The shift from dbms to catalogs is already underway. Earlier, the databases were the veritable access grantors but with heterogenous data stores, this has shifted to catalogs like the Unity Catalog for databricks and the Horizon catalog for Snowflake. This is a deliberate attempt from the perspective of these platforms even though they fight for their ecosystems. The end-users and the organizations that empower them are rapidly making this shift themselves. 

For example, the Databricks Unity Catalog offers centralized access control, auditing, lineage, and data discovery capabilities across multiple Databricks workspaces. It includes user management, metastore, clusters, SQL warehouses, and a standards-compliant security model based on ANSI SQL. The catalog also includes built-in auditing and lineage, allowing for user-level audit logs and data discovery. The metadata store is a top-level container, while the data catalog has a three-level namespace namely catalog.schema.table. The catalog explorer allows for creation of tables and views, while the tables of views and volumes provide governance for nontabular data. The catalog is multi-cloud friendly, allowing for federation across multiple cloud vendors and unified access. The idea here is that you can define once and secure anywhere. 

Databricks Unity Catalog consists of a metastore and a catalog. The metastore is the top-level logical container for metadata, storing data assets like tables or models and defining the namespace hierarchy. It handles access control policies and auditing. The catalog is the first-level organizational unit within the metastore, grouping related data assets and providing access controls. However, only one metastore per deployment is used. Each Databricks region requires its own Unity Catalog metastore.  

There is a Unity catalog quick start notebook in Python. The key steps include creating a workspace with the Unity Catalog meta store, creating a catalog, creating a managed schema, managing a table, and using the Unity catalog in the Pandas API on Spark. The code starts with creating a catalog, selecting show, and then creating a managed schema. The next step involves creating and managing schemas, extending them, and granting permissions. The table is managed using the schema created earlier, and the table is shown and all available tables are shown. The final step involves using the Pandas API on Spark, which can be found in the official documentation for Databricks. This quick start is a great way to get a feel for the process and to toggle back and forth with the key steps inside the code. 

The Unity Catalog system employs object security best practices, including access control lists (ACLs) for granting or restricting access to specific users and groups on securable objects. ACLs provide fine-grain control, ensuring access to sensitive data and objects. Less privilege is used, limiting access to the minimum required, avoiding broad groups like All Users unless necessary. Access is revoked once the purpose is served, and policies are reviewed regularly for relevance. This technique enhances data security and compliance, prevents unnecessary broad access, and controls a blast radius in case of security breaches. 

The Databricks Unity Catalog system offers best practices for catalogs. First, create a separate catalog for loose coupling, managing access and compliance at the catalog level. Align catalog boundaries with business domains or applications, such as marketing analytics or HR. Customize security policies and governance within the catalog to drill down into specific domains. Create access control groups and roles specific to a catalog, fine-tune read-write privileges, and customize settings like resource quotas and scrum rules. These fine-grain policies provide the best of security and functionality in catalogs. 

To ensure security and manage external connections, limit visibility by granting access only to specific users, groups, and roles, and setting lease privileges. Limit access to only necessary users and groups using granular access control lists or ACLs. Be aware of team activities and avoid giving them unnecessary access to external resources. Tag connections effectively for discovery using source categories or data classifications, and discover connections by use case for organizational visibility. This approach enhances security, prevents unintended data access, and simplifies external connection discovery and management. 

Databricks Unity Catalog Business Unit Best Practices emphasize the importance of providing dedicated sandboxes for each business unit, allowing independent development environments, and preventing interference between different workflows. Centralizing shareable data into production catalogs ensures consistency and reduces the need for duplicate data. Discoverability is crucial, with meaningful naming conventions and metadata best practices. Federated queries via Lakehouse architecture unify data access across silos, governing securely via contracts and permissions. This approach supports autonomy for units, increases productivity through reuse, and maintains consistency with collaborative governance. This approach supports autonomy, increases productivity, and maintains consistency. 

In conclusion, the Unity catalog standard allows centralized data governance and best practices for catalogs, connections, and business units. 

https://docs.databricks.com/en/data-governance/unity-catalog/enable-workspaces.html#enable-workspace 

https://docs.databricks.com/en/data-governance/unity-catalog/create-metastore.html