Immuta service principal can interact with those tables.

  • SELECT and MODIFY on all tables registered as Immuta data sources so that the Immuta service principal can grant and revoke access to tables and apply Unity Catalog row- and column-level security controls.

  • USE CATALOG on the system catalog for native query audit.

  • USE SCHEMA on the system.access schema for native query audit.

  • SELECT on the following system tables for native query audit:

    • system.access.audit

    • system.access.table_lineage

    • system.access.column_lineage

Prerequisites

• Unity Catalog enabled on your Databricks cluster or SQL warehouse. All SQL warehouses have Unity Catalog enabled if your workspace is attached to a Unity Catalog metastore. Immuta recommends linking a SQL warehouse to your Immuta tenant rather than a cluster for both performance and availability reasons.

Register a connection

1. Click Data and select the Infrastructure tab in the navigation menu.

2. Click the + Add Host button.

3. Select the Databricks data platform tile.

4. Enter the connection information:

   • Host: The hostname of your Databricks workspace.

   • Port: Your Databricks port.

   • HTTP Path: The HTTP path of your Databricks cluster or SQL warehouse.

   • Immuta Catalog: The name of the catalog Immuta will create to store internal entitlements and other user data specific to Immuta. This catalog will only be readable for the Immuta service principal and should not be granted to other users. The catalog name may only contain letters, numbers, and underscores and cannot start with a number.

   • Connection Key: The connection key represents the unique name of your connection and will be used as prefix in the name for all data objects associated with this connection. It will also appear as the display name in the UI and will be used in all API calls made to update or delete the connection.

5. Click Next.

6. Select Access Token authentication method from the dropdown menu.

7. Enter the Access Token in the Immuta System Account Credentials section. This is the access token for the Immuta service principal. This service principal must have the metastore privileges listed in the requirements section at the top of this page for the metastore associated with the Databricks workspace. If this token is configured to expire, update this field regularly for the integration to continue to function. This authentication information will be included in the script populated later on the page.

8. Copy the provided script and run it in Databricks as a user with the CREATE CATALOG privilege on the Unity Catalog metastore.

9. Click Validate Connection.

10. If the connection is successful, click Next. If there are any errors, check the connection details and credentials to ensure they are correct and try again.

11. Ensure all the details are correct in the summary and click Complete Setup.

Connections allow you to register your data objects in a technology through a single connection, making data registration more scalable for your organization. Instead of registering schema and databases individually, you can register them all at once and allow Immuta to monitor your data platform for changes so that data sources are added and removed automatically to reflect the state of data on your data platform.

Once you register your connection, Immuta presents a hierarchical view of your data that reflects the hierarchy of objects in your data platform:

• Account (Snowflake) or Metastore (Databricks Unity Catalog)

• Database

• Schema

• Tables: These represent the individual objects in your data platform, and when active, become data sources

Beyond making the registration of your data more intuitive, connections provides more control. Instead of performing operations on individual schemas or tables, you can perform operations (such as object sync) at the connection level.

Requirements

See the or connection registration how-to guides for a list of requirements.

Connection registration and object sync

With connections, you configure the integration and register data sources simultaneously. Once you save your connection, Immuta manages and applies Snowflake or Unity Catalog governance features to data objects registered in Immuta.

Then, Immuta crawls your connection to register all tables within every schema and database that the Snowflake role or Databricks account credentials you provided during the registration has access to. The object metadata, user metadata, and policy definitions are stored in the Immuta metadata database, and this metadata is used to enforce policies for users accessing this data.

After initial registration, your connection can be crawled in two ways:

• Periodic crawl: This crawl happens once every 24 hours. Currently, updating this schedule is not configurable.

During these subsequent crawls of your connection, Immuta identifies tables, schemas, or databases that have been added or removed. If tables are added, new data sources are created in Immuta. If remote tables are deleted, the corresponding data sources and data objects will be removed from Immuta.

Data source requests

When 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.

Default settings

• Object sync: This setting allows Immuta to monitor the connection for changes. When Immuta identifies a new table, a data source will automatically be created. Similarly, if remote tables are deleted, the corresponding data sources and data objects will be deleted in Immuta. This setting is enabled by default and cannot be disabled.

• Default run schedule: This sets the time interval for Immuta to check for new objects. By default, this schedule is set to 24 hours.

• Impersonation: This setting enable and defines the role for user impersonation in Snowflake. User impersonation is not supported in the Databricks Unity Catalog integration. This setting is disabled by default.

Deregistering a connection

Deregistering a connection automatically deletes all of its child objects in Immuta. However, Immuta will not remove the objects in your Snowflake or Databricks account.

Limitations

• Snowflake and Databricks Unity Catalog are currently the only integrations that support connections

• Databricks Unity Catalog: Delta shares are unsupported.

Related guides

How-to guides

Reference guides

Connections are an improvement from the existing process for not only onboarding your data sources but also managing the integration. However, there are some differences between the two processes that should be noted and understood before you start with the upgrade.

1. API changes: See the for a complete breakdown of the APIs that will not work once you begin the upgrade. These changes will mostly affect users with automated API calls around schema monitoring and data source registration.

2. Automated data source names: Previously, you could name data sources manually. However, data sources from connections are automatically named using the information (database, schema, table) from your data platform.

3. Schema projects phased out: With integrations, many settings and the connection info for data sources were controlled in the schema project. This functionality is no longer needed with connections and now you can control connection details in a central spot.

4. New hierarchy display: With integrations, tables were brought in as data sources and presented as a flat list on the data source list page. With connections, databases and schemas are displayed as objects too.

5. Change from schema monitoring to object sync: Object metadata synchronization between Immuta and your data platform is no longer optional but always required:

   1. If schema monitoring is off before the upgrade: Once the connection is registered, everything the system user can see will be pulled into Immuta and, if it didn't already exist in Immuta, it will be an inactive object. These inactive objects exist so you can see them, but policy is not protecting the objects, and they will not appear as data sources.

   2. If schema monitoring is on before the upgrade: Once the connection is registered, everything the system user can see will be pulled into Immuta. If it already existed in Immuta, it will be an active object and continue to appear as data source.

If you attempted the upgrade and receive the message that your upgrade is Partially Complete, find the un-upgraded data sources by navigating to the Upgrade Manager and clicking the number in the Available column for the relevant connection.

Use the options below to resolve those un-upgraded data sources in order to finish your upgrade. See the linked how-to's for more details on the actions to take.

Note that these un-upgraded data sources still exist and are still protected by policy.

1. : The easiest solution is to delete the data sources that did not upgrade. Note that disabled data sources that no longer exist in your data platform will never be upgraded. Only do this if you no longer need these data sources in Immuta.

2. Adjust the privileges of the system user used to connect Immuta and your data platform: Ensure that the Immuta system user can also access all remaining un-upgraded data sources in your data platform.

   1. Expand permissions in or (recommended): Extend the Immuta system user's permissions in your data platform by granting it access to all remaining un-upgraded data sources.

   2. : You can also provide Immuta with a different set of credentials that already have the required permissions on the un-upgraded data sources.

Required Snowflake permissions

Ensure that has at least the following permissions:

• USAGE on parent databases and schemas of objects registered as Immuta data sources

• REFERENCES on all objects registered as Immuta data sources

And has been granted to the .

Required Databricks Unity Catalog permissions

Ensure the Databricks service principal you created and connected with Immuta has at least the following permissions:

• USE CATALOG and USE SCHEMA on parent catalogs and schemas of objects registered as Immuta data sources

• SELECT and MODIFY on all objects registered as Immuta data sources

Delete the data sources

Expand permissions in Snowflake

Expand permissions in Databricks Unity Catalog

Change the system user credentials used by Immuta

If you have another set of credentials on hand with wider permissions, you can edit the connection to use these credentials instead to resolve the un-upgraded data sources.

Connections allow you to register your data objects in a technology through a single connection, making data registration more scalable for your organization. Instead of registering schema and databases individually, you can register them all at once and allow Immuta to monitor your data platform for changes so that data sources are added and removed automatically to reflect the state of data on your data platform.

Native integrations

Supported technology and authorization methods

Snowflake

• Snowflake OAuth

• Username and password

• Key pair

Databricks

• Token

*M2M OAuth is not yet supported.

Supported features

The tables below outline Immuta features, their availability with integrations, and their availability with connections.

Snowflake

Databricks Unity Catalog

Data sources

Supported object types

The supported object types are the same for both data sources with integrations and data sources with connections.

Snowflake

• Table

• View

• Materialized view

• External table

• Event table

• Iceberg table

• Dynamic table

Databricks Unity Catalog

• Table

• View

• Materialized view

• Streaming table

• External table

Hierarchy

With connections, your data sources are ingested and presented to reflect the infrastructure hierarchy of your connected data platform. For example, this is what the new hierarchy will look like for a Snowflake connection:

Tags

Consideration

The V2 /data endpoint allows users to register data sources and attach a tag automatically when the data sources are registered in Immuta.

The V2 /data endpoint is not supported with a connection, and there is no substitution for this behavior at this time. If you require default tags for newly onboarded data sources, please reach out to your Immuta support professional before upgrading.

Users and permissions

With integrations

With connections

Schema monitoring

During object sync, Immuta crawls your connection to ingest metadata for every database, schema, and table that the Snowflake role or Databricks account credentials you provided during the configuration has access to. Upon completion of the upgrade, the tables' states depend on your previous schema monitoring settings:

• If you had schema monitoring enabled on a schema: All tables from that schema will be registered in Immuta as active data sources.

• If you had schema monitoring disabled on a schema: All tables from that schema (that were not already registered in Immuta) will be registered as inactive data sources. They are visible from the infrastructure view, but are not listed as data sources until they are activated.

Additional settings

Object sync provides additional controls compared to schema monitoring:

• Object status: Connections, databases, schemas and tables can be marked active, which for tables make them appear as data sources, or inactive. These statuses are inherited to all lower objects by default, but that can be overridden. For example, if you make a database inactive, all schemas and tables within that database will also be inactive. However, if you want one of those tables to be a data source, you can manually activate it.

• Activate new data objects: This setting controls what state new objects are registered as in Immuta when found by object sync.

  • Active: New data objects found by object sync will automatically be activated and tables will be registered as data sources.

  • Inactive: This is the default. New data objects found by object sync will be inactive.

Comparison

Databricks Unity Catalog

APIs

Consolidating integration setup and data source registration into a single connection significantly simplifies programmatic interaction with the Immuta APIs. Actions that used to be managed through multiple different endpoints can now be achieved through one simple and standardized one. As a result, multiple API endpoints are blocked once a user has upgraded their connection.

When a data source is exposed, 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:

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.

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).

Requirement

CREATE_S3_DATA_SOURCE Immuta permission

Prerequisite

Configure the Amazon S3 integration

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 default domain 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.

Connections allow you to register your data objects in a technology through a single connection, making data registration more scalable for your organization. Instead of registering schema and databases individually, you can register them all at once and allow Immuta to monitor your data platform for changes so that data sources are added and removed automatically to reflect the state of data on your data platform.

How-to guides

• Register a Snowflake connection: Register a connection with a Snowflake account and register the data objects within it.

• Register a Databricks Unity Catalog connection: Register a connection with a Databricks Unity Catalog metastore and register the data objects within it.

• Crawl a connection or object: Trigger a manual crawl of the entire connection or a single object to sync your remote data platform objects with Immuta.

• Use the connection upgrade manager: Complete the upgrade path from the existing native integrations and data sources to a connection.

Reference guides

• Connections: This reference guide discusses the major concepts, design, and settings of connections.

• Upgrading to a connection: This reference guide discusses the differences when upgrading from the existing integrations and data sources to a connection.

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).

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

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 , 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 using the Google Cloud web console

1. • BigQuery User

   • BigQuery Data Viewer

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:

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:

   • 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.

9. Optional Advanced Settings:

   • 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

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

The 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

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).

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.

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 app settings page by a system administrator. However, with automatic table statistics disabled these policies will be unavailable until the data source owner manually generates the fingerprint:

• 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.

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

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

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

• Manage data source access requests: Approve and deny subscriptions requests on data source.

• Disable data sampling: Disable metadata collection that requires sampling data.

• Data dictionary: Manage the data dictionary descriptions and tags.

Reference guide

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

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

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 subscription policies

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

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 read access policies 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 read access policies on the data source.

   You can also opt to specify an expiration date for when the user’s access should expire.

4. Select Read or Write from the Access Grant dropdown. This option is only available if write policies 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

For information about data source members and subscriptions, see the data source user roles section.

How-to guides

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

• column tags

• data dictionaries

• 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.

As a data owner, you can edit your data source settings and disable, delete, and re-enable a data source.

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

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:

• Azure Synapse Analytics

• Databricks

• Redshift

• Snowflake

• Starburst (Trino)

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.