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Snowflake\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/governance_with_horizon_header.png\" alt=\"assets/governance_with_horizon_header.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWelcome to the Powered by Tasty Bytes - Zero to Snowflake Quickstart focused on Data Governance!\u003C/p\u003E\n","\u003Cp\u003EWithin this Quickstart we will learn about Snowflake Roles, Role Based Access Control and deploy both Column and Row Level Security that can scale with your business.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBefore beginning, please make sure you have completed the \u003Ca href=\"/en/developers/guides/tasty-bytes-introduction/\"\u003E\u003Cstrong\u003EIntroduction to Tasty Bytes Quickstart\u003C/strong\u003E\u003C/a\u003E which provides a walkthrough on setting up a trial account and deploying the Tasty Bytes Foundation required to complete this Quickstart.\u003C/li\u003E\u003Cli\u003EPlease note that features leveraged within this Quickstart will require an Enterprise or Business Critical Snowflake account. Please see our documentation an \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-editions#overview-of-editions\"\u003E\u003Cstrong\u003EOverview of Snowflake Editions\u003C/strong\u003E\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhat System Defined Roles Exist in Snowflake Accounts\u003C/li\u003E\u003Cli\u003EHow to Create a Role\u003C/li\u003E\u003Cli\u003EHow to Grant Privileges to a Role\u003C/li\u003E\u003Cli\u003EHow to Create a Tag\u003C/li\u003E\u003Cli\u003EHow to Create a Masking Policy\u003C/li\u003E\u003Cli\u003EHow to Deploy a Tag Based Masking Policy\u003C/li\u003E\u003Cli\u003EHow to Create a Row Access Policy using a Mapping Table\u003C/li\u003E\u003Cli\u003EHow to Create an Aggregation Policy\u003C/li\u003E\u003Cli\u003EHow to Create a Project Policy\u003C/li\u003E\u003Cli\u003EHow to Leverage Automatic and Custom Data Classification\u003C/li\u003E\u003Cli\u003EHow to use Universal Search\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EComplete Role Based Access Control for a Test Role\u003C/li\u003E\u003Cli\u003EA Robust Data Governance Foundation for your Account\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ECreating a Worksheet and Copying in our SQL\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWithin this Quickstart we will follow a Tasty Bytes themed story via a Snowsight SQL Worksheet with this page serving as a side by side guide complete with additional commentary, images and documentation links.\u003C/p\u003E\n","\u003Cp\u003EThis section will walk you through logging into Snowflake, Creating a New Worksheet, Renaming the Worksheet, Copying SQL from GitHub, and Pasting the SQL we will be leveraging within this Quickstart.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Accessing Snowflake via URL\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EOpen a browser window and enter the URL of your Snowflake Account\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2 - Logging into Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ELog into your Snowflake account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 3 - Navigating to Worksheets\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the Projects Tab in the left-hand navigation bar and click Worksheets.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 4 - Creating a Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWithin Worksheets, click the "+" button in the top-right corner of Snowsight.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 5 - Renaming a Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERename the Worksheet by clicking on the auto-generated Timestamp name and inputting "Tasty Bytes - Governance"\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 6 - Accessing Quickstart SQL in GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick the button below which will direct you to our Tasty Bytes SQL file that is hosted on GitHub.\n<button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/FY25_Zero_To_Snowflake/tb_governance_snowflake_horizon.sql\"\u003Etb_zts_data_governance.sql\u003C/a\u003E</button>\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 7 - Copying Setup SQL from GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWithin GitHub navigate to the right side and click "Copy raw contents". This will copy all of the required SQL into your clipboard.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/github_copy_raw_contents.png\" alt=\"assets/github_copy_raw_contents.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 8 - Pasting Setup SQL from GitHub into your Snowflake Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPath back to Snowsight and your newly created Worksheet and Paste (\u003Cem\u003ECMD + V for Mac or CTRL + V for Windows\u003C/em\u003E) what we just copied from GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 9 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EExploring Available Roles\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EOur Tasty Bytes Administrator has been tasked with learning the process of deploying Role Based Access Control (RBAC) and proper Governance across our Snowflake Account.\u003C/p\u003E\n","\u003Cp\u003ETo begin, let's first dive into the Snowflake System Defined Roles provided by default in all accounts and learn a bit more on their privileges.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Setting our Context\u003C/h3\u003E\n","\u003Cp\u003EBefore we can begin executing queries within the Snowflake Snowsight interface we must first set our context by running \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/use-role\"\u003EUSE ROLE\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/use-warehouse\"\u003EUSE WAREHOUSE\u003C/a\u003E commands or manually setting these in the top-right corner.\u003C/p\u003E\n","\u003Cp\u003EFor this step, we will do this by executing our first two queries by highlighting them both and clicking the "▶ Run" button.\u003C/p\u003E\n","\u003Cp\u003EOnce complete our results pane will result in a \u003Ccode\u003EStatement Executed Successfully\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE accountadmin;\nUSE WAREHOUSE tb_dev_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith our context set, we can continue on our learning journey.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Exploring All Roles in our Account\u003C/h3\u003E\n","\u003Cp\u003ENow let's run the next query which leverages \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/show-roles\"\u003ESHOW ROLES\u003C/a\u003E to provide a result set consisting of all roles currently deployed in our account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW ROLES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you are operating in a trial account with only Tasty Bytes deployed you will only see a few \u003Ccode\u003Etb_\u003C/code\u003E roles, however if you are using an existing Snowflake account your list may be more extensive.\u003C/p\u003E\n","\u003Cp\u003EThankfully we can filter down this result set which we will cover in the next step.\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Using Result Scan to Filter our Result\u003C/h3\u003E\n","\u003Cp\u003ETo filter on just the Snowflake System Defined Roles from our previous output please execute the next query which utilizes \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/result_scan\"\u003ERESULT_SCAN\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/last_query_id\"\u003ELAST_QUERY_ID\u003C/a\u003E to query our previous SHOW ROLES results as if they were a table giving us the ability to add a WHERE clause.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    "name",\n    "comment"\nFROM TABLE(RESULT_SCAN(LAST_QUERY_ID()))\nWHERE "name" IN ('ORGADMIN','ACCOUNTADMIN','SYSADMIN','USERADMIN','SECURITYADMIN','PUBLIC');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/sysroles.png\" alt=\"assets/sysroles.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn our result set we can see the high-level descriptions of what these Snowflake System Defined Roles have privileges to do.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003ENote:\u003C/strong\u003E For additional details on these, please the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-access-control-overview#system-defined-roles\"\u003ESnowflake System Defined Roles\u003C/a\u003E documentation.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003ECreating a Role and Granting Privileges\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003ENow that we understand these System Defined roles, let's begin leveraging them to create a test role and grant it access to the Customer Loyalty data we will deploy our initial Data Governance features against and also providing the ability to use our \u003Ccode\u003Etb_dev_wh\u003C/code\u003E Warehouse.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Using the Useradmin Role to Create our Test Role\u003C/h3\u003E\n","\u003Cp\u003EAs we saw, a \u003Ccode\u003Euseradmin\u003C/code\u003E can create and manage users and roles. Please kick off the next two queries with the first assuming that \u003Ccode\u003Euseradmin\u003C/code\u003E role and the second leveraging a \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-role\"\u003ECREATE ROLE\u003C/a\u003E command to generate a new \u003Ccode\u003Etb_test_role\u003C/code\u003E we will use throughout this Quickstart. This will result in a \u003Ccode\u003ERole TB_TEST_ROLE successfully created.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE useradmin;\n\nCREATE OR REPLACE ROLE tb_test_role\n    COMMENT = 'test role for tasty bytes';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Using the Securityadmin Role to Grant Warehouse Privileges\u003C/h3\u003E\n","\u003Cp\u003EWith our \u003Ccode\u003Etb_test_role\u003C/code\u003E in place, we can now begin to use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/grant-privilege\"\u003EGRANT\u003C/a\u003E statements to give access privileges to required Database objects and our \u003Ccode\u003Etb_dev_wh\u003C/code\u003E Warehouse.\u003C/p\u003E\n","\u003Cp\u003ETo begin, please run the next three queries which will first assume the \u003Ccode\u003Esecurityadmin\u003C/code\u003E role that we learned can manage security aspects in our account, next we will grant ALL privileges on this Warehouse to our \u003Ccode\u003Esysadmin\u003C/code\u003E  and finally grant OPERATE and USAGE on our \u003Ccode\u003Etb_dev_wh\u003C/code\u003E to our \u003Ccode\u003Etb_test_role\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EWhen completed we will recieve a \u003Ccode\u003EStatement executed successfully.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE securityadmin;\n\nGRANT ALL ON WAREHOUSE tb_dev_wh TO ROLE sysadmin;\n\nGRANT OPERATE, USAGE ON WAREHOUSE tb_dev_wh TO ROLE tb_test_role;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor more on Snowflake Warehouse Privilege Grants please see below:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EMODIFY:\u003C/strong\u003E Enables altering any properties of a warehouse, including changing its size.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMONITOR:\u003C/strong\u003E Enables viewing current and past queries executed on a warehouse as well as usage statistics on that warehouse.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOPERATE:\u003C/strong\u003E Enables changing the state of a warehouse (stop, start, suspend, resume). In addition, enables viewing current and past queries executed on a warehouse and aborting any executing queries.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUSAGE:\u003C/strong\u003E Enables using a virtual warehouse and, as a result, executing queries on the warehouse. If the warehouse is configured to auto-resume when a SQL statement is submitted to it, the warehouse resumes automatically and executes the statement.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EALL:\u003C/strong\u003E Grants all privileges, except OWNERSHIP, on the warehouse.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3 - Using the Securityadmin Role to Grant Database and Schema Privileges\u003C/h3\u003E\n","\u003Cp\u003EWith the Warehouse privileges in place, please execute the next two queries which will provide the \u003Ccode\u003Etb_test_role\u003C/code\u003E with the USAGE privilege on the \u003Ccode\u003Etb_101\u003C/code\u003E database and all schemas within.\u003C/p\u003E\n","\u003Cp\u003EOnce again when completed we will recieve a \u003Ccode\u003EStatement executed successfully. X objects affected.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGRANT USAGE ON DATABASE tb_101 TO ROLE tb_test_role;\nGRANT USAGE ON ALL SCHEMAS IN DATABASE tb_101 TO ROLE tb_test_role;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor more on Snowflake Database and Schema Grants please see below:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EMODIFY:\u003C/strong\u003E Enables altering any settings of a database.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMONITOR:\u003C/strong\u003E Enables performing the DESCRIBE command on the database.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUSAGE:\u003C/strong\u003E Enables using a database, including returning the database details in the SHOW DATABASES command output. Additional privileges are required to view or take actions on objects in a database.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EALL:\u003C/strong\u003E Grants all privileges, except OWNERSHIP, on a database.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 4 - Using the Securityadmin Role to Grant Table and View Privileges\u003C/h3\u003E\n","\u003Cp\u003EAs we will be testing several Data Governance features as our \u003Ccode\u003Etb_test_role\u003C/code\u003E let's ensure it can run SELECT statements across our entire Data Model.\u003C/p\u003E\n","\u003Cp\u003EPlease now execute the next three queries of this section which will once again result in a \u003Ccode\u003EStatement executed successfully. X objects affected.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGRANT SELECT ON ALL TABLES IN SCHEMA tb_101.raw_customer TO ROLE tb_test_role;\nGRANT SELECT ON ALL TABLES IN SCHEMA tb_101.raw_pos TO ROLE tb_test_role;\nGRANT SELECT ON ALL VIEWS IN SCHEMA tb_101.analytics TO ROLE tb_test_role;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor more on Snowflake View and Table Privilege Grants please see below:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003ESELECT:\u003C/strong\u003E Enables executing a SELECT statement on a table/view. Note that this privilege is sufficient to query a view.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EINSERT:\u003C/strong\u003E Enables executing an INSERT command on a table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUPDATE:\u003C/strong\u003E Enables executing an UPDATE command on a table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETRUNCATE:\u003C/strong\u003E Enables executing a TRUNCATE TABLE command on a table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDELETE:\u003C/strong\u003E Enables executing a DELETE command on a table.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 5 - Using the Securityadmin Role to Grant our Role to our User\u003C/h3\u003E\n","\u003Cp\u003EWith our \u003Ccode\u003Etb_test_role\u003C/code\u003E properly privileged, let's now execute the last two queries of this section. Here we will first \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/set\"\u003ESET\u003C/a\u003E a SQL Variable called \u003Ccode\u003Emy_user_var\u003C/code\u003E equal to our \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/current_user\"\u003ECURRENT_USER\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe second query will then grant our role to our user we are logged in as which has been stored in our \u003Ccode\u003E$my_user_var\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EAs we've seen before, our query will result in a \u003Ccode\u003EStatement executed successfully\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESET my_user_var  = CURRENT_USER();\nGRANT ROLE tb_test_role TO USER identifier($my_user_var);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAwesome!\u003C/strong\u003E We now have a \u003Ccode\u003Etb_test_role\u003C/code\u003E with required Database Object and Warehouse privileges and our user is able to leverage it.\u003C/p\u003E\n","\u003Ch3\u003EStep 6 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EColumn-Level Security and Tagging = Tag-Based Masking\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EThe first Governance feature set we want to deploy and test will be Snowflake Tag Based Dynamic Data Masking. This will allow us to mask PII data in columns from our Test Role but not from more privileged Roles.\u003C/p\u003E\n","\u003Cp\u003EBefore we can begin masking data, let's first explore what PII exists in our Customer Loyalty data.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Finding our PII Columns\u003C/h3\u003E\n","\u003Cp\u003EThanks to our last step, we can now officially use our \u003Ccode\u003Etb_test_role\u003C/code\u003E Role and leverage the \u003Ccode\u003Etb_dev_wh\u003C/code\u003E Warehouse from that role.\u003C/p\u003E\n","\u003Cp\u003EPlease execute the next four queries which will first handle setting our context. With the context in place the fourth query will explore our \u003Ccode\u003Eraw_customer.customer_loyalty\u003C/code\u003E which we ingest from the Tasty Bytes Customer Loyalty Program provider.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tb_test_role;\nUSE WAREHOUSE tb_dev_wh;\nUSE DATABASE tb_101;\n\nSELECT\n    cl.customer_id,\n    cl.first_name,\n    cl.last_name,\n    cl.e_mail,\n    cl.phone_number,\n    cl.city,\n    cl.country,\n    cl.sign_up_date,\n    cl.birthday_date\nFROM raw_customer.customer_loyalty cl \nSAMPLE (1000 ROWS);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/customer_loyalty.png\" alt=\"assets/customer_loyalty.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWoah!!\u003C/strong\u003E there is a lot of PII we need to take care before our users can touch this data. Luckily we can use Snowflakes native Tag-Based Masking functionality to do just this.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nA \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tag-based-masking-policies\"\u003ETag-based Masking Policy\u003C/a\u003E combines the object tagging and masking policy features to allow a masking policy to be set on a tag using an ALTER TAG command. When the data type in the masking policy signature and the data type of the column match, the tagged column is automatically protected by the conditions in the masking policy.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 - Creating our Schemas\u003C/h3\u003E\n","\u003Cp\u003ETo keep ourselves organized and to follow best practices, let's now create and privilege \u003Ccode\u003ETags\u003C/code\u003E and \u003Ccode\u003EGovernance\u003C/code\u003E schemas within our Database by executing the next five queries.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE accountadmin;\n\nCREATE OR REPLACE SCHEMA tags\n    COMMENT = 'Schema containing Object Tags';\n\nGRANT USAGE ON SCHEMA tags TO ROLE public;\n\nCREATE OR REPLACE SCHEMA governance\n    COMMENT = 'Schema containing Security Policies';\n\nGRANT ALL ON SCHEMA governance TO ROLE sysadmin;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3 - Creating our Tag\u003C/h3\u003E\n","\u003Cp\u003ETo begin our masking process, please run the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-tag\"\u003ECREATE TAG\u003C/a\u003E query below.\u003C/p\u003E\n","\u003Cp\u003EWithin this query we are creating a \u003Ccode\u003Etasty_pii\u003C/code\u003E Tag for these values: NAME, PHONE_NUMBER, EMAIL, BIRTHDAY. Not only will this tag prevent free text values, but it will also add the Tag to the selection menu to Snowsight. This will result in a \u003Ccode\u003ETag TASTY_PII successfully created.\u003C/code\u003E result.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE TAG tags.tasty_pii\n    ALLOWED_VALUES 'NAME', 'PHONE_NUMBER', 'EMAIL', 'BIRTHDAY'\n    COMMENT = 'Tag for PII, allowed values are: NAME, PHONE_NUMBER, EMAIL, BIRTHDAY';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nTags enable data stewards to track sensitive data for compliance, discovery, protection, and resource usage use cases through either a centralized or decentralized data governance management approach.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 4 - Applying Tags\u003C/h3\u003E\n","\u003Cp\u003EWith the Tag in place, let's now execute the next query which uses \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/alter-table-column\"\u003EALTER TABLE... MODIFY COLUMN\u003C/a\u003E to set our tag to each respective column in the \u003Ccode\u003Ecustomer_loyalty\u003C/code\u003E table. This will result in a \u003Ccode\u003EStatement executed successfully.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE raw_customer.customer_loyalty\n    MODIFY COLUMN \n    first_name SET TAG tags.tasty_pii = 'NAME',\n    last_name SET TAG tags.tasty_pii = 'NAME',\n    phone_number SET TAG tags.tasty_pii = 'PHONE_NUMBER',\n    e_mail SET TAG tags.tasty_pii = 'EMAIL',\n    birthday_date SET TAG tags.tasty_pii = 'BIRTHDAY';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 5 - Exploring Tags on a Table\u003C/h3\u003E\n","\u003Cp\u003EWith our Tag created and applied to Columns, please kick off the next query where we leverage the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/tag_references_all_columns\"\u003ETAG_REFERENCES_ALL_COLUMNS\u003C/a\u003E function to validate the work we just completed.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    tag_database,\n    tag_schema,\n    tag_name,\n    column_name,\n    tag_value\nFROM TABLE(information_schema.tag_references_all_columns\n    ('tb_101.raw_customer.customer_loyalty','table'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/tag_ref_all.png\" alt=\"assets/tag_ref_all.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPerfect!\u003C/strong\u003E Just as desired, we see our created tag is associated to the PII columns we will look to mask in the next section.\u003C/p\u003E\n","\u003Ch3\u003EStep 6 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECreating Masking Policies and Applying to Tags\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWith our Tags in place we can now create our Masking Policies that will mask data for all but privileged Roles. We need to create 1 policy for every data type where the return data type can be implicitly cast into the column datatype. We can only assign 1 policy per datatype to an individual Tag.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Creating our first Data Masking Policy\u003C/h3\u003E\n","\u003Cp\u003EFor the three different column types we are looking to mask, we want to address the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENames:\u003C/strong\u003E Complete masking for any role that is not \u003Ccode\u003Eaccountadmin\u003C/code\u003E or \u003Ccode\u003Esysadmin\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPhone Numbers\u003C/strong\u003E: Partial masking with only the first three numbers exposed to any role that is not \u003Ccode\u003Eaccountadmin\u003C/code\u003E or \u003Ccode\u003Esysadmin\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EE-mail Addresses\u003C/strong\u003E: Partial masking with only the email provider information available to any role that is not \u003Ccode\u003Eaccountadmin\u003C/code\u003E or \u003Ccode\u003Esysadmin\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn this step we will create our tasty_pii_string_mask Masking Policy. Within the policy we will see the use of \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-masking-policy\"\u003ECREATE MASKING POLICY\u003C/a\u003E as well as the other Snowflake functions required to complete our masking including \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/current_role\"\u003ECURRENT_ROLE\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/concat\"\u003ECONCAT\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/left\"\u003ELEFT\u003C/a\u003E, and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/split_part\"\u003ESPLIT_PART\u003C/a\u003E. This query will result in a \u003Ccode\u003EMasking policy TASTY_PII_STRING_MASK successfully created.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE MASKING POLICY governance.tasty_pii_string_mask AS (val STRING) RETURNS STRING ->\n    CASE\n        -- these active roles have access to unmasked values \n        WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN')\n            THEN val \n        -- if a column is tagged with TASTY_PII : PHONE_NUMBER \n        -- then mask everything but the first 3 digits   \n        WHEN SYSTEM$GET_TAG_ON_CURRENT_COLUMN('TAGS.TASTY_PII') = 'PHONE_NUMBER'\n            THEN CONCAT(LEFT(val,3), '-***-****')\n        -- if a column is tagged with TASTY_PII : EMAIL  \n        -- then mask everything before the @ sign  \n        WHEN SYSTEM$GET_TAG_ON_CURRENT_COLUMN('TAGS.TASTY_PII') = 'EMAIL'\n            THEN CONCAT('**~MASKED~**','@', SPLIT_PART(val, '@', -1))\n        -- all other conditions should be fully masked   \n    ELSE '**~MASKED~**' \nEND;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Addressing Re-Identification\u003C/h3\u003E\n","\u003Cp\u003EThe combination of an individuals City, first 3 Phone Number digits, and Birthday to re-identify them. Let's play it safe and also truncate Birthdays into 5 year buckets which will fit the use case of our Analyst. Run the next query to create our Date Masking Policy to return modified Birthdays. This will return \u003Ccode\u003EMasking policy TASTY_PII_DATE_MASK successfully created.\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE MASKING POLICY governance.tasty_pii_date_mask AS (val DATE) RETURNS DATE ->\n    CASE\n        -- these active roles have access to unmasked values \n        WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN')\n            THEN val\n        -- if a column is tagged with TASTY_PII : BIRTHDAY  \n        -- then truncate to 5 year buckets \n        WHEN SYSTEM$GET_TAG_ON_CURRENT_COLUMN('TAGS.TASTY_PII') = 'BIRTHDAY'\n            THEN DATE_FROM_PARTS(YEAR(val) - (YEAR(val) % 5),1,1)\n        -- if a Date column is not tagged with BIRTHDAY, return NULL\n    ELSE NULL \nEND;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3 - Set Masking Policies on Tagged Columns\u003C/h3\u003E\n","\u003Cp\u003ENow we are able to use an ALTER TAG statement to set the Masking Policies on the PII tagged columns Please now execute the query in this step, which applies each Masking Policy to the appropriate Tag using \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/alter-tag\"\u003EALTER TAG... SET MASKING POLICY\u003C/a\u003E command.\u003C/p\u003E\n","\u003Cp\u003EThis will result in a \u003Ccode\u003EStatement executed successfully.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TAG tags.tasty_pii SET\n    MASKING POLICY governance.tasty_pii_string_mask,\n    MASKING POLICY governance.tasty_pii_date_mask;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can now officially say we have deployed Tag Based Masking Policies. Let's move on to the next step where we will validate our work.\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Validating our Work\u003C/h3\u003E\n","\u003Cp\u003EWith Tag Based Masking in-place, let's check things out by first assuming our Test Role and using our Development Warehouse and then beginning to query both raw and downstream data.\u003C/p\u003E\n","\u003Cp\u003EWithin our first query we will query the Raw layer Customer Loyalty table directly.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tb_test_role;\nUSE WAREHOUSE tb_dev_wh;\n\nSELECT\n    cl.customer_id,\n    cl.first_name,\n    cl.last_name,\n    cl.phone_number,\n    cl.e_mail,\n    cl.birthday_date,\n    cl.city,\n    cl.country\nFROM raw_customer.customer_loyalty cl\nWHERE cl.country IN ('United States','Canada','Brazil');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/raw_mask_test.png\" alt=\"assets/raw_mask_test.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESince we reference this \u003Ccode\u003Eraw_customer.customer_loyalty\u003C/code\u003E Table in downstream Views let's see if this Masking Policy impacts us there as well. Please kick off the next query which calculates our Customer Loyalty members \u003Ccode\u003Elifetime_sales_usd\u003C/code\u003E totals and sorts them in descending order by this amount.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT TOP 10\n    clm.customer_id,\n    clm.first_name,\n    clm.last_name,\n    clm.phone_number,\n    clm.e_mail,\n    SUM(clm.total_sales) AS lifetime_sales_usd\nFROM analytics.customer_loyalty_metrics_v clm\nWHERE clm.city = 'San Mateo'\nGROUP BY clm.customer_id, clm.first_name, clm.last_name, clm.phone_number, clm.e_mail\nORDER BY lifetime_sales_usd;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/analytics_mask_test.png\" alt=\"assets/analytics_mask_test.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 5 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003ERow-Access Policies\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EHappy with our Tag Based Dynamic Masking controlling masking at the Column level,\nwe will now look to restrict access at the Row level for our Test Role.\u003C/p\u003E\n","\u003Cp\u003EWithin our Customer Loyalty table, our Test Role should only see Customers who are\nbased in Tokyo. Thankfully, Snowflake has another powerful native Data Governance\nfeature that can handle this at scale called \u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-row-intro\"\u003ERow Access Policies\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor our use case, we will leverage the mapping table approach.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nSnowflake supports row-level security through the use of Row Access Policies to determine which rows to return in the query result. The row access policy can be relatively simple to allow one particular role to view rows, or be more complex to include a mapping table in the policy definition to determine access to rows in the query result.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1 - Creating a Mapping Table\u003C/h3\u003E\n","\u003Cp\u003ETo begin our Row Level Security journey we will start by creating a \u003Ccode\u003Erow_policy_map\u003C/code\u003E table that will serve as our Mapping Table which a Row Access Policy condition can reference to filter the query result set.\u003C/p\u003E\n","\u003Cp\u003ELet's now kick off the first two queries in this section which will assume the \u003Ccode\u003Eaccountadmin\u003C/code\u003E Role and create our Mapping Table that will accept Roles and the City they should have permission to see. This will result in \u003Ccode\u003ETable ROW_POLICY_MAP successfully created.\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE accountadmin;\n\nCREATE OR REPLACE TABLE governance.public.row_policy_map\n    (role STRING, city_permissions STRING);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Inserting Mapping Records\u003C/h3\u003E\n","\u003Cp\u003EWith our \u003Ccode\u003Erow_policy_map\u003C/code\u003E in place, please kick off the next query which will \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/insert\"\u003EINSERT\u003C/a\u003E a record that maps our \u003Ccode\u003Etb_test_role\u003C/code\u003E to Tokyo. This query will result in \u003Ccode\u003Enumber of rows insterted: 1\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EINSERT INTO governance.row_policy_map\n    VALUES ('TB_TEST_ROLE','Tokyo'); \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3 - Creating a Row Access Policy\u003C/h3\u003E\n","\u003Cp\u003ENow that we have a record in our \u003Ccode\u003Erow_policy_map\u003C/code\u003E Table, we can kick off our next query to create our Row Access Policy.\u003C/p\u003E\n","\u003Cp\u003EWithin this query we are using \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-row-access-policy\"\u003ECREATE ROW ACCESS POLICY\u003C/a\u003E that will allow our Admin and Tasty Workload Roles to see all rows but if a user is using a Role that is seen in our Mapping Table they will only be allowed to see the rows relevant to the City they are mapped to. After executing this query you will recieve a \u003Ccode\u003ERow access policy 'CUSTOMER_CITY_ROW_POLICY' is successfully created.\u003C/code\u003E result.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE ROW ACCESS POLICY governance.customer_city_row_policy\n    AS (city STRING) RETURNS BOOLEAN ->\n       CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN') -- list of roles that will not be subject to the policy\n        OR EXISTS -- this clause references our mapping table from above to handle the row level filtering\n            (\n            SELECT rp.role\n                FROM governance.row_policy_map rp\n            WHERE 1=1\n                AND rp.role = CURRENT_ROLE()\n                AND rp.city_permissions = city\n            )\nCOMMENT = 'Policy to limit rows returned based on mapping table of ROLE and CITY: governance.row_policy_map';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4 - Applying a Row Access Policy to a Table\u003C/h3\u003E\n","\u003Cp\u003EPlease kick off the next query which will associate our Row Access Policy to our \u003Ccode\u003Ecity\u003C/code\u003E Column within the \u003Ccode\u003Ecustomer_loyalty\u003C/code\u003E table.\u003C/p\u003E\n","\u003Cp\u003EOnce complete we will recieve a \u003Ccode\u003EStatement executed successfully.\u003C/code\u003E message indicating our policy is applied.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE raw_customer.customer_loyalty\n    ADD ROW ACCESS POLICY governance.customer_city_row_policy ON (city);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 5 - Testing our Row Access Policy in a Non-Privileged Role\u003C/h3\u003E\n","\u003Cp\u003EWith everything in place, we can begin to test that our Row Access Policy is operating as expected. Please now kick off the next two queries which set our \u003Ccode\u003Etb_test_role\u003C/code\u003E Role context and query the \u003Ccode\u003Ecustomer_loyalty\u003C/code\u003E table to return a list of our Customer Loyalty members including a calculated \u003Ccode\u003Eage\u003C/code\u003E Column that leverages \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/datediff\"\u003EDATEDIFF\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/current_date\"\u003ECURRENT_DATE\u003C/a\u003E functions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tb_test_role;\n\nSELECT\n    cl.customer_id,\n    cl.first_name,\n    cl.last_name,\n    cl.city,\n    cl.marital_status,\n    DATEDIFF(year, cl.birthday_date, CURRENT_DATE()) AS age\nFROM raw_customer.customer_loyalty cl SAMPLE (10000 ROWS)\nGROUP BY cl.customer_id, cl.first_name, cl.last_name, cl.city, cl.marital_status, age;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/raw_rls_test.png\" alt=\"assets/raw_rls_test.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYay!\u003C/strong\u003E Our Row Access Policy is working as expected and we are also seeing our masking in place since we are using the \u003Ccode\u003Etb_test_role\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 6 - Testing our Row Access Policy Downstream\u003C/h3\u003E\n","\u003Cp\u003EAs we did previously, let's now confirm our Row Access Policy is in effect when we query our downstream Analytics \u003Ccode\u003Ecustomer_loyalty_metrics_v\u003C/code\u003E View by running the next query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    clm.city,\n    SUM(clm.total_sales) AS total_sales_usd\nFROM analytics.customer_loyalty_metrics_v clm\nGROUP BY clm.city;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/analytics_rls_test.png\" alt=\"assets/analytics_rls_test.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EJust as expected, our Governance features are seen downstream despite only having to define them once and apply them to the Raw Tables our Analytics Views retrieve query results from.\u003C/p\u003E\n","\u003Ch3\u003EStep 7 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EAggregation Policies\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EOutside of the Data Access Policies (Masking and Row Access) we have covered, Snowflake Horizon also provides \u003Ca href=\"https://docs.snowflake.com/en/user-guide/aggregation-policies\"\u003EPrivacy Policies\u003C/a\u003E. In this section we will cover the ability to set Aggregation Policies on Database Objects which can restrict certain roles to only aggregate data by only allowing for queries that aggregate data into groups of a minimum size versus retrieving individual roles.\u003C/p\u003E\n","\u003Cp\u003EFor Tasty Bytes and the Test role we have created, let's test an Aggregation Policy ut against our Raw Order Header table.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\u003C/p\u003E\n","\u003Cp\u003EAn \u003Cstrong\u003EAggregation Policy\u003C/strong\u003E is a schema-level object that controls what type of query can access data from a table or view. When an aggregation policy is applied to a table, queries against that table must aggregate data into groups of a minimum size in order to return results, thereby preventing a query from returning information from an individual record.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1 - Creating a Conditional Aggregation Policy\u003C/h3\u003E\n","\u003Cp\u003ETo begin, let's once again assume our \u003Ccode\u003EAccountadmin\u003C/code\u003E role. For our use case, we will create a Conditional Aggregation Policy in our \u003Ccode\u003EGovernance\u003C/code\u003E Schema that will only allow queries from non-admin users to return results for queries that aggregate more than 1000 rows. Please kick off the next two queries which will result \u003Ccode\u003EAggregation policy 'TASTY_ORDER_TEST_AGGREGATION_POLICY' is successfuly created/\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE accountadmin;\n\nCREATE OR REPLACE AGGREGATION POLICY governance.tasty_order_test_aggregation_policy\n  AS () RETURNS AGGREGATION_CONSTRAINT ->\n    CASE\n      WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN')\n      THEN NO_AGGREGATION_CONSTRAINT()  \n      ELSE AGGREGATION_CONSTRAINT(MIN_GROUP_SIZE => 1000) -- atleast 1000 rows in aggregate\n    END;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Setting our Aggregation Policy on a Table\u003C/h3\u003E\n","\u003Cp\u003EWith the Aggregation Policy created, let's apply it to our Order Header table in the next query which will return a \u003Ccode\u003EStatement executed successully.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE raw_pos.order_header\n    SET AGGREGATION POLICY governance.tasty_order_test_aggregation_policy;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3 - Testing our Aggregation Policy\u003C/h3\u003E\n","\u003Cp\u003ENow let's test our work by assuming our Test Role and executing a few queries against the \u003Ccode\u003Eorder_header\u003C/code\u003E table. Please execute the next two queries which will set our role and run a TOP 10 SELECT statement from the table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tb_test_role;\n\nSELECT TOP 10 * FROM raw_pos.order_header;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/agg_test_fail.png\" alt=\"assets/agg_test_fail.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERun the next query to see what happens if we include over 1000 rows.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/agg_test_fail.png\" alt=\"assets/agg_test_fail.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Conducting Aggregate Analysis\u003C/h3\u003E\n","\u003Cp\u003EBringing in the Customer Loyalty table that we have previously:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDeployed Masking against PII columns\u003C/li\u003E\u003Cli\u003EDeployed Row Level Security to restrict our Test Role to only Tokyo results\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELet's answer a few aggregate questions that the business has presented to our Analyst.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuestion 1\u003C/strong\u003E - What are the total order amounts by gender?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    cl.gender,\n    cl.city,\n    COUNT(oh.order_id) AS count_order,\n    SUM(oh.order_amount) AS order_total\nFROM raw_pos.order_header oh\nJOIN raw_customer.customer_loyalty cl\n    ON oh.customer_id = cl.customer_id\nGROUP BY ALL\nORDER BY order_total DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/agg_1.png\" alt=\"assets/agg_1.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuestion 2\u003C/strong\u003E - What are the total order amounts by Postal Code?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    cl.postal_code,\n    cl.city,\n    COUNT(oh.order_id) AS count_order,\n    SUM(oh.order_amount) AS order_total\nFROM raw_pos.order_header oh\nJOIN raw_customer.customer_loyalty cl\n    ON oh.customer_id = cl.customer_id\nGROUP BY ALL\nORDER BY order_total DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/agg_2.png\" alt=\"assets/agg_2.png\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nNote: If the query returns a group that contains fewer records than the minimum group size of the policy, then Snowflake combines those groups into a remainder group.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EProjection Policies\u003C/h2\u003E\n","\u003Ch3\u003EOverview:\u003C/h3\u003E\n","\u003Cp\u003EWithin this step, we will cover another Privacy Policy framework provided by Snowflake Horizon, this time diving into \u003Ca href=\"https://docs.snowflake.com/en/user-guide/projection-policies\"\u003EProjection Policies\u003C/a\u003E which in short will prevent queries from using a SELECT statement to project values from a column.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Creating a Conditional Projection Policy\u003C/h3\u003E\n","\u003Cp\u003EFor our use case, we will create a Conditional Projection Policy in our \u003Ccode\u003EGovernance\u003C/code\u003E Schema that will only allow our Admin Roles to project the columns we will assign it to. Please execute the next two queries which will assume our \u003Ccode\u003Eaccountadmin\u003C/code\u003E Role and create our \u003Ccode\u003Etasty_customer_test_projection_policy\u003C/code\u003E Projection Policy. This will result in \u003Ccode\u003EProjection policy 'TASTY_CUSTOMER_TEST_PROJECTION_POLICY' is successfully created.\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE accountadmin;\n\nCREATE OR REPLACE PROJECTION POLICY governance.tasty_customer_test_projection_policy\n  AS () RETURNS PROJECTION_CONSTRAINT -> \n  CASE\n    WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN')\n    THEN PROJECTION_CONSTRAINT(ALLOW => true)\n    ELSE PROJECTION_CONSTRAINT(ALLOW => false)\n  END;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nA projection policy is a first-class, schema-level object that defines whether a column can be projected in the output of a SQL query result. A column with a projection policy assigned to it is said to be projection constrained.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 - Applying our Projection Policy to our Postal Code column\u003C/h3\u003E\n","\u003Cp\u003EWith the Projection Policy in place, let's assign it to our Postal Code column by executing the query below. This will result in a \u003Ccode\u003EStatement executed successfully.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE raw_customer.customer_loyalty\n MODIFY COLUMN postal_code\n SET PROJECTION POLICY governance.tasty_customer_test_projection_policy; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3 - Testing our Projection Policy\u003C/h3\u003E\n","\u003Cp\u003ENow we can move on to testing our Projection Policy by first assuming our \u003Ccode\u003Etb_test_role\u003C/code\u003E Role and running a SELECT TOP 100 * query against our \u003Ccode\u003Ecustomer_loyalty\u003C/code\u003E table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tb_test_role;\n\nSELECT TOP 100 * FROM raw_customer.customer_loyalty;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/projection_fail.png\" alt=\"assets/projection_fail.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's execute the next query to see what happens if we EXCLUDE the \u003Ccode\u003Epostal_code\u003C/code\u003E column.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT TOP 100 * EXCLUDE postal_code FROM raw_customer.customer_loyalty;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/projection_no_postal.png\" alt=\"assets/projection_no_postal.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003ESensitive Data Classification\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn some cases, you may not know if there is sensitive data in a table. Snowflake Horizon provides the capability to attempt to automatically detect sensitive information and apply relevant Snowflake system defined privacy tags.\u003C/p\u003E\n","\u003Cp\u003EClassification is a multi-step process that associates Snowflake-defined system tags to columns by analyzing the fields and metadata for personal data. Data  Classification can be done via SQL or the Snowsight interface.\u003C/p\u003E\n","\u003Cp\u003EWithin this step we will be using SQL to classify a single table as well as all tables within a schema.\u003C/p\u003E\n","\u003Cp\u003ETo learn how to complete Data Classification within the Snowsight interface, please see the following documentation:\u003C/p\u003E\n","\u003Cp\u003EUsing Snowsight to classify tables in a schema\n• https://docs.snowflake.com/en/user-guide/governance-classify-using#using-sf-web-interface-to-classify-tables-in-a-schema\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - SYSTEM$CLASSIFY\u003C/h3\u003E\n","\u003Cp\u003EAs our Raw Customer Schema only includes one table, let's use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/stored-procedures/system_classify\"\u003ESYSTEM$CLASSIFY\u003C/a\u003E against it after we assume our \u003Ccode\u003Eaccountadmin\u003C/code\u003E role. Please execute the next two queries.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE accountadmin;\n\nCALL SYSTEM$CLASSIFY('raw_customer.customer_loyalty', {'auto_tag': true});\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/system$classify.png\" alt=\"assets/system$classify.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow let's view the new Tags Snowflake applied automatically via Data Classification by executing the next query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT * FROM TABLE(information_schema.tag_references_all_columns('raw_customer.customer_loyalty','table'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/system$classify_info.png\" alt=\"assets/system$classify_info.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - SYSTEM$CLASSIFY_SCHEMA\u003C/h3\u003E\n","\u003Cp\u003EAs our Raw Point-of-Sale Schema includes numerous tables, let's use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/stored-procedures/system_classify_schema\"\u003ESYSTEM$CLASSIFY_SCHEMA\u003C/a\u003E against it in our next query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECALL SYSTEM$CLASSIFY_SCHEMA('raw_pos', {'auto_tag': true});\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/system$classify_schema.png\" alt=\"assets/system$classify_schema.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce again, let's view the Tags applied using the Franchise table within the Schema.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT * FROM TABLE(information_schema.tag_references_all_columns('raw_pos.franchise','table'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/system$classify_schema_info.png\" alt=\"assets/system$classify_schema_info.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECustom Data Classification\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003ESnowflake provides the CUSTOM_CLASSIFIER class in the SNOWFLAKE.DATA_PRIVACY schema to enable Data Engineers to extend their Data Classification capabilities based on their own knowledge of their data.\u003C/p\u003E\n","\u003Cp\u003EIn this step, we will cover creating and deploying a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/classify-custom\"\u003ECustomer Classifier\u003C/a\u003E to identify \u003Ca href=\"https://www.placekey.io/pricing\"\u003EPlacekey\u003C/a\u003E location identifiers across our data.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Exploring our Data\u003C/h3\u003E\n","\u003Cp\u003ETo begin, let's take a look at our \u003Ccode\u003ELocation\u003C/code\u003E table where we know Placekey is present so that we can see what they look like.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    TOP 10 *\nFROM raw_pos.location\nWHERE city = 'London';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/london.png\" alt=\"assets/london.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Creating our Classifiers Schema and Placekey Custom Classifier\u003C/h3\u003E\n","\u003Cp\u003ELet's now create a \u003Ccode\u003EClassifier\u003C/code\u003E Schema to store our Classifiers in by running the next query. This will result in a \u003Ccode\u003ESchema CLASSIFIERS successfully created.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE SCHEMA classifiers\n    COMMENT = 'Schema containing Custom Classifiers';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith our schema available, let's now create our \u003Ccode\u003Eplacekey\u003C/code\u003E Custom Classifier by executing the next query which will provide a \u003Ccode\u003EInstance PLACEKEY successfully created.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE snowflake.data_privacy.custom_classifier classifiers.placekey();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3 - Adding REGEX to our Placekey Custom Classifier\u003C/h3\u003E\n","\u003Cp\u003ENext let's test the Regular Expression (Regex) that our Data Engineer has created to locate the Placekey value by running our next query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    placekey\nFROM raw_pos.location\nWHERE placekey REGEXP('^[a-zA-Z0-9d]{3}-[a-zA-Z0-9d]{3,4}@[a-zA-Z0-9d]{3}-[a-zA-Z0-9d]{3}-.*$');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/placekey_regex.png\" alt=\"assets/placekey_regex.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's now use the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/classes/custom_classifier/methods/add_regex\"\u003EADD_REGEX\u003C/a\u003E method to assign this to our Placekey Classifier\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECALL placekey!ADD_REGEX(\n  'PLACEKEY', -- semantic category\n  'IDENTIFIER', -- privacy category\n  '^[a-zA-Z0-9d]{3}-[a-zA-Z0-9d]{3,4}@[a-zA-Z0-9d]{3}-[a-zA-Z0-9d]{3}-.*$', -- regex expression\n  'PLACEKEY*', --column name regex\n  'Add a regex to identify Placekey' -- description\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith the details in place, we can now use the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/classes/custom_classifier/methods/list\"\u003ELIST\u003C/a\u003E method to validate our work\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT placekey!LIST();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/list.png\" alt=\"assets/list.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Using our Placekey Custom Classifier\u003C/h3\u003E\n","\u003Cp\u003ELet's now use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/stored-procedures/system_classify\"\u003ESYSTEM$CLASSIFY\u003C/a\u003E and our Classifier against the \u003Ccode\u003ELocation\u003C/code\u003E table\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECALL SYSTEM$CLASSIFY('raw_pos.location', {'custom_classifiers': ['placekey'], 'auto_tag':true});\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/classify_location.png\" alt=\"assets/classify_location.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo finish, let's confirm our \u003Ccode\u003EPlacekey\u003C/code\u003E column was successfully tagged\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    tag_name,\n    level, \n    tag_value,\n    column_name\nFROM TABLE(information_schema.tag_references_all_columns('raw_pos.location','table'))\nWHERE tag_value = 'PLACEKEY';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/placekey_info.png\" alt=\"assets/placekey_info.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EMoving forward as Schemas or Tables are created and updated we can use this exact process of Automatic and Custom Classification to maintain a strong governance posture and build rich semantic-layer metadata.\u003C/p\u003E\n","\u003Ch2\u003EAccess History (Reads and Writes)\u003C/h2\u003E\n","\u003Ch3\u003EOverview:\u003C/h3\u003E\n","\u003Cp\u003EAccess History provides insights into user queries encompassing what data was  read and when, as well as what statements have performed a write operations.\u003C/p\u003E\n","\u003Cp\u003EFor Tasty Bytes, Access History is particularly important for Compliance, Auditing, and Governance.\u003C/p\u003E\n","\u003Cp\u003EWithin this step, we will walk through leveraging Access History to find when the last time our Raw data was read from and written to.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nAccess History latency is up to 3 hours. If you have just recently setupb the Tasty Bytes environment, some of the queries below may not have results.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1 - Leveraging Access History\u003C/h3\u003E\n","\u003Cp\u003EBy executing the next query we will see how many queries have accessed each of your Raw layer tables directly.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    value:"objectName"::STRING AS object_name,\n    COUNT(DISTINCT query_id) AS number_of_queries\nFROM snowflake.account_usage.access_history,\nLATERAL FLATTEN (input => direct_objects_accessed)\nWHERE object_name ILIKE 'tb_101.raw_%'\nGROUP BY object_name\nORDER BY number_of_queries DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/ah_1.png\" alt=\"assets/ah_1.png\"\u003E\n\u003Cem\u003EPlease note your results may not match the image above\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003EWithin the next query we will determined what is the breakdown between Read and Write queries and when did they last occur.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    value:"objectName"::STRING AS object_name,\n    CASE \n        WHEN object_modified_by_ddl IS NOT NULL THEN 'write'\n        ELSE 'read'\n    END AS query_type,\n    COUNT(DISTINCT query_id) AS number_of_queries,\n    MAX(query_start_time) AS last_query_start_time\nFROM snowflake.account_usage.access_history,\nLATERAL FLATTEN (input => direct_objects_accessed)\nWHERE object_name ILIKE 'tb_101.raw_%'\nGROUP BY object_name, query_type\nORDER BY object_name, number_of_queries DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/ah_2.png\" alt=\"assets/ah_2.png\"\u003E\n\u003Cem\u003EPlease note your results may not match the image above\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003ETo wrap things up, our last query will determine how many queries have accessed each of our Raw layer tables indirectly.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    base.value:"objectName"::STRING AS object_name,\n    COUNT(DISTINCT query_id) AS number_of_queries\nFROM snowflake.account_usage.access_history,\nLATERAL FLATTEN (input => base_objects_accessed) base,\nLATERAL FLATTEN (input => direct_objects_accessed) direct,\nWHERE 1=1\n    AND object_name ILIKE 'tb_101.raw_%'\n    AND object_name <> direct.value:"objectName"::STRING -- base object is not direct object\nGROUP BY object_name\nORDER BY number_of_queries DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/ah_3.png\" alt=\"assets/ah_3.png\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EDirect Objects Accessed:\u003C/strong\u003E Data objects directly named in the query explicitly.\n\u003Cstrong\u003EBase Objects Accessed:\u003C/strong\u003E Base data objects required to execute a query.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EDiscovery with Snowflake Horizon - Universal Search\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EHaving explored a wide variety of Governance functionality available in Snowflake, it is time to put it all together with Universal Search.\u003C/p\u003E\n","\u003Cp\u003EUniversal Search enables Tasty Bytes to easily find Account objects, Snowflake Marketplace listings, relevant Snowflake Documentation and Snowflake Community Knowledge Base articles.\u003C/p\u003E\n","\u003Cp\u003EUniversal Search understands your query and information about your database objects and can find objects with names that differ from your search terms.\u003C/p\u003E\n","\u003Cp\u003EEven if you misspell or type only part of your search term, you can still see useful results.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Universal Search\u003C/h3\u003E\n","\u003Cp\u003ETo leverage Universal Search in Snowsight:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse the Left Navigation Menu\u003C/li\u003E\u003Cli\u003ESelect "Search" (Magnifying Glass)\u003C/li\u003E\u003Cli\u003EEnter Search criteria such as:\n\u003Cul\u003E\u003Cli\u003ETasty Bytes\u003C/li\u003E\u003Cli\u003ESnowflake Best Practices\u003C/li\u003E\u003Cli\u003EHow to use Snowflake Column Masking\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/tasty_bytes.png\" alt=\"assets/tasty_bytes.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EConclusion and Next Steps\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EFantastic work! You have successfully completed the Tasty Bytes - Zero to Snowflake - Governance with Snowflake Horizon Quickstart.\u003C/p\u003E\n","\u003Cp\u003EBy doing so you have now:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELearned System Defined Roles Exist in Snowflake Accounts\u003C/li\u003E\u003Cli\u003ECreated a Role\u003C/li\u003E\u003Cli\u003EGranted Privileges to a Role\u003C/li\u003E\u003Cli\u003ECreate a Tag\u003C/li\u003E\u003Cli\u003ECreated a Masking Policy\u003C/li\u003E\u003Cli\u003EDeployed a Tag Based Masking Policy\u003C/li\u003E\u003Cli\u003ECreated a Row Access Policy using a Mapping Table\u003C/li\u003E\u003Cli\u003ECreated an Aggregation Policy\u003C/li\u003E\u003Cli\u003ECreated a Project Policy\u003C/li\u003E\u003Cli\u003ELeveraged Automatic and Custom Data Classification\u003C/li\u003E\u003Cli\u003EUsed Universal Search\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you would like to re-run this Quickstart please leverage the Reset scripts in the bottom of your associated Worksheet.\u003C/p\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n","\u003Cp\u003ETo continue your journey in the Snowflake AI Data Cloud, please now visit the link below to see all other Powered by Tasty Bytes - Quickstarts available to you.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Ch3\u003E\u003Ca href=\"/en/developers/guides/tasty-bytes-introduction/\"\u003EPowered by Tasty Bytes - Quickstarts Table of Contents\u003C/a\u003E\u003C/h3\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Tasty Bytes - Zero to Snowflake - Governance with Snowflake Horizon","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Governance in Snowflake \r\n![assets/governance_with_horizon_header.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/governance_with_horizon_header.png)\r\n\r\n### Overview\r\nWelcome to the Powered by Tasty Bytes - Zero to Snowflake Quickstart focused on Data Governance! \r\n\r\nWithin this Quickstart we will learn about Snowflake Roles, Role Based Access Control and deploy both Column and Row Level Security that can scale with your business.\r\n\r\n### Prerequisites\r\n- Before beginning, please make sure you have completed the [**Introduction to Tasty Bytes Quickstart**](/en/developers/guides/tasty-bytes-introduction/) which provides a walkthrough on setting up a trial account and deploying the Tasty Bytes Foundation required to complete this Quickstart.\r\n- Please note that features leveraged within this Quickstart will require an Enterprise or Business Critical Snowflake account. Please see our documentation an [**Overview of Snowflake Editions**](https://docs.snowflake.com/en/user-guide/intro-editions#overview-of-editions).\r\n\r\n\r\n### What You Will Learn\r\n- What System Defined Roles Exist in Snowflake Accounts\r\n- How to Create a Role\r\n- How to Grant Privileges to a Role\r\n- How to Create a Tag\r\n- How to Create a Masking Policy\r\n- How to Deploy a Tag Based Masking Policy\r\n- How to Create a Row Access Policy using a Mapping Table\r\n- How to Create an Aggregation Policy\r\n- How to Create a Project Policy\r\n- How to Leverage Automatic and Custom Data Classification\r\n- How to use Universal Search\r\n\r\n### What You Will Build\r\n- Complete Role Based Access Control for a Test Role\r\n- A Robust Data Governance Foundation for your Account\r\n\r\n## Creating a Worksheet and Copying in our SQL\r\n\r\n### Overview\r\nWithin this Quickstart we will follow a Tasty Bytes themed story via a Snowsight SQL Worksheet with this page serving as a side by side guide complete with additional commentary, images and documentation links.\r\n\r\nThis section will walk you through logging into Snowflake, Creating a New Worksheet, Renaming the Worksheet, Copying SQL from GitHub, and Pasting the SQL we will be leveraging within this Quickstart.\r\n\r\n### Step 1 - Accessing Snowflake via URL\r\n- Open a browser window and enter the URL of your Snowflake Account \r\n\r\n### Step 2 - Logging into Snowflake\r\n- Log into your Snowflake account.\r\n\r\n### Step 3 - Navigating to Worksheets\r\n- Click on the Projects Tab in the left-hand navigation bar and click Worksheets.\r\n\r\n### Step 4 - Creating a Worksheet\r\n- Within Worksheets, click the \"+\" button in the top-right corner of Snowsight.\r\n\r\n### Step 5 - Renaming a Worksheet\r\n- Rename the Worksheet by clicking on the auto-generated Timestamp name and inputting \"Tasty Bytes - Governance\"\r\n\r\n### Step 6 - Accessing Quickstart SQL in GitHub\r\n- Click the button below which will direct you to our Tasty Bytes SQL file that is hosted on GitHub.\r\n\u003Cbutton\u003E[tb_zts_data_governance.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/FY25_Zero_To_Snowflake/tb_governance_snowflake_horizon.sql)\u003C/button\u003E\r\n\r\n### Step 7 - Copying Setup SQL from GitHub\r\n- Within GitHub navigate to the right side and click \"Copy raw contents\". This will copy all of the required SQL into your clipboard.\r\n    - ![assets/github_copy_raw_contents.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/github_copy_raw_contents.png)\r\n\r\n### Step 8 - Pasting Setup SQL from GitHub into your Snowflake Worksheet\r\n- Path back to Snowsight and your newly created Worksheet and Paste (*CMD + V for Mac or CTRL + V for Windows*) what we just copied from GitHub.\r\n\r\n### Step 9 - Click Next --\u003E\r\n\r\n## Exploring Available Roles\r\n\r\n### Overview\r\nOur Tasty Bytes Administrator has been tasked with learning the process of deploying Role Based Access Control (RBAC) and proper Governance across our Snowflake Account. \r\n\r\nTo begin, let's first dive into the Snowflake System Defined Roles provided by default in all accounts and learn a bit more on their privileges.\r\n\r\n### Step 1 - Setting our Context\r\nBefore we can begin executing queries within the Snowflake Snowsight interface we must first set our context by running [USE ROLE](https://docs.snowflake.com/en/sql-reference/sql/use-role) and [USE WAREHOUSE](https://docs.snowflake.com/en/sql-reference/sql/use-warehouse) commands or manually setting these in the top-right corner.\r\n\r\nFor this step, we will do this by executing our first two queries by highlighting them both and clicking the \"▶ Run\" button. \r\n\r\nOnce complete our results pane will result in a `Statement Executed Successfully` message.\r\n\r\n```\r\nUSE ROLE accountadmin;\r\nUSE WAREHOUSE tb_dev_wh;\r\n```\r\n\r\nWith our context set, we can continue on our learning journey.\r\n\r\n### Step 2 - Exploring All Roles in our Account\r\nNow let's run the next query which leverages [SHOW ROLES](https://docs.snowflake.com/en/sql-reference/sql/show-roles) to provide a result set consisting of all roles currently deployed in our account.\r\n\r\n```\r\nSHOW ROLES;\r\n```\r\n\r\n\r\nIf you are operating in a trial account with only Tasty Bytes deployed you will only see a few `tb_` roles, however if you are using an existing Snowflake account your list may be more extensive. \r\n\r\nThankfully we can filter down this result set which we will cover in the next step.\r\n\r\n###  Step 3 - Using Result Scan to Filter our Result\r\nTo filter on just the Snowflake System Defined Roles from our previous output please execute the next query which utilizes [RESULT_SCAN](https://docs.snowflake.com/en/sql-reference/functions/result_scan) and [LAST_QUERY_ID](https://docs.snowflake.com/en/sql-reference/functions/last_query_id) to query our previous SHOW ROLES results as if they were a table giving us the ability to add a WHERE clause.\r\n\r\n```\r\nSELECT \r\n    \"name\",\r\n    \"comment\"\r\nFROM TABLE(RESULT_SCAN(LAST_QUERY_ID()))\r\nWHERE \"name\" IN ('ORGADMIN','ACCOUNTADMIN','SYSADMIN','USERADMIN','SECURITYADMIN','PUBLIC');\r\n```\r\n\r\n![assets/sysroles.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/sysroles.png)\r\n\r\n\r\nIn our result set we can see the high-level descriptions of what these Snowflake System Defined Roles have privileges to do. \r\n\r\n\u003Easide positive\r\n\u003E**Note:** For additional details on these, please the [Snowflake System Defined Roles](https://docs.snowflake.com/en/user-guide/security-access-control-overview#system-defined-roles) documentation.\r\n\u003E\r\n\r\n## Creating a Role and Granting Privileges\r\n\r\n### Overview\r\nNow that we understand these System Defined roles, let's begin leveraging them to create a test role and grant it access to the Customer Loyalty data we will deploy our initial Data Governance features against and also providing the ability to use our `tb_dev_wh` Warehouse.\r\n\r\n### Step 1 - Using the Useradmin Role to Create our Test Role\r\nAs we saw, a `useradmin` can create and manage users and roles. Please kick off the next two queries with the first assuming that `useradmin` role and the second leveraging a [CREATE ROLE](https://docs.snowflake.com/en/sql-reference/sql/create-role) command to generate a new `tb_test_role` we will use throughout this Quickstart. This will result in a `Role TB_TEST_ROLE successfully created.` message.\r\n\r\n```\r\nUSE ROLE useradmin;\r\n\r\nCREATE OR REPLACE ROLE tb_test_role\r\n    COMMENT = 'test role for tasty bytes';\r\n```\r\n\r\n\r\n### Step 2 - Using the Securityadmin Role to Grant Warehouse Privileges\r\nWith our `tb_test_role` in place, we can now begin to use [GRANT](https://docs.snowflake.com/en/sql-reference/sql/grant-privilege) statements to give access privileges to required Database objects and our `tb_dev_wh` Warehouse.\r\n\r\nTo begin, please run the next three queries which will first assume the `securityadmin` role that we learned can manage security aspects in our account, next we will grant ALL privileges on this Warehouse to our `sysadmin`  and finally grant OPERATE and USAGE on our `tb_dev_wh` to our `tb_test_role`. \r\n\r\nWhen completed we will recieve a `Statement executed successfully.` message.\r\n\r\n```\r\nUSE ROLE securityadmin;\r\n\r\nGRANT ALL ON WAREHOUSE tb_dev_wh TO ROLE sysadmin;\r\n\r\nGRANT OPERATE, USAGE ON WAREHOUSE tb_dev_wh TO ROLE tb_test_role;\r\n```\r\n\r\nFor more on Snowflake Warehouse Privilege Grants please see below:\r\n\u003Easide positive\r\n\u003E**MODIFY:** Enables altering any properties of a warehouse, including changing its size.\r\n\u003E \r\n\u003E**MONITOR:** Enables viewing current and past queries executed on a warehouse as well as usage statistics on that warehouse.\r\n\u003E\r\n\u003E**OPERATE:** Enables changing the state of a warehouse (stop, start, suspend, resume). In addition, enables viewing current and past queries executed on a warehouse and aborting any executing queries.\r\n\u003E\r\n\u003E**USAGE:** Enables using a virtual warehouse and, as a result, executing queries on the warehouse. If the warehouse is configured to auto-resume when a SQL statement is submitted to it, the warehouse resumes automatically and executes the statement.\r\n\u003E\r\n\u003E**ALL:** Grants all privileges, except OWNERSHIP, on the warehouse.\r\n\u003E\r\n\r\n### Step 3 - Using the Securityadmin Role to Grant Database and Schema Privileges\r\nWith the Warehouse privileges in place, please execute the next two queries which will provide the `tb_test_role` with the USAGE privilege on the `tb_101` database and all schemas within.\r\n\r\nOnce again when completed we will recieve a `Statement executed successfully. X objects affected.` message.\r\n\r\n```\r\nGRANT USAGE ON DATABASE tb_101 TO ROLE tb_test_role;\r\nGRANT USAGE ON ALL SCHEMAS IN DATABASE tb_101 TO ROLE tb_test_role;\r\n```\r\n\r\nFor more on Snowflake Database and Schema Grants please see below:\r\n\u003Easide positive\r\n\u003E **MODIFY:** Enables altering any settings of a database.\r\n\u003E\r\n\u003E **MONITOR:** Enables performing the DESCRIBE command on the database.\r\n\u003E\r\n\u003E **USAGE:** Enables using a database, including returning the database details in the SHOW DATABASES command output. Additional privileges are required to view or take actions on objects in a database.\r\n\u003E \r\n\u003E **ALL:** Grants all privileges, except OWNERSHIP, on a database.\r\n\u003E\r\n\r\n### Step 4 - Using the Securityadmin Role to Grant Table and View Privileges\r\nAs we will be testing several Data Governance features as our `tb_test_role` let's ensure it can run SELECT statements across our entire Data Model. \r\n\r\nPlease now execute the next three queries of this section which will once again result in a `Statement executed successfully. X objects affected.` message.\r\n\r\n```\r\nGRANT SELECT ON ALL TABLES IN SCHEMA tb_101.raw_customer TO ROLE tb_test_role;\r\nGRANT SELECT ON ALL TABLES IN SCHEMA tb_101.raw_pos TO ROLE tb_test_role;\r\nGRANT SELECT ON ALL VIEWS IN SCHEMA tb_101.analytics TO ROLE tb_test_role;\r\n```\r\n\r\nFor more on Snowflake View and Table Privilege Grants please see below:\r\n\u003E aside positive\r\n\u003E **SELECT:** Enables executing a SELECT statement on a table/view. Note that this privilege is sufficient to query a view.\r\n\u003E\r\n\u003E **INSERT:** Enables executing an INSERT command on a table.\r\n\u003E\r\n\u003E **UPDATE:** Enables executing an UPDATE command on a table.\r\n\u003E\r\n\u003E **TRUNCATE:** Enables executing a TRUNCATE TABLE command on a table.\r\n\u003E\r\n\u003E **DELETE:** Enables executing a DELETE command on a table.\r\n\u003E\r\n\r\n### Step 5 - Using the Securityadmin Role to Grant our Role to our User\r\nWith our `tb_test_role` properly privileged, let's now execute the last two queries of this section. Here we will first [SET](https://docs.snowflake.com/en/sql-reference/sql/set) a SQL Variable called `my_user_var` equal to our [CURRENT_USER](https://docs.snowflake.com/en/sql-reference/functions/current_user). \r\n\r\nThe second query will then grant our role to our user we are logged in as which has been stored in our `$my_user_var`. \r\n\r\nAs we've seen before, our query will result in a `Statement executed successfully` message.\r\n\r\n```\r\nSET my_user_var  = CURRENT_USER();\r\nGRANT ROLE tb_test_role TO USER identifier($my_user_var);\r\n```\r\n\r\n**Awesome!** We now have a `tb_test_role` with required Database Object and Warehouse privileges and our user is able to leverage it.\r\n\r\n### Step 6 - Click Next --\u003E\r\n\r\n\r\n## Column-Level Security and Tagging = Tag-Based Masking\r\n\r\n### Overview\r\nThe first Governance feature set we want to deploy and test will be Snowflake Tag Based Dynamic Data Masking. This will allow us to mask PII data in columns from our Test Role but not from more privileged Roles.\r\n\r\nBefore we can begin masking data, let's first explore what PII exists in our Customer Loyalty data.\r\n\r\n### Step 1 - Finding our PII Columns\r\nThanks to our last step, we can now officially use our `tb_test_role` Role and leverage the `tb_dev_wh` Warehouse from that role. \r\n\r\nPlease execute the next four queries which will first handle setting our context. With the context in place the fourth query will explore our `raw_customer.customer_loyalty` which we ingest from the Tasty Bytes Customer Loyalty Program provider.\r\n\r\n```\r\nUSE ROLE tb_test_role;\r\nUSE WAREHOUSE tb_dev_wh;\r\nUSE DATABASE tb_101;\r\n\r\nSELECT\r\n    cl.customer_id,\r\n    cl.first_name,\r\n    cl.last_name,\r\n    cl.e_mail,\r\n    cl.phone_number,\r\n    cl.city,\r\n    cl.country,\r\n    cl.sign_up_date,\r\n    cl.birthday_date\r\nFROM raw_customer.customer_loyalty cl \r\nSAMPLE (1000 ROWS);\r\n```\r\n\r\n![assets/customer_loyalty.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/customer_loyalty.png)\r\n\r\n**Woah!!** there is a lot of PII we need to take care before our users can touch this data. Luckily we can use Snowflakes native Tag-Based Masking functionality to do just this.\r\n\r\n\u003E aside positive\r\n\u003E A [Tag-based Masking Policy](https://docs.snowflake.com/en/user-guide/tag-based-masking-policies) combines the object tagging and masking policy features to allow a masking policy to be set on a tag using an ALTER TAG command. When the data type in the masking policy signature and the data type of the column match, the tagged column is automatically protected by the conditions in the masking policy.\r\n\u003E\r\n\r\n### Step 2 - Creating our Schemas\r\nTo keep ourselves organized and to follow best practices, let's now create and privilege `Tags` and `Governance` schemas within our Database by executing the next five queries.\r\n\r\n\r\n```\r\nUSE ROLE accountadmin;\r\n\r\nCREATE OR REPLACE SCHEMA tags\r\n    COMMENT = 'Schema containing Object Tags';\r\n\r\nGRANT USAGE ON SCHEMA tags TO ROLE public;\r\n\r\nCREATE OR REPLACE SCHEMA governance\r\n    COMMENT = 'Schema containing Security Policies';\r\n\r\nGRANT ALL ON SCHEMA governance TO ROLE sysadmin;\r\n```\r\n\r\n\r\n### Step 3 - Creating our Tag\r\nTo begin our masking process, please run the [CREATE TAG](https://docs.snowflake.com/en/sql-reference/sql/create-tag) query below. \r\n\r\nWithin this query we are creating a `tasty_pii` Tag for these values: NAME, PHONE_NUMBER, EMAIL, BIRTHDAY. Not only will this tag prevent free text values, but it will also add the Tag to the selection menu to Snowsight. This will result in a `Tag TASTY_PII successfully created.` result.\r\n\r\n```\r\nCREATE OR REPLACE TAG tags.tasty_pii\r\n    ALLOWED_VALUES 'NAME', 'PHONE_NUMBER', 'EMAIL', 'BIRTHDAY'\r\n    COMMENT = 'Tag for PII, allowed values are: NAME, PHONE_NUMBER, EMAIL, BIRTHDAY';\r\n```\r\n\r\n\u003Easide positive\r\n\u003ETags enable data stewards to track sensitive data for compliance, discovery, protection, and resource usage use cases through either a centralized or decentralized data governance management approach.\r\n\u003E\r\n\r\n### Step 4 - Applying Tags\r\nWith the Tag in place, let's now execute the next query which uses [ALTER TABLE... MODIFY COLUMN](https://docs.snowflake.com/en/sql-reference/sql/alter-table-column) to set our tag to each respective column in the `customer_loyalty` table. This will result in a `Statement executed successfully.` message.\r\n\r\n```\r\nALTER TABLE raw_customer.customer_loyalty\r\n    MODIFY COLUMN \r\n    first_name SET TAG tags.tasty_pii = 'NAME',\r\n    last_name SET TAG tags.tasty_pii = 'NAME',\r\n    phone_number SET TAG tags.tasty_pii = 'PHONE_NUMBER',\r\n    e_mail SET TAG tags.tasty_pii = 'EMAIL',\r\n    birthday_date SET TAG tags.tasty_pii = 'BIRTHDAY';\r\n```\r\n\r\n### Step 5 - Exploring Tags on a Table\r\nWith our Tag created and applied to Columns, please kick off the next query where we leverage the [TAG_REFERENCES_ALL_COLUMNS](https://docs.snowflake.com/en/sql-reference/functions/tag_references_all_columns) function to validate the work we just completed.\r\n\r\n```\r\nSELECT\r\n    tag_database,\r\n    tag_schema,\r\n    tag_name,\r\n    column_name,\r\n    tag_value\r\nFROM TABLE(information_schema.tag_references_all_columns\r\n    ('tb_101.raw_customer.customer_loyalty','table'));\r\n```\r\n\r\n![assets/tag_ref_all.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/tag_ref_all.png)\r\n\r\n**Perfect!** Just as desired, we see our created tag is associated to the PII columns we will look to mask in the next section.\r\n\r\n### Step 6 - Click Next --\u003E\r\n\r\n## Creating Masking Policies and Applying to Tags\r\n\r\n### Overview\r\nWith our Tags in place we can now create our Masking Policies that will mask data for all but privileged Roles. We need to create 1 policy for every data type where the return data type can be implicitly cast into the column datatype. We can only assign 1 policy per datatype to an individual Tag.\r\n\r\n### Step 1 - Creating our first Data Masking Policy\r\nFor the three different column types we are looking to mask, we want to address the following:\r\n- **Names:** Complete masking for any role that is not `accountadmin` or `sysadmin`\r\n- **Phone Numbers**: Partial masking with only the first three numbers exposed to any role that is not `accountadmin` or `sysadmin`.\r\n- **E-mail Addresses**: Partial masking with only the email provider information available to any role that is not `accountadmin` or `sysadmin`.\r\n\r\nIn this step we will create our tasty_pii_string_mask Masking Policy. Within the policy we will see the use of [CREATE MASKING POLICY](https://docs.snowflake.com/en/sql-reference/sql/create-masking-policy) as well as the other Snowflake functions required to complete our masking including [CURRENT_ROLE](https://docs.snowflake.com/en/sql-reference/functions/current_role), [CONCAT](https://docs.snowflake.com/en/sql-reference/functions/concat), [LEFT](https://docs.snowflake.com/en/sql-reference/functions/left), and [SPLIT_PART](https://docs.snowflake.com/en/sql-reference/functions/split_part). This query will result in a `Masking policy TASTY_PII_STRING_MASK successfully created.` message.\r\n\r\n```\r\nCREATE OR REPLACE MASKING POLICY governance.tasty_pii_string_mask AS (val STRING) RETURNS STRING -\u003E\r\n    CASE\r\n        -- these active roles have access to unmasked values \r\n        WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN')\r\n            THEN val \r\n        -- if a column is tagged with TASTY_PII : PHONE_NUMBER \r\n        -- then mask everything but the first 3 digits   \r\n        WHEN SYSTEM$GET_TAG_ON_CURRENT_COLUMN('TAGS.TASTY_PII') = 'PHONE_NUMBER'\r\n            THEN CONCAT(LEFT(val,3), '-***-****')\r\n        -- if a column is tagged with TASTY_PII : EMAIL  \r\n        -- then mask everything before the @ sign  \r\n        WHEN SYSTEM$GET_TAG_ON_CURRENT_COLUMN('TAGS.TASTY_PII') = 'EMAIL'\r\n            THEN CONCAT('**~MASKED~**','@', SPLIT_PART(val, '@', -1))\r\n        -- all other conditions should be fully masked   \r\n    ELSE '**~MASKED~**' \r\nEND;\r\n```\r\n\r\n### Step 2 - Addressing Re-Identification\r\nThe combination of an individuals City, first 3 Phone Number digits, and Birthday to re-identify them. Let's play it safe and also truncate Birthdays into 5 year buckets which will fit the use case of our Analyst. Run the next query to create our Date Masking Policy to return modified Birthdays. This will return `Masking policy TASTY_PII_DATE_MASK successfully created.`\r\n\r\n```\r\nCREATE OR REPLACE MASKING POLICY governance.tasty_pii_date_mask AS (val DATE) RETURNS DATE -\u003E\r\n    CASE\r\n        -- these active roles have access to unmasked values \r\n        WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN')\r\n            THEN val\r\n        -- if a column is tagged with TASTY_PII : BIRTHDAY  \r\n        -- then truncate to 5 year buckets \r\n        WHEN SYSTEM$GET_TAG_ON_CURRENT_COLUMN('TAGS.TASTY_PII') = 'BIRTHDAY'\r\n            THEN DATE_FROM_PARTS(YEAR(val) - (YEAR(val) % 5),1,1)\r\n        -- if a Date column is not tagged with BIRTHDAY, return NULL\r\n    ELSE NULL \r\nEND;\r\n```\r\n\r\n### Step 3 - Set Masking Policies on Tagged Columns\r\nNow we are able to use an ALTER TAG statement to set the Masking Policies on the PII tagged columns Please now execute the query in this step, which applies each Masking Policy to the appropriate Tag using [ALTER TAG... SET MASKING POLICY](https://docs.snowflake.com/en/sql-reference/sql/alter-tag) command.\r\n\r\nThis will result in a `Statement executed successfully.` message.\r\n\r\n```\r\nALTER TAG tags.tasty_pii SET\r\n    MASKING POLICY governance.tasty_pii_string_mask,\r\n    MASKING POLICY governance.tasty_pii_date_mask;\r\n```\r\n\r\nWe can now officially say we have deployed Tag Based Masking Policies. Let's move on to the next step where we will validate our work.\r\n\r\n### Step 4 - Validating our Work\r\nWith Tag Based Masking in-place, let's check things out by first assuming our Test Role and using our Development Warehouse and then beginning to query both raw and downstream data.\r\n\r\nWithin our first query we will query the Raw layer Customer Loyalty table directly.\r\n\r\n```\r\nUSE ROLE tb_test_role;\r\nUSE WAREHOUSE tb_dev_wh;\r\n\r\nSELECT\r\n    cl.customer_id,\r\n    cl.first_name,\r\n    cl.last_name,\r\n    cl.phone_number,\r\n    cl.e_mail,\r\n    cl.birthday_date,\r\n    cl.city,\r\n    cl.country\r\nFROM raw_customer.customer_loyalty cl\r\nWHERE cl.country IN ('United States','Canada','Brazil');\r\n```\r\n\r\n![assets/raw_mask_test.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/raw_mask_test.png)\r\n\r\nSince we reference this `raw_customer.customer_loyalty` Table in downstream Views let's see if this Masking Policy impacts us there as well. Please kick off the next query which calculates our Customer Loyalty members `lifetime_sales_usd` totals and sorts them in descending order by this amount.\r\n\r\n```\r\nSELECT TOP 10\r\n    clm.customer_id,\r\n    clm.first_name,\r\n    clm.last_name,\r\n    clm.phone_number,\r\n    clm.e_mail,\r\n    SUM(clm.total_sales) AS lifetime_sales_usd\r\nFROM analytics.customer_loyalty_metrics_v clm\r\nWHERE clm.city = 'San Mateo'\r\nGROUP BY clm.customer_id, clm.first_name, clm.last_name, clm.phone_number, clm.e_mail\r\nORDER BY lifetime_sales_usd;\r\n```\r\n\r\n![assets/analytics_mask_test.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/analytics_mask_test.png) \r\n\r\n### Step 5 - Click Next --\u003E\r\n\r\n## Row-Access Policies\r\n\r\n### Overview\r\nHappy with our Tag Based Dynamic Masking controlling masking at the Column level,\r\nwe will now look to restrict access at the Row level for our Test Role. \r\n\r\nWithin our Customer Loyalty table, our Test Role should only see Customers who are\r\nbased in Tokyo. Thankfully, Snowflake has another powerful native Data Governance\r\nfeature that can handle this at scale called [Row Access Policies](https://docs.snowflake.com/en/user-guide/security-row-intro). \r\n\r\nFor our use case, we will leverage the mapping table approach.\r\n\r\n\u003Easide positive\r\n\u003E Snowflake supports row-level security through the use of Row Access Policies to determine which rows to return in the query result. The row access policy can be relatively simple to allow one particular role to view rows, or be more complex to include a mapping table in the policy definition to determine access to rows in the query result.\r\n\u003E\r\n\r\n### Step 1 - Creating a Mapping Table\r\nTo begin our Row Level Security journey we will start by creating a `row_policy_map` table that will serve as our Mapping Table which a Row Access Policy condition can reference to filter the query result set. \r\n\r\nLet's now kick off the first two queries in this section which will assume the `accountadmin` Role and create our Mapping Table that will accept Roles and the City they should have permission to see. This will result in `Table ROW_POLICY_MAP successfully created.`.\r\n\r\n```\r\nUSE ROLE accountadmin;\r\n\r\nCREATE OR REPLACE TABLE governance.public.row_policy_map\r\n    (role STRING, city_permissions STRING);\r\n```\r\n\r\n\r\n### Step 2 - Inserting Mapping Records\r\nWith our `row_policy_map` in place, please kick off the next query which will [INSERT](https://docs.snowflake.com/en/sql-reference/sql/insert) a record that maps our `tb_test_role` to Tokyo. This query will result in `number of rows insterted: 1`.\r\n\r\n```\r\nINSERT INTO governance.row_policy_map\r\n    VALUES ('TB_TEST_ROLE','Tokyo'); \r\n```\r\n\r\n\r\n\r\n### Step 3 - Creating a Row Access Policy\r\nNow that we have a record in our `row_policy_map` Table, we can kick off our next query to create our Row Access Policy.\r\n\r\nWithin this query we are using [CREATE ROW ACCESS POLICY](https://docs.snowflake.com/en/sql-reference/sql/create-row-access-policy) that will allow our Admin and Tasty Workload Roles to see all rows but if a user is using a Role that is seen in our Mapping Table they will only be allowed to see the rows relevant to the City they are mapped to. After executing this query you will recieve a `Row access policy 'CUSTOMER_CITY_ROW_POLICY' is successfully created.` result.\r\n\r\n```\r\nCREATE OR REPLACE ROW ACCESS POLICY governance.customer_city_row_policy\r\n    AS (city STRING) RETURNS BOOLEAN -\u003E\r\n       CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN') -- list of roles that will not be subject to the policy\r\n        OR EXISTS -- this clause references our mapping table from above to handle the row level filtering\r\n            (\r\n            SELECT rp.role\r\n                FROM governance.row_policy_map rp\r\n            WHERE 1=1\r\n                AND rp.role = CURRENT_ROLE()\r\n                AND rp.city_permissions = city\r\n            )\r\nCOMMENT = 'Policy to limit rows returned based on mapping table of ROLE and CITY: governance.row_policy_map';\r\n```\r\n\r\n\r\n### Step 4 - Applying a Row Access Policy to a Table\r\nPlease kick off the next query which will associate our Row Access Policy to our `city` Column within the `customer_loyalty` table.\r\n\r\nOnce complete we will recieve a `Statement executed successfully.` message indicating our policy is applied.\r\n\r\n```\r\nALTER TABLE raw_customer.customer_loyalty\r\n    ADD ROW ACCESS POLICY governance.customer_city_row_policy ON (city);\r\n```\r\n\r\n### Step 5 - Testing our Row Access Policy in a Non-Privileged Role\r\nWith everything in place, we can begin to test that our Row Access Policy is operating as expected. Please now kick off the next two queries which set our `tb_test_role` Role context and query the `customer_loyalty` table to return a list of our Customer Loyalty members including a calculated `age` Column that leverages [DATEDIFF](https://docs.snowflake.com/en/sql-reference/functions/datediff) and [CURRENT_DATE](https://docs.snowflake.com/en/sql-reference/functions/current_date) functions.\r\n\r\n```\r\nUSE ROLE tb_test_role;\r\n\r\nSELECT\r\n    cl.customer_id,\r\n    cl.first_name,\r\n    cl.last_name,\r\n    cl.city,\r\n    cl.marital_status,\r\n    DATEDIFF(year, cl.birthday_date, CURRENT_DATE()) AS age\r\nFROM raw_customer.customer_loyalty cl SAMPLE (10000 ROWS)\r\nGROUP BY cl.customer_id, cl.first_name, cl.last_name, cl.city, cl.marital_status, age;\r\n```\r\n\r\n![assets/raw_rls_test.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/raw_rls_test.png)\r\n\r\n**Yay!** Our Row Access Policy is working as expected and we are also seeing our masking in place since we are using the `tb_test_role`. \r\n\r\n\r\n### Step 6 - Testing our Row Access Policy Downstream\r\nAs we did previously, let's now confirm our Row Access Policy is in effect when we query our downstream Analytics `customer_loyalty_metrics_v` View by running the next query.\r\n\r\n```\r\nSELECT\r\n    clm.city,\r\n    SUM(clm.total_sales) AS total_sales_usd\r\nFROM analytics.customer_loyalty_metrics_v clm\r\nGROUP BY clm.city;\r\n```\r\n\r\n![assets/analytics_rls_test.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/analytics_rls_test.png)\r\n\r\nJust as expected, our Governance features are seen downstream despite only having to define them once and apply them to the Raw Tables our Analytics Views retrieve query results from. \r\n\r\n### Step 7 - Click Next --\u003E\r\n\r\n\r\n## Aggregation Policies\r\n\r\n### Overview\r\n Outside of the Data Access Policies (Masking and Row Access) we have covered, Snowflake Horizon also provides [Privacy Policies](https://docs.snowflake.com/en/user-guide/aggregation-policies). In this section we will cover the ability to set Aggregation Policies on Database Objects which can restrict certain roles to only aggregate data by only allowing for queries that aggregate data into groups of a minimum size versus retrieving individual roles.\r\n\r\nFor Tasty Bytes and the Test role we have created, let's test an Aggregation Policy ut against our Raw Order Header table.\r\n\r\n\u003E aside positive\r\n\u003E\r\n\u003E An **Aggregation Policy** is a schema-level object that controls what type of query can access data from a table or view. When an aggregation policy is applied to a table, queries against that table must aggregate data into groups of a minimum size in order to return results, thereby preventing a query from returning information from an individual record.\r\n\u003E\r\n\r\n\r\n### Step 1 - Creating a Conditional Aggregation Policy\r\nTo begin, let's once again assume our `Accountadmin` role. For our use case, we will create a Conditional Aggregation Policy in our `Governance` Schema that will only allow queries from non-admin users to return results for queries that aggregate more than 1000 rows. Please kick off the next two queries which will result `Aggregation policy 'TASTY_ORDER_TEST_AGGREGATION_POLICY' is successfuly created/`. \r\n\r\n```\r\nUSE ROLE accountadmin;\r\n\r\nCREATE OR REPLACE AGGREGATION POLICY governance.tasty_order_test_aggregation_policy\r\n  AS () RETURNS AGGREGATION_CONSTRAINT -\u003E\r\n    CASE\r\n      WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN')\r\n      THEN NO_AGGREGATION_CONSTRAINT()  \r\n      ELSE AGGREGATION_CONSTRAINT(MIN_GROUP_SIZE =\u003E 1000) -- atleast 1000 rows in aggregate\r\n    END;\r\n```\r\n\r\n### Step 2 - Setting our Aggregation Policy on a Table\r\nWith the Aggregation Policy created, let's apply it to our Order Header table in the next query which will return a `Statement executed successully.` message.\r\n\r\n```\r\nALTER TABLE raw_pos.order_header\r\n    SET AGGREGATION POLICY governance.tasty_order_test_aggregation_policy;\r\n```\r\n\r\n### Step 3 - Testing our Aggregation Policy\r\nNow let's test our work by assuming our Test Role and executing a few queries against the `order_header` table. Please execute the next two queries which will set our role and run a TOP 10 SELECT statement from the table.\r\n\r\n```\r\nUSE ROLE tb_test_role;\r\n\r\nSELECT TOP 10 * FROM raw_pos.order_header;\r\n```\r\n\r\n![assets/agg_test_fail.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/agg_test_fail.png)\r\n\r\nRun the next query to see what happens if we include over 1000 rows.\r\n\r\n![assets/agg_test_fail.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/agg_test_fail.png)\r\n\r\n### Step 4 - Conducting Aggregate Analysis\r\nBringing in the Customer Loyalty table that we have previously:\r\n - Deployed Masking against PII columns\r\n - Deployed Row Level Security to restrict our Test Role to only Tokyo results\r\n\r\nLet's answer a few aggregate questions that the business has presented to our Analyst.\r\n\r\n**Question 1** - What are the total order amounts by gender?\r\n\r\n```\r\nSELECT \r\n    cl.gender,\r\n    cl.city,\r\n    COUNT(oh.order_id) AS count_order,\r\n    SUM(oh.order_amount) AS order_total\r\nFROM raw_pos.order_header oh\r\nJOIN raw_customer.customer_loyalty cl\r\n    ON oh.customer_id = cl.customer_id\r\nGROUP BY ALL\r\nORDER BY order_total DESC;\r\n```\r\n\r\n![assets/agg_1.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/agg_1.png)\r\n\r\n\r\n**Question 2** - What are the total order amounts by Postal Code?\r\n\r\n```\r\nSELECT \r\n    cl.postal_code,\r\n    cl.city,\r\n    COUNT(oh.order_id) AS count_order,\r\n    SUM(oh.order_amount) AS order_total\r\nFROM raw_pos.order_header oh\r\nJOIN raw_customer.customer_loyalty cl\r\n    ON oh.customer_id = cl.customer_id\r\nGROUP BY ALL\r\nORDER BY order_total DESC;\r\n```\r\n\r\n![assets/agg_2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/agg_2.png)\r\n\r\n\r\n\u003E aside positive\r\n\u003E Note: If the query returns a group that contains fewer records than the minimum group size of the policy, then Snowflake combines those groups into a remainder group.\r\n\u003E\r\n\r\n\r\n## Projection Policies\r\n\r\n### Overview:\r\nWithin this step, we will cover another Privacy Policy framework provided by Snowflake Horizon, this time diving into [Projection Policies](https://docs.snowflake.com/en/user-guide/projection-policies) which in short will prevent queries from using a SELECT statement to project values from a column.\r\n\r\n\r\n### Step 1 - Creating a Conditional Projection Policy\r\nFor our use case, we will create a Conditional Projection Policy in our `Governance` Schema that will only allow our Admin Roles to project the columns we will assign it to. Please execute the next two queries which will assume our `accountadmin` Role and create our `tasty_customer_test_projection_policy` Projection Policy. This will result in `Projection policy 'TASTY_CUSTOMER_TEST_PROJECTION_POLICY' is successfully created.`\r\n\r\n\r\n```\r\nUSE ROLE accountadmin;\r\n\r\nCREATE OR REPLACE PROJECTION POLICY governance.tasty_customer_test_projection_policy\r\n  AS () RETURNS PROJECTION_CONSTRAINT -\u003E \r\n  CASE\r\n    WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','SYSADMIN')\r\n    THEN PROJECTION_CONSTRAINT(ALLOW =\u003E true)\r\n    ELSE PROJECTION_CONSTRAINT(ALLOW =\u003E false)\r\n  END;\r\n```\r\n\r\n\r\n\u003E aside positive\r\n\u003E A projection policy is a first-class, schema-level object that defines whether a column can be projected in the output of a SQL query result. A column with a projection policy assigned to it is said to be projection constrained.\r\n\u003E\r\n\r\n\r\n### Step 2 - Applying our Projection Policy to our Postal Code column\r\nWith the Projection Policy in place, let's assign it to our Postal Code column by executing the query below. This will result in a `Statement executed successfully.` message.\r\n\r\n```\r\nALTER TABLE raw_customer.customer_loyalty\r\n MODIFY COLUMN postal_code\r\n SET PROJECTION POLICY governance.tasty_customer_test_projection_policy; \r\n```\r\n\r\n\r\n### Step 3 - Testing our Projection Policy\r\nNow we can move on to testing our Projection Policy by first assuming our `tb_test_role` Role and running a SELECT TOP 100 * query against our `customer_loyalty` table.\r\n\r\n\r\n```\r\nUSE ROLE tb_test_role;\r\n\r\nSELECT TOP 100 * FROM raw_customer.customer_loyalty;\r\n```\r\n\r\n![assets/projection_fail.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/projection_fail.png)\r\n\r\n\r\nLet's execute the next query to see what happens if we EXCLUDE the `postal_code` column.\r\n\r\n```\r\nSELECT TOP 100 * EXCLUDE postal_code FROM raw_customer.customer_loyalty;\r\n```\r\n\r\n![assets/projection_no_postal.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/projection_no_postal.png)\r\n\r\n### Step 4 - Click Next --\u003E\r\n\r\n\r\n## Sensitive Data Classification\r\n\r\n### Overview\r\nIn some cases, you may not know if there is sensitive data in a table. Snowflake Horizon provides the capability to attempt to automatically detect sensitive information and apply relevant Snowflake system defined privacy tags. \r\n\r\nClassification is a multi-step process that associates Snowflake-defined system tags to columns by analyzing the fields and metadata for personal data. Data  Classification can be done via SQL or the Snowsight interface.\r\n\r\nWithin this step we will be using SQL to classify a single table as well as all tables within a schema.\r\n\r\nTo learn how to complete Data Classification within the Snowsight interface, please see the following documentation: \r\n\r\nUsing Snowsight to classify tables in a schema \r\n  • https://docs.snowflake.com/en/user-guide/governance-classify-using#using-sf-web-interface-to-classify-tables-in-a-schema\r\n\r\n### Step 1 - SYSTEM$CLASSIFY\r\nAs our Raw Customer Schema only includes one table, let's use [SYSTEM$CLASSIFY](https://docs.snowflake.com/en/sql-reference/stored-procedures/system_classify) against it after we assume our `accountadmin` role. Please execute the next two queries.\r\n\r\n\r\n```\r\nUSE ROLE accountadmin;\r\n\r\nCALL SYSTEM$CLASSIFY('raw_customer.customer_loyalty', {'auto_tag': true});\r\n```\r\n\r\n![assets/system$classify.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/system$classify.png)\r\n\r\n\r\nNow let's view the new Tags Snowflake applied automatically via Data Classification by executing the next query.\r\n\r\n```\r\nSELECT * FROM TABLE(information_schema.tag_references_all_columns('raw_customer.customer_loyalty','table'));\r\n```\r\n\r\n![assets/system$classify_info.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/system$classify_info.png)\r\n\r\n### Step 2 - SYSTEM$CLASSIFY_SCHEMA\r\nAs our Raw Point-of-Sale Schema includes numerous tables, let's use [SYSTEM$CLASSIFY_SCHEMA](https://docs.snowflake.com/en/sql-reference/stored-procedures/system_classify_schema) against it in our next query.\r\n\r\n```\r\nCALL SYSTEM$CLASSIFY_SCHEMA('raw_pos', {'auto_tag': true});\r\n```\r\n\r\n![assets/system$classify_schema.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/system$classify_schema.png)\r\n\r\n\r\nOnce again, let's view the Tags applied using the Franchise table within the Schema.\r\n\r\n```\r\nSELECT * FROM TABLE(information_schema.tag_references_all_columns('raw_pos.franchise','table'));\r\n```\r\n\r\n![assets/system$classify_schema_info.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/system$classify_schema_info.png)\r\n\r\n### Step 3 - Click Next --\u003E\r\n\r\n\r\n\r\n## Custom Data Classification\r\n\r\n### Overview\r\nSnowflake provides the CUSTOM_CLASSIFIER class in the SNOWFLAKE.DATA_PRIVACY schema to enable Data Engineers to extend their Data Classification capabilities based on their own knowledge of their data.\r\n\r\nIn this step, we will cover creating and deploying a [Customer Classifier](https://docs.snowflake.com/en/user-guide/classify-custom) to identify [Placekey](https://www.placekey.io/pricing) location identifiers across our data.\r\n\r\n### Step 1 - Exploring our Data\r\nTo begin, let's take a look at our `Location` table where we know Placekey is present so that we can see what they look like.\r\n\r\n```\r\nSELECT \r\n    TOP 10 *\r\nFROM raw_pos.location\r\nWHERE city = 'London';\r\n```\r\n\r\n![assets/london.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/london.png)\r\n\r\n### Step 2 - Creating our Classifiers Schema and Placekey Custom Classifier\r\n\r\nLet's now create a `Classifier` Schema to store our Classifiers in by running the next query. This will result in a `Schema CLASSIFIERS successfully created.` message.\r\n\r\n```\r\nCREATE OR REPLACE SCHEMA classifiers\r\n    COMMENT = 'Schema containing Custom Classifiers';\r\n```\r\n\r\nWith our schema available, let's now create our `placekey` Custom Classifier by executing the next query which will provide a `Instance PLACEKEY successfully created.` message.\r\n\r\n```\r\nCREATE OR REPLACE snowflake.data_privacy.custom_classifier classifiers.placekey();\r\n```\r\n\r\n### Step 3 - Adding REGEX to our Placekey Custom Classifier\r\nNext let's test the Regular Expression (Regex) that our Data Engineer has created to locate the Placekey value by running our next query.\r\n\r\n```\r\nSELECT \r\n    placekey\r\nFROM raw_pos.location\r\nWHERE placekey REGEXP('^[a-zA-Z0-9d]{3}-[a-zA-Z0-9d]{3,4}@[a-zA-Z0-9d]{3}-[a-zA-Z0-9d]{3}-.*$');\r\n```\r\n\r\n![assets/placekey_regex.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/placekey_regex.png)\r\n\r\nLet's now use the [ADD_REGEX](https://docs.snowflake.com/en/sql-reference/classes/custom_classifier/methods/add_regex) method to assign this to our Placekey Classifier\r\n```\r\nCALL placekey!ADD_REGEX(\r\n  'PLACEKEY', -- semantic category\r\n  'IDENTIFIER', -- privacy category\r\n  '^[a-zA-Z0-9d]{3}-[a-zA-Z0-9d]{3,4}@[a-zA-Z0-9d]{3}-[a-zA-Z0-9d]{3}-.*$', -- regex expression\r\n  'PLACEKEY*', --column name regex\r\n  'Add a regex to identify Placekey' -- description\r\n);\r\n```\r\n\r\nWith the details in place, we can now use the [LIST](https://docs.snowflake.com/en/sql-reference/classes/custom_classifier/methods/list) method to validate our work\r\n\r\n```\r\nSELECT placekey!LIST();\r\n```\r\n\r\n![assets/list.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/list.png)\r\n\r\n### Step 4 - Using our Placekey Custom Classifier\r\nLet's now use [SYSTEM$CLASSIFY](https://docs.snowflake.com/en/sql-reference/stored-procedures/system_classify) and our Classifier against the `Location` table\r\n\r\n```\r\nCALL SYSTEM$CLASSIFY('raw_pos.location', {'custom_classifiers': ['placekey'], 'auto_tag':true});\r\n```\r\n\r\n![assets/classify_location.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/classify_location.png)\r\n\r\nTo finish, let's confirm our `Placekey` column was successfully tagged\r\n```\r\nSELECT \r\n    tag_name,\r\n    level, \r\n    tag_value,\r\n    column_name\r\nFROM TABLE(information_schema.tag_references_all_columns('raw_pos.location','table'))\r\nWHERE tag_value = 'PLACEKEY';\r\n```\r\n\r\n![assets/placekey_info.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/placekey_info.png)\r\n\r\nMoving forward as Schemas or Tables are created and updated we can use this exact process of Automatic and Custom Classification to maintain a strong governance posture and build rich semantic-layer metadata.\r\n\r\n\r\n## Access History (Reads and Writes)\r\n\r\n### Overview:\r\nAccess History provides insights into user queries encompassing what data was  read and when, as well as what statements have performed a write operations.\r\n \r\nFor Tasty Bytes, Access History is particularly important for Compliance, Auditing, and Governance.\r\n\r\nWithin this step, we will walk through leveraging Access History to find when the last time our Raw data was read from and written to.\r\n\r\n\u003E aside negative\r\n\u003E Access History latency is up to 3 hours. If you have just recently setupb the Tasty Bytes environment, some of the queries below may not have results. \r\n\u003E\r\n\r\n### Step 1 - Leveraging Access History\r\n\r\nBy executing the next query we will see how many queries have accessed each of your Raw layer tables directly.\r\n\r\n```\r\nSELECT \r\n    value:\"objectName\"::STRING AS object_name,\r\n    COUNT(DISTINCT query_id) AS number_of_queries\r\nFROM snowflake.account_usage.access_history,\r\nLATERAL FLATTEN (input =\u003E direct_objects_accessed)\r\nWHERE object_name ILIKE 'tb_101.raw_%'\r\nGROUP BY object_name\r\nORDER BY number_of_queries DESC;\r\n```\r\n\r\n![assets/ah_1.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/ah_1.png)\r\n*Please note your results may not match the image above*\r\n\r\nWithin the next query we will determined what is the breakdown between Read and Write queries and when did they last occur.\r\n\r\n```\r\nSELECT \r\n    value:\"objectName\"::STRING AS object_name,\r\n    CASE \r\n        WHEN object_modified_by_ddl IS NOT NULL THEN 'write'\r\n        ELSE 'read'\r\n    END AS query_type,\r\n    COUNT(DISTINCT query_id) AS number_of_queries,\r\n    MAX(query_start_time) AS last_query_start_time\r\nFROM snowflake.account_usage.access_history,\r\nLATERAL FLATTEN (input =\u003E direct_objects_accessed)\r\nWHERE object_name ILIKE 'tb_101.raw_%'\r\nGROUP BY object_name, query_type\r\nORDER BY object_name, number_of_queries DESC;\r\n```\r\n\r\n![assets/ah_2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/ah_2.png)\r\n*Please note your results may not match the image above*\r\n\r\nTo wrap things up, our last query will determine how many queries have accessed each of our Raw layer tables indirectly.\r\n\r\n```\r\nSELECT \r\n    base.value:\"objectName\"::STRING AS object_name,\r\n    COUNT(DISTINCT query_id) AS number_of_queries\r\nFROM snowflake.account_usage.access_history,\r\nLATERAL FLATTEN (input =\u003E base_objects_accessed) base,\r\nLATERAL FLATTEN (input =\u003E direct_objects_accessed) direct,\r\nWHERE 1=1\r\n    AND object_name ILIKE 'tb_101.raw_%'\r\n    AND object_name \u003C\u003E direct.value:\"objectName\"::STRING -- base object is not direct object\r\nGROUP BY object_name\r\nORDER BY number_of_queries DESC;\r\n```\r\n\r\n![assets/ah_3.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/ah_3.png)\r\n\r\n\u003E aside positive\r\n\u003E **Direct Objects Accessed:** Data objects directly named in the query explicitly.\r\n\u003E **Base Objects Accessed:** Base data objects required to execute a query.\r\n\u003E\r\n\r\n### Step 2 - Click Next --\u003E\r\n\r\n## Discovery with Snowflake Horizon - Universal Search\r\n\r\n### Overview\r\nHaving explored a wide variety of Governance functionality available in Snowflake, it is time to put it all together with Universal Search.\r\n\r\nUniversal Search enables Tasty Bytes to easily find Account objects, Snowflake Marketplace listings, relevant Snowflake Documentation and Snowflake Community Knowledge Base articles.\r\n\r\nUniversal Search understands your query and information about your database objects and can find objects with names that differ from your search terms.\r\n \r\n Even if you misspell or type only part of your search term, you can still see useful results.\r\n\r\n### Step 1 - Universal Search\r\n\r\nTo leverage Universal Search in Snowsight:\r\n- Use the Left Navigation Menu\r\n- Select \"Search\" (Magnifying Glass)\r\n- Enter Search criteria such as:\r\n    - Tasty Bytes\r\n    - Snowflake Best Practices\r\n    - How to use Snowflake Column Masking\r\n\r\n![assets/tasty_bytes.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-governance-with-horizon/tasty_bytes.png)\r\n\r\n\r\n### Step 2 - Click Next --\u003E\r\n\r\n## Conclusion and Next Steps\r\n\r\n### Conclusion\r\nFantastic work! You have successfully completed the Tasty Bytes - Zero to Snowflake - Governance with Snowflake Horizon Quickstart. \r\n\r\nBy doing so you have now:\r\n- Learned System Defined Roles Exist in Snowflake Accounts\r\n- Created a Role\r\n- Granted Privileges to a Role\r\n- Create a Tag\r\n- Created a Masking Policy\r\n- Deployed a Tag Based Masking Policy\r\n- Created a Row Access Policy using a Mapping Table\r\n- Created an Aggregation Policy\r\n- Created a Project Policy\r\n- Leveraged Automatic and Custom Data Classification\r\n- Used Universal Search\r\n\r\nIf you would like to re-run this Quickstart please leverage the Reset scripts in the bottom of your associated Worksheet.\r\n\r\n### Next Steps\r\nTo continue your journey in the Snowflake AI Data Cloud, please now visit the link below to see all other Powered by Tasty Bytes - Quickstarts available to you.\r\n\r\n- ### [Powered by Tasty Bytes - Quickstarts Table of Contents](/en/developers/guides/tasty-bytes-introduction/)\r\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-06d384e8eb","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-33975274a2",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-46e7562987","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-05-24",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-253a8965eb","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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in Snowflake\u003C/h2\u003E","\u003Ch2\u003ECreating a Worksheet and Copying in our SQL\u003C/h2\u003E","\u003Ch2\u003EExploring Available Roles\u003C/h2\u003E","\u003Ch2\u003ECreating a Role and Granting Privileges\u003C/h2\u003E","\u003Ch2\u003EColumn-Level Security and Tagging = Tag-Based Masking\u003C/h2\u003E","\u003Ch2\u003ECreating Masking Policies and Applying to Tags\u003C/h2\u003E","\u003Ch2\u003ERow-Access Policies\u003C/h2\u003E","\u003Ch2\u003EAggregation Policies\u003C/h2\u003E","\u003Ch2\u003EProjection Policies\u003C/h2\u003E","\u003Ch2\u003ESensitive Data Classification\u003C/h2\u003E","\u003Ch2\u003ECustom Data Classification\u003C/h2\u003E","\u003Ch2\u003EAccess History (Reads and Writes)\u003C/h2\u003E","\u003Ch2\u003EDiscovery with Snowflake Horizon - Universal Search\u003C/h2\u003E","\u003Ch2\u003EConclusion and Next 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