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understand, and trust data. In this lab you'll learn how Horizon ensures people have reliable and trustworthy data to make confident, data-driven decisions while ensuring observability and security of data assets.\u003C/p\u003E\n","\u003Cp\u003EIn this expert-led, hands-on lab, you will follow a step-by-step guide utilizing a provided sample database of synthetic customer orders. Using this example data, you will learn how Horizon can monitor and provide visibility into your data within Snowflake. We will examine Horizon features from three different personas within Snowflake\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Ea Data Engineer monitoring pipelines\u003C/li\u003E\u003Cli\u003Ea Data Governor monitoring and masking PII\u003C/li\u003E\u003Cli\u003Ea Data Governor Admin auditing access and lineage\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EIntroduction to Horizon\u003C/h3\u003E\n","\u003Cp\u003EBefore we dive into the lab, lets take a look at a typical governance workflow and learn a bit more about the personas we will be exploring today.\u003C/p\u003E\n","\u003Ch4\u003ETypical Governance Workflow\u003C/h4\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-horizon-for-data-governance-in-snowflake/workflow.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E\u003Ca href=\"https://youtu.be/MdZ1PaJWH2w?si=o8k8HDrzQjZ5Jhst\"\u003EData Engineer Persona Video\u003C/a\u003E\u003C/h4\u003E\n","\u003Ch4\u003E\u003Ca href=\"https://youtu.be/bF6FAMeGEZc?si=mKxGlzJL6843B-FK\"\u003EData Governor/Steward Persona Video\u003C/a\u003E\u003C/h4\u003E\n","\u003Ch4\u003E\u003Ca href=\"https://youtu.be/doView4YqUI?si=tQd_KP7YzIIvogla\"\u003EData Governor Admin Persona Video\u003C/a\u003E\u003C/h4\u003E\n","\u003Cp\u003ENow that you have the introduction to Horizon and our personas, lets get started.\u003C/p\u003E\n","\u003Ch3\u003E- What You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to protect sensitive data using Snowflake's role-based masking policies\u003C/li\u003E\u003Cli\u003EHow to visualize column-level lineage for impact analysis\u003C/li\u003E\u003Cli\u003EHow to create a Horizon dashboard in Snowsight to monitor your data and policies\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E- What You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA trial \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003ESnowflake\u003C/a\u003E Account with ACCOUNTADMIN access (recommended) or an existing Snowflake account (with ACCOUNTADMIN access)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EAll the scripts for this lab are available at \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake\"\u003ESnowflake Labs\u003C/a\u003E for you as a resource.\u003C/p\u003E\n","\u003Cp\u003ELet's get started! First we will run the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/0-lab-Setup.sql\"\u003Escript 0_lab_setup.sql\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Create a new worksheet titled 0_lab_setup\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EIn Snowsight create a new worksheet and rename it 0_lab_setup.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Copy the below script in its entirety and paste into your worksheet.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis script will create the objects and load data needed to run the lab. More explanation on these objects and how they are used will be provided in later steps.\u003C/p\u003E\n","\u003Ch3\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/0-lab-Setup.sql\"\u003Escript 0_lab_setup.sql\u003C/a\u003E\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E--Create all Roles and assign to user\nUSE ROLE SECURITYADMIN;\nCREATE OR REPLACE ROLE HRZN_DATA_ENGINEER;\nCREATE OR REPLACE ROLE HRZN_DATA_GOVERNOR;\nCREATE OR REPLACE ROLE HRZN_DATA_USER;\nCREATE OR REPLACE ROLE HRZN_IT_ADMIN;\n\nGRANT ROLE HRZN_DATA_ENGINEER TO ROLE SYSADMIN;\nGRANT ROLE HRZN_DATA_GOVERNOR TO ROLE SYSADMIN;\nGRANT ROLE HRZN_DATA_USER TO ROLE SYSADMIN;\nGRANT ROLE HRZN_IT_ADMIN TO ROLE SYSADMIN;\n\nSET MY_USER_ID  = CURRENT_USER();\nSELECT ($MY_USER_ID);\nGRANT ROLE HRZN_DATA_ENGINEER TO USER identifier($MY_USER_ID);\nGRANT ROLE HRZN_DATA_GOVERNOR TO USER identifier($MY_USER_ID);\nGRANT ROLE HRZN_DATA_USER TO USER identifier($MY_USER_ID);\nGRANT ROLE HRZN_IT_ADMIN TO USER identifier($MY_USER_ID);\n\n--Create warehouse and provide grants\nUSE ROLE SYSADMIN;\nCREATE OR REPLACE WAREHOUSE HRZN_WH WITH WAREHOUSE_SIZE='X-SMALL';\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_ENGINEER;\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_GOVERNOR;\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_USER;\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_IT_ADMIN;\n\n--Create database, schemas and assign to appropriate roles\n\nGRANT  CREATE DATABASE ON ACCOUNT TO ROLE HRZN_DATA_ENGINEER;\n\nUSE ROLE HRZN_DATA_ENGINEER;\nCREATE OR REPLACE DATABASE HRZN_DB;\nCREATE OR REPLACE SCHEMA HRZN_DB.HRZN_SCH;\n\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\nGRANT USAGE ON SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_GOVERNOR;\nGRANT CREATE SCHEMA ON DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\n\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\nGRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\n\nGRANT SELECT ON ALL TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_GOVERNOR;\nGRANT SELECT ON ALL VIEWS IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_GOVERNOR;\n\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\nGRANT USAGE ON SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_IT_ADMIN;\nGRANT CREATE SCHEMA ON DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\n\n\n\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_DATA_USER;\nGRANT USAGE ON SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_USER;\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_DATA_USER;\nGRANT SELECT ON ALL TABLES IN DATABASE HRZN_DB TO ROLE HRZN_DATA_USER;\n\nUSE ROLE HRZN_DATA_GOVERNOR;\n\n-- Create a  Schema to contain classifiers\nCREATE OR REPLACE SCHEMA HRZN_DB.CLASSIFIERS\nCOMMENT = 'Schema containing Classifiers';\n\n-- Create a Schema to contain Tags\nCREATE OR REPLACE SCHEMA HRZN_DB.TAG_SCHEMA\nCOMMENT = 'Schema containing Tags';\n\nCREATE OR REPLACE TABLE HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP\n    (role STRING, state_visibility STRING);\n\n-- with the table in place, we will now INSERT the relevant Role to City Permissions mapping to ensure\n-- our Test only can see Massachusetts customers\nINSERT INTO HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP\n    VALUES ('HRZN_DATA_USER','MA'); \n\n    \n-- Create a Schema to contain Security Policies\nCREATE OR REPLACE SCHEMA SEC_POLICIES_SCHEMA\nCOMMENT = 'Schema containing Security Policies';\n\nUSE ROLE SECURITYADMIN;\nGRANT SELECT, INSERT, UPDATE, DELETE ON FUTURE TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_GOVERNOR;\nGRANT SELECT, INSERT, UPDATE, DELETE ON FUTURE TABLES IN DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\n\nGRANT SELECT ON FUTURE TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_USER;\nGRANT SELECT ON FUTURE TABLES IN DATABASE HRZN_DB TO ROLE HRZN_DATA_USER;\n\nGRANT SELECT ON FUTURE TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_IT_ADMIN;\nGRANT SELECT ON FUTURE TABLES IN DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\n\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\nGRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\nGRANT SELECT ON ALL VIEWS IN DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\nGRANT SELECT ON ALL TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_IT_ADMIN;\nGRANT SELECT ON ALL VIEWS IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_IT_ADMIN;\n\n\nUSE ROLE ACCOUNTADMIN;\nGRANT DATABASE ROLE SNOWFLAKE.DATA_METRIC_USER TO ROLE HRZN_DATA_ENGINEER;\nGRANT EXECUTE DATA METRIC FUNCTION ON ACCOUNT TO ROLE HRZN_DATA_ENGINEER;\n-- Commented out because as of May 22 2024 this will not work in trial accounts.\n--GRANT APPLICATION ROLE SNOWFLAKE.DATA_QUALITY_MONITORING_VIEWER TO ROLE HRZN_DATA_ENGINEER;\n\nGRANT DATABASE ROLE SNOWFLAKE.GOVERNANCE_VIEWER TO ROLE HRZN_DATA_GOVERNOR;\nGRANT DATABASE ROLE SNOWFLAKE.OBJECT_VIEWER TO ROLE HRZN_DATA_GOVERNOR;\nGRANT DATABASE ROLE SNOWFLAKE.USAGE_VIEWER TO ROLE HRZN_DATA_GOVERNOR;\nGRANT DATABASE ROLE SNOWFLAKE.DATA_METRIC_USER TO ROLE HRZN_DATA_GOVERNOR;\nGRANT EXECUTE DATA METRIC FUNCTION ON ACCOUNT TO ROLE HRZN_DATA_GOVERNOR;\n-- Commented out because as of May 22 2024 this will not work in trial accounts.\n--GRANT APPLICATION ROLE SNOWFLAKE.DATA_QUALITY_MONITORING_VIEWER TO ROLE HRZN_DATA_GOVERNOR;\n\n\nGRANT DATABASE ROLE SNOWFLAKE.GOVERNANCE_VIEWER TO ROLE HRZN_IT_ADMIN;\nGRANT DATABASE ROLE SNOWFLAKE.OBJECT_VIEWER TO ROLE HRZN_IT_ADMIN;\nGRANT DATABASE ROLE SNOWFLAKE.USAGE_VIEWER TO ROLE HRZN_IT_ADMIN;\nGRANT DATABASE ROLE SNOWFLAKE.DATA_METRIC_USER TO ROLE HRZN_IT_ADMIN;\nGRANT EXECUTE DATA METRIC FUNCTION ON ACCOUNT TO ROLE HRZN_IT_ADMIN;\n-- Commented out because as of May 22 2024 this will not work in trial accounts.\n--GRANT APPLICATION ROLE SNOWFLAKE.DATA_QUALITY_MONITORING_VIEWER TO ROLE HRZN_IT_ADMIN;\n\n\n\n/***** C R E A T E   T A B L E *******/\n\nUSE ROLE HRZN_DATA_ENGINEER;\nCREATE OR REPLACE TABLE HRZN_DB.HRZN_SCH.CUSTOMER (\n ID FLOAT,\n FIRST_NAME VARCHAR,\n LAST_NAME VARCHAR,\n STREET_ADDRESS VARCHAR,\n STATE VARCHAR,\n CITY VARCHAR,\n ZIP VARCHAR,\n PHONE_NUMBER VARCHAR,\n EMAIL VARCHAR,\n SSN VARCHAR,\n BIRTHDATE VARCHAR,\n JOB VARCHAR,\n CREDITCARD VARCHAR,\n COMPANY VARCHAR,\n OPTIN VARCHAR\n);\nCREATE OR REPLACE TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS (\n    CUSTOMER_ID VARCHAR, \n    ORDER_ID VARCHAR, \n    ORDER_TS DATE, \n    ORDER_CURRENCY VARCHAR, \n    ORDER_AMOUNT FLOAT, \n    ORDER_TAX FLOAT, \n    ORDER_TOTAL FLOAT\n);\n-- Load data from S3 into target tables. Then perform GRANTS.\nCOPY INTO HRZN_DB.HRZN_SCH.CUSTOMER\nFROM s3://sfquickstarts/summit_2024_horizon_hol/CustomerDataRaw.csv\nFILE_FORMAT = (TYPE = 'CSV', SKIP_HEADER = 1)\n;\n\nCOPY INTO HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS\nFROM s3://sfquickstarts/summit_2024_horizon_hol/CustomerOrders.csv\nFILE_FORMAT = (TYPE = 'CSV', SKIP_HEADER = 1)\n;\nGRANT ALL ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER TO ROLE HRZN_DATA_GOVERNOR;\nGRANT SELECT ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER TO ROLE HRZN_DATA_USER;\nGRANT SELECT ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER TO ROLE HRZN_IT_ADMIN;\n\nGRANT ALL ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS TO ROLE HRZN_DATA_GOVERNOR;\nGRANT SELECT ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS TO ROLE HRZN_DATA_USER;\nGRANT SELECT ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS TO ROLE HRZN_IT_ADMIN;\n\n\n\nUSE ROLE ACCOUNTADMIN;\nGRANT APPLY TAG on ACCOUNT to ROLE HRZN_DATA_GOVERNOR;\nGRANT APPLY MASKING POLICY on ACCOUNT to ROLE HRZN_DATA_GOVERNOR;\nGRANT APPLY ROW ACCESS POLICY ON ACCOUNT TO ROLE HRZN_DATA_GOVERNOR;\nGRANT APPLY AGGREGATION POLICY ON ACCOUNT TO ROLE HRZN_DATA_GOVERNOR;\nGRANT APPLY PROJECTION POLICY ON ACCOUNT TO ROLE HRZN_DATA_GOVERNOR;\n\nGRANT DATABASE ROLE SNOWFLAKE.CLASSIFICATION_ADMIN TO ROLE HRZN_DATA_GOVERNOR;\n\n\n--USE ROLE HRZN_DATA_ENGINEER;\n--truncate table HRZN_DB.HRZN_SCH.CUSTOMER;\n--truncate table HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\n\n\n--Create Lineage\nUSE ROLE HRZN_DATA_ENGINEER;\n\n\nuse database HRZN_DB;\nuse schema HRZN_DB.HRZN_SCH;\nUSE WAREHOUSE HRZN_WH;\n\n-- create new table, then populate it with dynamic content\ncreate OR REPLACE table HRZN_DB.HRZN_SCH.Customer_NY as\nselect *  EXCLUDE ZIP from HRZN_DB.HRZN_SCH.CUSTOMER where state='NY';\n\ncreate OR REPLACE table HRZN_DB.HRZN_SCH.Customer_DC as\nselect *  EXCLUDE ZIP from HRZN_DB.HRZN_SCH.CUSTOMER where state='DC';\n\ncreate OR REPLACE table HRZN_DB.HRZN_SCH.Customer_AR as\nselect *  EXCLUDE ZIP from HRZN_DB.HRZN_SCH.CUSTOMER where state='AR';\n\n\nCREATE OR REPLACE VIEW HRZN_DB.HRZN_SCH.CUSTOMER_ORDER_SUMMARY AS\nSELECT C.ID, C.FIRST_NAME, C.LAST_NAME, COUNT(CO.ORDER_ID) ORDERS_COUNT, SUM(CO.ORDER_TOTAL) ORDER_TOTAL\nFROM HRZN_DB.HRZN_SCH.CUSTOMER C, HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS CO\nWHERE C.ID  = CO.CUSTOMER_ID\nGROUP BY 1,2,3;\n\nCREATE OR REPLACE TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDER_SUMMARY_NY AS\nSELECT CS.*\nFROM HRZN_DB.HRZN_SCH.Customer_NY C, HRZN_DB.HRZN_SCH.CUSTOMER_ORDER_SUMMARY CS\nWHERE C.ID  = CS.ID;\n\n-- create new user stage, then copy CUSTOMER data here\ncreate OR REPLACE stage CustomerNYStage;\ncopy into @CustomerNYStage from HRZN_DB.HRZN_SCH.CUSTOMER_ORDER_SUMMARY_NY;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EHorizon as a Data Engineer - Data Quality Monitoring\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EData Governance doesn't need to be a daunting undertaking. This section is all about how to get started with curating assets to understand common problems that most data organizations want to solve such as data quality monitoring. We will show you how easily all roles benefit from Horizon and Snowflake's RBAC Framework.\u003C/p\u003E\n","\u003Cp\u003EBefore we begin, the Snowflake Access Control Framework is based on:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERole-based Access Control (RBAC): Access privileges are assigned to roles, which\nare in turn assigned to users.\u003C/li\u003E\u003Cli\u003EDiscretionary Access Control (DAC): Each object has an owner, who can in turn\ngrant access to that object.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nThe key concepts to understanding access control in Snowflake are:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESecurable Object: An entity to which access can be granted. Unless allowed by a\ngrant, access is denied. Securable Objects are owned by a Role (as opposed to a User)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n\u003Cul\u003E\u003Cli\u003EExamples: Database, Schema, Table, View, Warehouse, Function, etc\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003ERole: An entity to which privileges can be granted. Roles are in turn assigned\nto users. Note that roles can also be assigned to other roles, creating a role\nhierarchy.\u003C/li\u003E\u003Cli\u003EPrivilege: A defined level of access to an object. Multiple distinct privileges\nmay be used to control the granularity of access granted.\u003C/li\u003E\u003Cli\u003EUser: A user identity recognized by Snowflake, whether associated with a person\nor program.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn Summary:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn Snowflake, a Role is a container for Privileges to a Securable Object.\u003C/li\u003E\u003Cli\u003EPrivileges can be granted Roles\u003C/li\u003E\u003Cli\u003ERoles can be granted to Users\u003C/li\u003E\u003Cli\u003ERoles can be granted to other Roles (which inherit that Roles Privileges)\u003C/li\u003E\u003Cli\u003EWhen Users choose a Role, they inherit all the Privileges of the Roles in the hierarchy.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESystem Defined Roles and Privileges\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nBefore beginning to deploy Role Based Access Control (RBAC) for Horizon HOL,\nlet's first take a look at the Snowflake System Defined Roles and their privileges\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn Snowsight create a new worksheet and rename it 1_Data_Engineer. Copy and paste each code block below and execute. You can also find the entire Data Engineer Script at \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/hol-lab/1-DataEngineer.sql\"\u003E1-DataEngineer.sql\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's start by assuming the Data Engineer role and our Snowflake Development Warehouse (synonymous with compute) and we will set the context with the appropriate Database and Schema.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_ENGINEER;\nUSE WAREHOUSE HRZN_WH;\nUSE DATABASE HRZN_DB;\nUSE SCHEMA HRZN_SCH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo follow best practices we will begin to investigate and deploy RBAC (Role-Based Access Control)\u003C/p\u003E\n","\u003Cp\u003EFirst, let's take a look at the Roles currently in our account\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW ROLES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis next query, will turn the output of our last SHOW command and allow us to filter on the Snowflake System Roles provided by default in all Snowflake Accounts\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: Depending on your permissions you may not see a result for every Role in the Where clause below.\u003C/p\u003E\n\u003C/blockquote\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","\u003Ch4\u003ESnowflake System Defined Role Definitions:\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EORGADMIN\u003C/strong\u003E: Role that manages operations at the organization level.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E: Role that encapsulates the SYSADMIN and SECURITYADMIN system-defined roles.\nIt is the top-level role in the system and should be granted only to a limited/controlled number of users\nin your account.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESECURITYADMIN\u003C/strong\u003E: Role that can manage any object grant globally, as well as create, monitor,\nand manage users and roles.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUSERADMIN\u003C/strong\u003E: Role that is dedicated to user and role management only.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESYSADMIN\u003C/strong\u003E: Role that has privileges to create warehouses and databases in an account.\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nIf, as recommended, you create a role hierarchy that ultimately assigns all custom roles to the SYSADMIN role, this role also has the ability to grant privileges on warehouses, databases, and other objects to other roles.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EPUBLIC: Pseudo-role that is automatically granted to every user and every role in your account. The PUBLIC role can own securable objects, just like any other role; however, the objects owned by the role are available to every other user and role in your account.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERole Creation, GRANTS and SQL Variables\u003C/h3\u003E\n","\u003Cp\u003ENow that we understand System Defined Roles, let's begin leveraging them to create a Test Role and provide it access to the Customer Order data we will deploy our initial Snowflake Horizon Governance features against.\u003C/p\u003E\n","\u003Cp\u003EWe will use the Useradmin Role to create a Data Analyst Role\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE USERADMIN;\n\nCREATE OR REPLACE ROLE HRZN_DATA_ANALYST\n    COMMENT = 'Analyst Role';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we will switch to Securityadmin to handle our privilege GRANTS\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE SECURITYADMIN;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFirst we will grant ALL privileges on the Development Warehouse to our Sysadmin\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGRANT ALL ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_ANALYST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we will grant only OPERATE and USAGE privileges to our Test Role\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGRANT OPERATE, USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_ANALYST;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003ESnowflake Warehouse Privilege Grants\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EMODIFY: Enables altering any properties of a warehouse, including changing its size.\u003C/li\u003E\u003Cli\u003EMONITOR: Enables viewing current and past queries executed on a warehouse as well as usage statistics on that warehouse.\u003C/li\u003E\u003Cli\u003EOPERATE: 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/li\u003E\u003Cli\u003EUSAGE: Enables using a virtual warehouse and, as a result, executing queries on the warehouse.\nIf the warehouse is configured to auto-resume when a SQL statement is submitted to it, the warehouse resumes automatically and executes the statement.\u003C/li\u003E\u003Cli\u003EALL: Grants all privileges, except OWNERSHIP, on the warehouse.\u003C/li\u003E\u003C/ol\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENow we will grant USAGE on our Database and all Schemas within it\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_DATA_ANALYST;\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_DATA_ANALYST;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003ESnowflake Database and Schema Grants\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EMODIFY: Enables altering any settings of a database.\u003C/li\u003E\u003Cli\u003EMONITOR: Enables performing the DESCRIBE command on the database.\u003C/li\u003E\u003Cli\u003EUSAGE: 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/li\u003E\u003Cli\u003EALL: Grants all privileges, except OWNERSHIP, on a database.\u003C/li\u003E\u003C/ol\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWe are going to test Data Governance features as our Test Role, so let's ensure it can run SELECT statements against our Data Model\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGRANT SELECT ON ALL TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_ANALYST;\nGRANT SELECT ON ALL VIEWS IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_ANALYST;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003ESnowflake View and Table Privilege Grants\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESELECT: Enables executing a SELECT statement on a table/view.\u003C/li\u003E\u003Cli\u003EINSERT: Enables executing an INSERT command on a table.\u003C/li\u003E\u003Cli\u003EUPDATE: Enables executing an UPDATE command on a table.\u003C/li\u003E\u003Cli\u003ETRUNCATE: Enables executing a TRUNCATE TABLE command on a table.\u003C/li\u003E\u003Cli\u003EDELETE: Enables executing a DELETE command on a table.\n**/\u003C/li\u003E\u003C/ol\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EBefore we proceed, let's SET a SQL Variable to equal our CURRENT_USER()\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESET MY_USER_ID  = CURRENT_USER();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we can GRANT our Role to the User we are currently logged in as and use that role\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGRANT ROLE HRZN_DATA_ANALYST TO USER identifier($MY_USER_ID);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EData Quality Monitoring\u003C/h3\u003E\n","\u003Cp\u003EWithin Snowflake, you can measure the quality of your data by using Data Metric Functions. Using these, we want to ensure that there are not duplicate or invalid Customer Email Addresses present in our system. While our team works to resolve any existing bad records, as a data engineer, we will work to monitor these occuring moving forward.\u003C/p\u003E\n","\u003Ch4\u003ECreating Data Metric functions\u003C/h4\u003E\n","\u003Cp\u003EWithin this step, we will walk through adding Data Metric Functions to our Customer Order Table to capture Duplicate and Invalid Email Address counts everytime data is updated.\u003C/p\u003E\n","\u003Cp\u003ECreating a System DMF by first setting a schedule on the table and then setting the metrics\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_ENGINEER;\n--Schedule\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';\n\n--Accuracy\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT on (EMAIL);\n\n--Uniqueness\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.UNIQUE_COUNT on (EMAIL);\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.DUPLICATE_COUNT on (EMAIL);;\n\n--Volume\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.ROW_COUNT on ();\n\n\n--Review Counts\nSELECT SNOWFLAKE.CORE.NULL_COUNT(SELECT EMAIL FROM HRZN_DB.HRZN_SCH.CUSTOMER);\nSELECT SNOWFLAKE.CORE.UNIQUE_COUNT(SELECT EMAIL FROM HRZN_DB.HRZN_SCH.CUSTOMER);\nSELECT SNOWFLAKE.CORE.DUPLICATE_COUNT (SELECT EMAIL FROM HRZN_DB.HRZN_SCH.CUSTOMER) AS duplicate_count;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBefore moving on, let's validate Trigger on Changes Schedule is in place\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW PARAMETERS LIKE 'DATA_METRIC_SCHEDULE' IN TABLE HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECreating a custom DMF\u003C/h4\u003E\n","\u003Cp\u003ETo accompany the Duplicate Count DMF, let's also create a Custom Data Metric Function that uses Regular Expression (RegEx) to Count Invalid Email Addresses\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE DATA METRIC FUNCTION HRZN_DB.HRZN_SCH.INVALID_EMAIL_COUNT(IN_TABLE TABLE(IN_COL STRING))\nRETURNS NUMBER \nAS\n'SELECT COUNT_IF(FALSE = (IN_COL regexp ''^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Za-z]{2,4}$'')) FROM IN_TABLE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor demo purposes, let's grant this to everyone\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGRANT ALL ON FUNCTION HRZN_DB.HRZN_SCH.INVALID_EMAIL_COUNT(TABLE(STRING)) TO ROLE PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs we did above, let's see how many Invalid Email Addresses currently exist\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT HRZN_DB.HRZN_SCH.INVALID_EMAIL_COUNT(SELECT EMAIL FROM HRZN_DB.HRZN_SCH.CUSTOMER) AS INVALID_EMAIL_COUNT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBefore we can apply our DMF's to the table, we must first set the Data Metric Schedule. For our demo we will trigger this to run every 5 minutes\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER SET DATA_METRIC_SCHEDULE = '5 minute'; \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nData Metric Schedule specifies the schedule for running Data Metric Functions\nfor tables and can leverage MINUTE, USING CRON or TRIGGER_ON_CHANGES\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENow we will add our Invalid Email Count Data Metric Function (DMF) to our table\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER \n    ADD DATA METRIC FUNCTION HRZN_DB.HRZN_SCH.INVALID_EMAIL_COUNT ON (EMAIL);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBefore moving on, let's validate the Schedule is in place\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW PARAMETERS LIKE 'DATA_METRIC_SCHEDULE' IN TABLE HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReview the schedule\nby selecting metric_name, ref_entity_name, schedule, schedule_status from table(information_schema.data_metric_function_references(\nref_entity_name => 'HRZN_DB.HRZN_SCH.CUSTOMER',\nref_entity_domain => 'TABLE'));\u003C/p\u003E\n","\u003Cp\u003EThe results our Data Metric Functions are written to an Event table, let's start by taking a look at the Raw output\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: Latency can be up to a few minutes. If the queries below are empty please wait a few minutes.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EFor ease of use, a flattened View is also provided so let's take a look at this as well\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nThis view will not work in a trial account.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    change_commit_time,\n    measurement_time,\n    table_schema,\n    table_name,\n    metric_name,\n    value\nFROM SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS\nWHERE table_database = 'HRZN_DB'\nORDER BY change_commit_time DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith the Data Quality metrics being logged every time our table changes we will be able to monitor the counts as new data flows in and existing e-mail updates are run.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nIn a production scenario a logical next step would be to configure alerts to notify you when changes to data quality occur. By combining the DMF and alert functionality, you can have consistent threshold notifications for data quality on the tables that you measure.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EHorizon as Data Governor - Know & protect your data\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOverview\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EIn today's world of data management, it is common to have policies and procedures that range from data quality and retention to personal data protection. A Data Governor within an organization defines and applies data policies. Here we will explore Horizon features such as \u003Cstrong\u003Euniversal search\u003C/strong\u003E that makes it easier to find Account objects,Snowflake Marketplace listings, relevant Snowflake Documentation and Snowflake Community Knowledge Base articles.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\u003C/p\u003E\n","\u003Cp\u003ENote: Universal Search understands your query and information about your database objects and can find objects with names that differ from your search terms.\nEven if you misspell or type only part of your search term, you can still see\nuseful results.\u003C/p\u003E\n\u003C/blockquote\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\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","\u003Ch3\u003ECreate a new worksheet\u003C/h3\u003E\n","\u003Cp\u003EIn snowsight create a new worksheet and rename it 2_Data_Governor. Copy and paste each code block below and execute. You can also find the entire Data Governor Script at \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/hol-lab/2-DataGovernor_DataUser.sql\"\u003E2-DataGovernor_DataUser.sql\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's start by assuming the Data User role and using our Horizon Warehouse (synonymous with compute). This lets us see what access our Data Users have to our customer data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_USER;\nUSE WAREHOUSE HRZN_WH;\nUSE DATABASE HRZN_DB;\nUSE SCHEMA HRZN_SCH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, Let’s look at the customer details\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT FIRST_NAME, LAST_NAME, STREET_ADDRESS, STATE, CITY, ZIP, PHONE_NUMBER, EMAIL, SSN, BIRTHDATE, CREDITCARD\nFROM HRZN_DB.HRZN_SCH.CUSTOMER\nSAMPLE (100 ROWS);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EProtecting Sensitive Information\u003C/h3\u003E\n","\u003Cp\u003ELooking at this table we can see there is a lot of PII and sensitive data that needs to be protected. However, as a Data user, we may not understand what fields contain the sensitive data.\u003C/p\u003E\n","\u003Cp\u003ETo set this straight, we need to ensure that the right fields are classified and tagged properly. Further, we need to mask PII and other senstive data. Lets switch to the Data governor role and we can explore the Horizon features for classification, tagging and masking.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_GOVERNOR;\nUSE WAREHOUSE HRZN_WH;\nUSE DATABASE HRZN_DB;\nUSE SCHEMA HRZN_SCH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ESensitive Data Classification\u003C/h4\u003E\n","\u003Cp\u003EIn some cases, you may not know if there is sensitive data in a table. Snowflake Horizon provides the capability to automatically detect\nsensitive 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\ntags to columns by analyzing the fields and metadata for personal data. Data\nClassification 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\ntables within a schema.\u003C/p\u003E\n","\u003Cp\u003ETo learn how to complete Data Classification within the Snowsight interface,\nplease see the following documentation:\u003C/p\u003E\n","\u003Cp\u003E[Using 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","\u003Ch4\u003EAutoclassification for Sensitive information\u003C/h4\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nOPTIONAL: You can perform classification through the UI as well.\n--Databases -> HRZN_DB -> HRZN_SCH --> Click "..." -> Classify and Tag Sensitive Data\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAs our Raw Customer Schema only includes one table, let's use SYSTEM$CLASSIFY against it\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECALL SYSTEM$CLASSIFY('HRZN_DB.HRZN_SCH.CUSTOMER', {'auto_tag': true});\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's view the new Tags Snowflake applied automatically via Data Classification\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT TAG_DATABASE, TAG_SCHEMA, OBJECT_NAME, COLUMN_NAME, TAG_NAME, TAG_VALUE\nFROM TABLE(\n  HRZN_DB.INFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS(\n    'HRZN_DB.HRZN_SCH.CUSTOMER',\n    'table'\n));\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nOPTIONAL You can perform classification through the UI as well.\n--Databases -> HRZN_DB -> HRZN_SCH --> Click "..." -> Classify and Tag Sensitive Data\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAs our Raw Point-of-Sale Schema includes numerous tables, let's use SYSTEM$CLASSIFY_SCHEMA against it\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECALL SYSTEM$CLASSIFY_SCHEMA('HRZN_DB.HRZN_SCH', {'auto_tag': true});\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce again, let's view the Tags applied using the Customer table within the Schema\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E    SELECT * FROM TABLE(HRZN_DB.information_schema.tag_references_all_columns('HRZN_DB.HRZN_SCH.CUSTOMER','table'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECustom Classification\u003C/h4\u003E\n","\u003Cp\u003ESnowflake provides the CUSTOM_CLASSIFIER class in the SNOWFLAKE.DATA_PRIVACY schema\nto enable Data Engineers / Governors to extend their Data Classification capabilities based on their own knowledge of their data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE SCHEMA HRZN_DB.CLASSIFIERS;\n\ncreate or replace snowflake.data_privacy.custom_classifier CREDITCARD();\n\nShow snowflake.data_privacy.custom_classifier;\n\nCall creditcard!add_regex('MC_PAYMENT_CARD','IDENTIFIER','^(?:5[1-5][0-9]{2}|222[1-9]|22[3-9][0-9]|2[3-6][0-9]{2}|27[01][0-9]|2720)[0-9]{12}$');\nCall creditcard!add_regex('AMX_PAYMENT_CARD','IDENTIFIER','^3[4-7][0-9]{13}$');\n\nSelect creditcard!list();\n\nselect CREDITCARD from HRZN_DB.HRZN_SCH.CUSTOMER where CREDITCARD regexp '^3[4-7][0-9]{13}$';\n\nCALL SYSTEM$CLASSIFY('HRZN_DB.HRZN_SCH.CUSTOMER',{'auto_tag': true, 'custom_classifiers': ['HRZN_DB.CLASSIFIERS.CREDITCARD']});\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nNote: This statement shows if a column is classified as a particular tag\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ESelect SYSTEM$GET_TAG('snowflake.core.semantic_category','HRZN_DB.HRZN_SCH.CUSTOMER.CREDITCARD','column');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\u003C/p\u003E\n\u003C/blockquote\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","\u003Ch4\u003ETagging\u003C/h4\u003E\n","\u003Cp\u003EA tag-based masking policy 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\u003Cpre\u003E\u003Ccode\u003EUSE SCHEMA TAG_SCHEMA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate cost_center tag and add comment\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate tag HRZN_DB.TAG_SCHEMA.cost_center allowed_values 'Sales','Marketing','Support';\nalter tag HRZN_DB.TAG_SCHEMA.cost_center set comment = 'Respective Cost center for chargeback';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate on sensitive datasets and add comments\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate tag HRZN_DB.TAG_SCHEMA.confidential allowed_values 'Sensitive','Restricted','Highly Confidential';\nalter tag HRZN_DB.TAG_SCHEMA.confidential set comment = 'Confidential information';\n                                      \ncreate tag HRZN_DB.TAG_SCHEMA.pii_type allowed_values 'Email','Phone Number','Last Name';\nalter tag HRZN_DB.TAG_SCHEMA.pii_type set comment = 'PII Columns';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EApply tag on warehouse dev_demo_wh\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter warehouse HRZN_WH set tag cost_center = 'Sales';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EApply tags at the table and column level\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E--Table Level\nalter table HRZN_DB.HRZN_SCH.customer set tag HRZN_DB.TAG_SCHEMA.confidential ='Sensitive';  \nalter table HRZN_DB.HRZN_SCH.customer set tag HRZN_DB.TAG_SCHEMA.cost_center ='Sales';  \n--Column Level\nalter table HRZN_DB.HRZN_SCH.customer modify email set tag HRZN_DB.TAG_SCHEMA.pii_type ='Email';\nalter table HRZN_DB.HRZN_SCH.customer modify phone_number set tag HRZN_DB.TAG_SCHEMA.pii_type ='Phone Number';\nalter table HRZN_DB.HRZN_SCH.customer modify last_name set tag HRZN_DB.TAG_SCHEMA.pii_type ='Last Name';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQuery account usage view to check tags and reference\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nNote: The following VIEWs have a latency of about 20 min after creating TAG objects before they will be able to display data.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from snowflake.account_usage.tag_references where tag_name ='CONFIDENTIAL' ;\nselect * from snowflake.account_usage.tag_references where tag_name ='PII_TYPE' ;\nselect * from snowflake.account_usage.tag_references where tag_name ='COST_CENTER' ;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we can use the TAG_REFERENCE_ALL_COLUMNS function to return the Tags associated with our customer order table.\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    ('HRZN_DB.HRZN_SCH.customer','table'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EDynamic Data Masking\u003C/h4\u003E\n","\u003Cp\u003EIn Snowflake it is possible to use Column-Level Security to mask dynamically and create a conditional policy. Lets see how we can combine these to create a conditional masking policy.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E--Create masking policy for PII\nCREATE OR REPLACE MASKING POLICY HRZN_DB.TAG_SCHEMA.MASK_PII AS\n  (VAL CHAR) RETURNS CHAR ->\n  CASE\n    WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN', 'HRZN_DATA_GOVERNOR') THEN VAL\n      ELSE '***PII MASKED***'\n    END;\n\n\n CREATE OR REPLACE MASKING POLICY HRZN_DB.TAG_SCHEMA.MASK_SENSITIVE AS\n  (VAL CHAR) RETURNS CHAR ->\n  CASE\n    WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN', 'HRZN_DATA_GOVERNOR') THEN VAL\n      ELSE '***SENSITIVE***'\n    END;\n\n--Apply policies to specific columns\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN SSN SET MASKING POLICY HRZN_DB.TAG_SCHEMA.MASK_PII;\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN CREDITCARD SET MASKING POLICY HRZN_DB.TAG_SCHEMA.MASK_SENSITIVE;\n\n\nSELECT SSN,CREDITCARD FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we can switch back to our Data User role from the beginning of the script. LEts see if the Data User still has access to sensitive data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_USER;\nUSE WAREHOUSE HRZN_WH;\nSELECT SSN,CREDITCARD FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe data is masked for the Data User.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_GOVERNOR;\nUSE SCHEMA HRZN_DB.TAG_SCHEMA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe Data Governor can create opt-in masking based on condition\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace masking policy HRZN_DB.TAG_SCHEMA.conditionalPolicyDemo \n   as (phone_nbr string, optin string) returns string ->\n   case\n      when optin = 'Y' then phone_nbr\n      else '***OPT OUT***'\n   end;\n\nalter table HRZN_DB.HRZN_SCH.CUSTOMER modify column PHONE_NUMBER set\n   masking policy HRZN_DB.TAG_SCHEMA.conditionalPolicyDemo  using (PHONE_NUMBER, OPTIN);\n\nSELECT PHONE_NUMBER,OPTIN FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowflake makes it possible to streamline the masking process by grouping all these sensitive or PII columns under a common tag and apply masking for that tag.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E--Create a Tag\nCREATE OR REPLACE TAG HRZN_DB.TAG_SCHEMA.PII_COL ALLOWED_VALUES 'PII-DATA','NON-PII';\n\n--Apply to the table\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN LAST_NAME SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN BIRTHDATE SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN STREET_ADDRESS SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN CITY SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN STATE SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN ZIP SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\n\n\n--Create Masking Policy\nCREATE OR REPLACE MASKING POLICY HRZN_DB.TAG_SCHEMA.PII_DATA_MASK AS (VAL string) RETURNS string ->\nCASE\nWHEN SYSTEM$GET_TAG_ON_CURRENT_COLUMN('HRZN_DB.TAG_SCHEMA.PII_COL') = 'PII-DATA' \n    AND CURRENT_ROLE() NOT IN ('HRZN_DATA_GOVERNOR','ACCOUNTADMIN') \n    THEN '**PII TAG MASKED**'\nELSE VAL\nEND;\n\n\n--Apply Masking policy to the tag\nALTER TAG HRZN_DB.TAG_SCHEMA.PII_COL SET MASKING POLICY HRZN_DB.TAG_SCHEMA.PII_DATA_MASK;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELets switch back to the Data User role and Check if the sensitive data is visible or masked\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_USER;\nSELECT FIRST_NAME, LAST_NAME, STREET_ADDRESS, CITY, STATE, ZIP \nFROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen we switch back to the Data Governor role we can see that the data is still present, just masked when required.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_GOVERNOR;\nSELECT FIRST_NAME, LAST_NAME, STREET_ADDRESS, CITY, STATE, ZIP \nFROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ERow-Access Policies\u003C/h4\u003E\n","\u003Cp\u003ENow that our Data Governor is happy with our Tag Based Dynamic Masking controlling masking at the column level, we will now look to restrict access at the row level for our Data Analyst role.\u003C/p\u003E\n","\u003Cp\u003EWithin our Customer  table, our role should only see Customers who are\nbased in Massachussets(MA).\u003C/p\u003E\n","\u003Cp\u003EFirst, We need to unset any exising masking policies on the column\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-USE\"\u003EALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN STATE UNSET TAG  HRZN_DB.TAG_SCHEMA.PII_COL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELets see what the data user can see.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_USER;\nSELECT FIRST_NAME, STREET_ADDRESS, STATE, OPTIN, PHONE_NUMBER, EMAIL, JOB, COMPANY FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe will need to use row level security to show only the Data for Massachusetts.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_GOVERNOR;\n--The mapping for the user is in the table ROW_POLICY_MAP\nSELECT * FROM HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP; \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nNote: 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.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE ROW ACCESS POLICY HRZN_DB.TAG_SCHEMA.CUSTOMER_STATE_RESTRICTIONS\n    AS (STATE STRING) RETURNS BOOLEAN ->\n       CURRENT_ROLE() IN ('ACCOUNTADMIN','HRZN_DATA_ENGINEER','HRZN_DATA_GOVERNOR') -- 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 HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP rp\n            WHERE 1=1\n                AND rp.ROLE = CURRENT_ROLE()\n                AND rp.STATE_VISIBILITY = STATE\n            )\nCOMMENT = 'Policy to limit rows returned based on mapping table of ROLE and STATE: governance.row_policy_map';\n\n\n\n -- let's now apply the Row Access Policy to our City column in the Customer Order table\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER\n    ADD ROW ACCESS POLICY HRZN_DB.TAG_SCHEMA.CUSTOMER_STATE_RESTRICTIONS ON (STATE);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith the policy successfully applied, let's test it using the Data User Role\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_USER;\nSELECT FIRST_NAME, STREET_ADDRESS, STATE, OPTIN, PHONE_NUMBER, EMAIL, JOB, COMPANY FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EAggregation Policies\u003C/h4\u003E\n","\u003Cp\u003EOutside of the Data Access Policies (Masking and Row Access) we have covered,\nSnowflake Horizon also provides Privacy Policies. In this section we will cover\nthe ability to set Aggregation Policies on Database Objects which can restrict\ncertain roles to only aggregate data by only allowing for queries that aggregate\ndata into groups of a minimum size versus retrieving individual roles.\u003C/p\u003E\n","\u003Cp\u003EFor the Data User role we have created, let's test an Aggregation Policy out against our Raw Order Header table.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nAn 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.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EFor 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\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_GOVERNOR;\n\nCREATE OR REPLACE AGGREGATION POLICY HRZN_DB.TAG_SCHEMA.aggregation_policy\n  AS () RETURNS AGGREGATION_CONSTRAINT ->\n    CASE\n      WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','HRZN_DATA_ENGINEER','HRZN_DATA_GOVERNOR')\n      THEN NO_AGGREGATION_CONSTRAINT()  \n      ELSE AGGREGATION_CONSTRAINT(MIN_GROUP_SIZE => 100) -- atleast 100 rows in aggregate\n    END;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith the Aggregation Policy created, let's apply it to our Order Header table\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS\n    SET AGGREGATION POLICY HRZN_DB.TAG_SCHEMA.aggregation_policy;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ELets try running a simple SELECT *?\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT TOP 10 * FROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EWhat happens if we include over 100 rows?\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT TOP 101 * FROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, lets switch to the Data User role and try those same queries.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_USER;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ELets try running a simple SELECT *?\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT TOP 10 * FROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EWhat happens if we include over 100 rows?\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT TOP 101 * FROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELets answer a few aggregate business questions on on the Customer Order table that we have previously:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDeployed Masking against PII columns\u003C/li\u003E\u003Cli\u003EDeployed Row Level Security to restrict our Test Role to only Massachusetts results\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EWhat are the total order amounts?\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    cl.state,\n    cl.city,\n    COUNT(oh.order_id) AS count_order,\n    SUM(oh.order_amount) AS order_total\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\n    ON oh.customer_id = cl.id\nGROUP BY ALL\nORDER BY order_total DESC;\n\nSELECT \n    cl.state,\n    cl.city,\n    COUNT(oh.order_id) AS count_order,\n    SUM(oh.order_amount) AS order_total\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\n    ON oh.customer_id = cl.id\nWHERE oh.order_amount > 64\nGROUP BY ALL\nORDER BY order_total DESC;\n\nSELECT \n    cl.state,\n    cl.city,\n    COUNT(oh.order_id) AS count_order,\n    SUM(oh.order_amount) AS order_total\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\n    ON oh.customer_id = cl.id\nWHERE oh.order_amount > 3\nGROUP BY ALL\nORDER BY order_total DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E-What are the total order amounts by company and job?\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\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\u003Cpre\u003E\u003Ccode\u003ESELECT \n   cl.company,\n   cl.job,\n   COUNT(oh.order_id) AS count_order,\n   SUM(oh.order_amount) AS order_total\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\n   ON oh.customer_id = cl.id\nGROUP BY ALL\nORDER BY order_total DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow lets try switching to our Data Governor Role, and now run that same query to see what the results look like in a privileged Role not restricted by Row Access and Aggregation policies.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_DATA_GOVERNOR;\nUSE SCHEMA HRZN_DB.TAG_SCHEMA;\n\nSELECT \n    cl.company,\n    cl.job,\n    COUNT(oh.order_id) AS count_order,\n    SUM(oh.order_amount) AS order_total\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\n    ON oh.customer_id = cl.id\nGROUP BY ALL\nORDER BY order_total DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EProjection Policies\u003C/h4\u003E\n","\u003Cp\u003EWithin this step, we will cover another Privacy Policy framework provided by Snowflake Horizon this time diving into Projection Policies which in short will prevent queries from using a SELECT statement to project values from a column.\u003C/p\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","\u003Cp\u003EFor 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\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE PROJECTION POLICY HRZN_DB.TAG_SCHEMA.projection_policy\n  AS () RETURNS PROJECTION_CONSTRAINT -> \n  CASE\n    WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','HRZN_DATA_ENGINEER', 'HRZN_DATA_GOVERNOR')\n    THEN PROJECTION_CONSTRAINT(ALLOW => true)\n    ELSE PROJECTION_CONSTRAINT(ALLOW => false)\n  END;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe need to unset any exising masking poilcies on the column\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN ZIP UNSET TAG  HRZN_DB.TAG_SCHEMA.PII_COL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith the Projection Policy in place, let's assign it to our Postal Code column\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER\n MODIFY COLUMN ZIP\n SET PROJECTION POLICY HRZN_DB.TAG_SCHEMA.projection_policy;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELets see how our projection policy works for the Data User. What does a\nSELECT * against the table yield?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E USE ROLE HRZN_DATA_USER;\nSELECT TOP 100 * FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhat if we EXCLUDE the postal_code column?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT TOP 100 * EXCLUDE ZIP FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAlthough our Projection Policy blocks our Data User Role from including the Postal Code column\nin the SELECT clause it can still be used in the WHERE clause to assist with analysis.\u003C/p\u003E\n","\u003Cp\u003EKnowing this, let's now help our marketing team by addressing a few of their questions\u003C/p\u003E\n","\u003Cp\u003EWhich CUSTOMERS from postal_code other than 97135 AND 95357 should recieve a program anniversary promotion this month?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n* EXCLUDE ZIP\nFROM HRZN_DB.HRZN_SCH.CUSTOMER\nWHERE ZIP NOT IN ('97135', '95357');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhich members from postal_code 97135 AND 95357 have have opted in for text messages?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    ID,FIRST_NAME,PHONE_NUMBER,EMAIL, COMPANY\nFROM HRZN_DB.HRZN_SCH.CUSTOMER\nWHERE ZIP IN ('97135', '95357')\n    AND OPTIN = 'Y';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow that we've protected our data and our users can access it appropriately to address their questions, let's move on to explore the Governor Admin role.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGovernor Admin - Access & Audit\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOverview\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EAccess History provides insights into user queries encompassing what data was\nread and when, as well as what statements have performed a write operations. 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.\nIn Snowsight create a new worksheet and rename it 3_Governor_Admin. Copy and paste each code block below and execute. You can also find the entire Data Governor Admin Script at \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/hol-lab/3-Data-governor-Admin.sql\"\u003E3-Data-governor-Admin.sql\u003C/a\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nNote: Access History latency is up to 3 hours.  So, some of the queries below may not have results right away.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE HRZN_IT_ADMIN;\n USE DATABASE HRZN_DB;\n USE SCHEMA HRZN_SCH;\n USE WAREHOUSE HRZN_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's check out how our data is being accessed\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHow many queries have accessed each of our Raw layer tables directly?\u003C/li\u003E\u003C/ul\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 'HRZN%'\nGROUP BY object_name\nORDER BY number_of_queries DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EWhat is the breakdown between Read and Write queries and when did they last occur?\u003C/li\u003E\u003C/ul\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 'HRZN%'\nGROUP BY object_name, query_type\nORDER BY object_name, number_of_queries DESC;\n\n-- last few "read" queries\nSELECT\n    qh.user_name,    \n    qh.query_text,\n    value:objectName::string as "TABLE"\nFROM snowflake.account_usage.query_history AS qh\nJOIN snowflake.account_usage.access_history AS ah\nON qh.query_id = ah.query_id,\n    LATERAL FLATTEN(input => ah.base_objects_accessed)\nWHERE query_type = 'SELECT' AND\n    value:objectName = 'HRZN_DB.HRZN_SCH.CUSTOMER' AND\n    start_time > dateadd(day, -90, current_date());\n\n-- last few "write" queries\nSELECT\n    qh.user_name,    \n    qh.query_text,\n    value:objectName::string as "TABLE"\nFROM snowflake.account_usage.query_history AS qh\nJOIN snowflake.account_usage.access_history AS ah\nON qh.query_id = ah.query_id,\n    LATERAL FLATTEN(input => ah.base_objects_accessed)\nWHERE query_type != 'SELECT' AND\n    value:objectName = 'HRZN_DB.HRZN_SCH.CUSTOMER' AND\n    start_time > dateadd(day, -90, current_date());\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EFind longest running queries\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\nquery_text,\nuser_name,\nrole_name,\ndatabase_name,\nwarehouse_name,\nwarehouse_size,\nexecution_status,\nround(total_elapsed_time/1000,3) elapsed_sec\nFROM snowflake.account_usage.query_history\nORDER BY total_elapsed_time desc\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EFind queries that have been executed against sensitive tables\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n  q.USER_NAME,\n  q.QUERY_TEXT,\n  q.START_TIME,\n  q.END_TIME\nFROM\n  SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY q \nWHERE\n  q.QUERY_TEXT ILIKE '%HRZN_DB.HRZN_SCH.CUSTOMER%'\nORDER BY\n  q.START_TIME DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EShow the flow of sensitive data\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    *\nFROM\n(\n    select\n      directSources.value: "objectId"::varchar as source_object_id,\n      directSources.value: "objectName"::varchar as source_object_name,\n      directSources.value: "columnName"::varchar as source_column_name,\n      'DIRECT' as source_column_type,\n      om.value: "objectName"::varchar as target_object_name,\n      columns_modified.value: "columnName"::varchar as target_column_name\n    from\n      (\n        select\n          *\n        from\n          snowflake.account_usage.access_history\n      ) t,\n      lateral flatten(input => t.OBJECTS_MODIFIED) om,\n      lateral flatten(input => om.value: "columns", outer => true) columns_modified,\n      lateral flatten(\n        input => columns_modified.value: "directSources",\n        outer => true\n      ) directSources\n    union\n// 2\n    select\n      baseSources.value: "objectId" as source_object_id,\n      baseSources.value: "objectName"::varchar as source_object_name,\n      baseSources.value: "columnName"::varchar as source_column_name,\n      'BASE' as source_column_type,\n      om.value: "objectName"::varchar as target_object_name,\n      columns_modified.value: "columnName"::varchar as target_column_name\n    from\n      (\n        select\n          *\n        from\n          snowflake.account_usage.access_history\n      ) t,\n      lateral flatten(input => t.OBJECTS_MODIFIED) om,\n      lateral flatten(input => om.value: "columns", outer => true) columns_modified,\n      lateral flatten(\n        input => columns_modified.value: "baseSources",\n        outer => true\n      ) baseSources\n) col_lin\n   WHERE\n       (SOURCE_OBJECT_NAME = 'HRZN_DB.HRZN_SCH.CUSTOMER' OR TARGET_OBJECT_NAME='HRZN_DB.HRZN_SCH.CUSTOMER')\n    AND\n        (SOURCE_COLUMN_NAME IN (\n                SELECT\n                    COLUMN_NAME\n                FROM\n                (\n                    SELECT\n                        *\n                    FROM TABLE(\n                      HRZN_DB.INFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS(\n                        'HRZN_DB.HRZN_SCH.CUSTOMER',\n                        'table'\n                      )\n                    )\n                )\n                WHERE TAG_NAME IN ('CONFIDENTIAL','PII_COL','PII_TYPE') \n            )\n            OR\n            TARGET_COLUMN_NAME IN (\n                SELECT\n                    COLUMN_NAME\n                FROM\n                (\n                    SELECT\n                        *\n                    FROM TABLE(\n                      HRZN_DB.INFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS(\n                        'HRZN_DB.HRZN_SCH.CUSTOMER',\n                        'table'\n                      )\n                    )\n                )\n                WHERE TAG_NAME IN ('CONFIDENTIAL','PII_COL','PII_TYPE') --Enter the relevant tag(s) to check against.\n            )\n            );\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EHow many queries have accessed each of our tables indirectly?\u003C/li\u003E\u003C/ul\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 'HRZN%'\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\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nDirect Objects Accessed: Data objects directly named in the query explicitly.\nBase Objects Accessed: Base data objects required to execute a query.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nClean up (Optional).\nCreate a new worksheet named 99_lab_teardown. Copy and paste the entire Teardown Script at \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/99-lab-teardown.sql\"\u003E99-lab-teardown.sql\u003C/a\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou did it! In this comprehensive lab, you have seen how Horizon:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ESecures data with role-based access control, governance policies, and more\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EMonitors data quality with both out-of-the-box and custom metrics\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAudits data usage through Access History and Schema Change Tracking\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUnderstands the flow of data through object dependencies and lineage\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create stages, databases, tables, views, and virtual warehouses.\u003C/li\u003E\u003Cli\u003EAs a Data Engineer, how to implement Data Quality Monitoring and data metric functions\u003C/li\u003E\u003Cli\u003EAs a Data Governor, how to apply column-level and row-level security and how to use projection and aggregation constraints\u003C/li\u003E\u003Cli\u003EAs a Governor Admin, how to use data lineage and dependencies to audit access and understand the flow of data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECheck out more Horizon [resources] (/en/data-cloud/horizon/) and \u003Ca href=\"https://docs.snowflake.com/en/guides-overview-govern\"\u003Edocumentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ERead the \u003Ca href=\"/resource/the-definitive-guide-to-governance-in-snowflake\"\u003EDefinitive Guide to Governance in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EJoin the \u003Ca href=\"https://community.snowflake.com/s/\"\u003ESnowflake Community\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESign up for \u003Ca href=\"http://https://community.snowflake.com/s/snowflake-university\"\u003ESnowflake University\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EFork the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/cortex-fine-tuning/site/sfguides/src/getting_started_with_llm_finetuning_using_cortex_ai/getting_started_with_llm_finetuning_using_cortex_ai.md\"\u003ERepo on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EDownload \u003Ca href=\"/content/dam/snowflake-site/developers/2024/06/LLM-Serverless-Fine-tuning-With-Snowflake-Cortex-AI-.pdf\"\u003EReference Architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/re-imagine-data-governance-with-snowflake-horizon-and-powered-by-ai-9ac1ead51b6f\"\u003ERead the Blog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://youtu.be/CML-mhhCvOA?list=TLGGiSkeWqigbJ4yNDA5MjAyNQ\"\u003EWatch the Demo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\nHorizon is a suite of native Snowflake features that allow people easily find, understand, and trust data. In this lab you'll learn how Horizon ensures people have reliable and trustworthy data to make confident, data-driven decisions while ensuring observability and security of data assets.\r\n\r\nIn this expert-led, hands-on lab, you will follow a step-by-step guide utilizing a provided sample database of synthetic customer orders. Using this example data, you will learn how Horizon can monitor and provide visibility into your data within Snowflake. We will examine Horizon features from three different personas within Snowflake \r\n - a Data Engineer monitoring pipelines\r\n - a Data Governor monitoring and masking PII\r\n - a Data Governor Admin auditing access and lineage \r\n\r\n### Introduction to Horizon\r\n\r\nBefore we dive into the lab, lets take a look at a typical governance workflow and learn a bit more about the personas we will be exploring today. \r\n#### Typical Governance Workflow  \r\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-horizon-for-data-governance-in-snowflake/workflow.png)\r\n\r\n\r\n#### [Data Engineer Persona Video](https://youtu.be/MdZ1PaJWH2w?si=o8k8HDrzQjZ5Jhst)  \r\n\r\n\r\n#### [Data Governor/Steward Persona Video](https://youtu.be/bF6FAMeGEZc?si=mKxGlzJL6843B-FK)  \r\n\r\n\r\n#### [Data Governor Admin Persona Video](https://youtu.be/doView4YqUI?si=tQd_KP7YzIIvogla)  \r\n\r\nNow that you have the introduction to Horizon and our personas, lets get started.\r\n\r\n### - What You’ll Learn \r\n- How to protect sensitive data using Snowflake's role-based masking policies\r\n- How to visualize column-level lineage for impact analysis\r\n- How to create a Horizon dashboard in Snowsight to monitor your data and policies\r\n\r\n### - What You’ll Need \r\n- A trial [Snowflake](https://signup.snowflake.com/?utm_cta=quickstarts_) Account with ACCOUNTADMIN access (recommended) or an existing Snowflake account (with ACCOUNTADMIN access)\r\n\u003C!-- ------------------------ --\u003E\r\n## Setup\r\n\r\nAll the scripts for this lab are available at [Snowflake Labs](https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake) for you as a resource.\r\n\r\nLet's get started! First we will run the [script 0_lab_setup.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/0-lab-Setup.sql) \r\n\r\n\r\n**1. Create a new worksheet titled 0_lab_setup**\r\n\r\nIn Snowsight create a new worksheet and rename it 0_lab_setup.\r\n\r\n**2. Copy the below script in its entirety and paste into your worksheet.**\r\n\r\nThis script will create the objects and load data needed to run the lab. More explanation on these objects and how they are used will be provided in later steps.\r\n### [script 0_lab_setup.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/0-lab-Setup.sql)\r\n\r\n````\r\n--Create all Roles and assign to user\r\nUSE ROLE SECURITYADMIN;\r\nCREATE OR REPLACE ROLE HRZN_DATA_ENGINEER;\r\nCREATE OR REPLACE ROLE HRZN_DATA_GOVERNOR;\r\nCREATE OR REPLACE ROLE HRZN_DATA_USER;\r\nCREATE OR REPLACE ROLE HRZN_IT_ADMIN;\r\n\r\nGRANT ROLE HRZN_DATA_ENGINEER TO ROLE SYSADMIN;\r\nGRANT ROLE HRZN_DATA_GOVERNOR TO ROLE SYSADMIN;\r\nGRANT ROLE HRZN_DATA_USER TO ROLE SYSADMIN;\r\nGRANT ROLE HRZN_IT_ADMIN TO ROLE SYSADMIN;\r\n\r\nSET MY_USER_ID  = CURRENT_USER();\r\nSELECT ($MY_USER_ID);\r\nGRANT ROLE HRZN_DATA_ENGINEER TO USER identifier($MY_USER_ID);\r\nGRANT ROLE HRZN_DATA_GOVERNOR TO USER identifier($MY_USER_ID);\r\nGRANT ROLE HRZN_DATA_USER TO USER identifier($MY_USER_ID);\r\nGRANT ROLE HRZN_IT_ADMIN TO USER identifier($MY_USER_ID);\r\n\r\n--Create warehouse and provide grants\r\nUSE ROLE SYSADMIN;\r\nCREATE OR REPLACE WAREHOUSE HRZN_WH WITH WAREHOUSE_SIZE='X-SMALL';\r\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_ENGINEER;\r\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_USER;\r\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_IT_ADMIN;\r\n\r\n--Create database, schemas and assign to appropriate roles\r\n\r\nGRANT  CREATE DATABASE ON ACCOUNT TO ROLE HRZN_DATA_ENGINEER;\r\n\r\nUSE ROLE HRZN_DATA_ENGINEER;\r\nCREATE OR REPLACE DATABASE HRZN_DB;\r\nCREATE OR REPLACE SCHEMA HRZN_DB.HRZN_SCH;\r\n\r\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT USAGE ON SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT CREATE SCHEMA ON DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\r\n\r\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\r\n\r\nGRANT SELECT ON ALL TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT SELECT ON ALL VIEWS IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_GOVERNOR;\r\n\r\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\r\nGRANT USAGE ON SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_IT_ADMIN;\r\nGRANT CREATE SCHEMA ON DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\r\n\r\n\r\n\r\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_DATA_USER;\r\nGRANT USAGE ON SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_USER;\r\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_DATA_USER;\r\nGRANT SELECT ON ALL TABLES IN DATABASE HRZN_DB TO ROLE HRZN_DATA_USER;\r\n\r\nUSE ROLE HRZN_DATA_GOVERNOR;\r\n\r\n-- Create a  Schema to contain classifiers\r\nCREATE OR REPLACE SCHEMA HRZN_DB.CLASSIFIERS\r\nCOMMENT = 'Schema containing Classifiers';\r\n\r\n-- Create a Schema to contain Tags\r\nCREATE OR REPLACE SCHEMA HRZN_DB.TAG_SCHEMA\r\nCOMMENT = 'Schema containing Tags';\r\n\r\nCREATE OR REPLACE TABLE HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP\r\n    (role STRING, state_visibility STRING);\r\n\r\n-- with the table in place, we will now INSERT the relevant Role to City Permissions mapping to ensure\r\n-- our Test only can see Massachusetts customers\r\nINSERT INTO HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP\r\n    VALUES ('HRZN_DATA_USER','MA'); \r\n\r\n    \r\n-- Create a Schema to contain Security Policies\r\nCREATE OR REPLACE SCHEMA SEC_POLICIES_SCHEMA\r\nCOMMENT = 'Schema containing Security Policies';\r\n\r\nUSE ROLE SECURITYADMIN;\r\nGRANT SELECT, INSERT, UPDATE, DELETE ON FUTURE TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT SELECT, INSERT, UPDATE, DELETE ON FUTURE TABLES IN DATABASE HRZN_DB TO ROLE HRZN_DATA_GOVERNOR;\r\n\r\nGRANT SELECT ON FUTURE TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_USER;\r\nGRANT SELECT ON FUTURE TABLES IN DATABASE HRZN_DB TO ROLE HRZN_DATA_USER;\r\n\r\nGRANT SELECT ON FUTURE TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_IT_ADMIN;\r\nGRANT SELECT ON FUTURE TABLES IN DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\r\n\r\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\r\nGRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\r\nGRANT SELECT ON ALL VIEWS IN DATABASE HRZN_DB TO ROLE HRZN_IT_ADMIN;\r\nGRANT SELECT ON ALL TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_IT_ADMIN;\r\nGRANT SELECT ON ALL VIEWS IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_IT_ADMIN;\r\n\r\n\r\nUSE ROLE ACCOUNTADMIN;\r\nGRANT DATABASE ROLE SNOWFLAKE.DATA_METRIC_USER TO ROLE HRZN_DATA_ENGINEER;\r\nGRANT EXECUTE DATA METRIC FUNCTION ON ACCOUNT TO ROLE HRZN_DATA_ENGINEER;\r\n-- Commented out because as of May 22 2024 this will not work in trial accounts.\r\n--GRANT APPLICATION ROLE SNOWFLAKE.DATA_QUALITY_MONITORING_VIEWER TO ROLE HRZN_DATA_ENGINEER;\r\n\r\nGRANT DATABASE ROLE SNOWFLAKE.GOVERNANCE_VIEWER TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT DATABASE ROLE SNOWFLAKE.OBJECT_VIEWER TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT DATABASE ROLE SNOWFLAKE.USAGE_VIEWER TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT DATABASE ROLE SNOWFLAKE.DATA_METRIC_USER TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT EXECUTE DATA METRIC FUNCTION ON ACCOUNT TO ROLE HRZN_DATA_GOVERNOR;\r\n-- Commented out because as of May 22 2024 this will not work in trial accounts.\r\n--GRANT APPLICATION ROLE SNOWFLAKE.DATA_QUALITY_MONITORING_VIEWER TO ROLE HRZN_DATA_GOVERNOR;\r\n\r\n\r\nGRANT DATABASE ROLE SNOWFLAKE.GOVERNANCE_VIEWER TO ROLE HRZN_IT_ADMIN;\r\nGRANT DATABASE ROLE SNOWFLAKE.OBJECT_VIEWER TO ROLE HRZN_IT_ADMIN;\r\nGRANT DATABASE ROLE SNOWFLAKE.USAGE_VIEWER TO ROLE HRZN_IT_ADMIN;\r\nGRANT DATABASE ROLE SNOWFLAKE.DATA_METRIC_USER TO ROLE HRZN_IT_ADMIN;\r\nGRANT EXECUTE DATA METRIC FUNCTION ON ACCOUNT TO ROLE HRZN_IT_ADMIN;\r\n-- Commented out because as of May 22 2024 this will not work in trial accounts.\r\n--GRANT APPLICATION ROLE SNOWFLAKE.DATA_QUALITY_MONITORING_VIEWER TO ROLE HRZN_IT_ADMIN;\r\n\r\n\r\n\r\n/***** C R E A T E   T A B L E *******/\r\n\r\nUSE ROLE HRZN_DATA_ENGINEER;\r\nCREATE OR REPLACE TABLE HRZN_DB.HRZN_SCH.CUSTOMER (\r\n ID FLOAT,\r\n FIRST_NAME VARCHAR,\r\n LAST_NAME VARCHAR,\r\n STREET_ADDRESS VARCHAR,\r\n STATE VARCHAR,\r\n CITY VARCHAR,\r\n ZIP VARCHAR,\r\n PHONE_NUMBER VARCHAR,\r\n EMAIL VARCHAR,\r\n SSN VARCHAR,\r\n BIRTHDATE VARCHAR,\r\n JOB VARCHAR,\r\n CREDITCARD VARCHAR,\r\n COMPANY VARCHAR,\r\n OPTIN VARCHAR\r\n);\r\nCREATE OR REPLACE TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS (\r\n    CUSTOMER_ID VARCHAR, \r\n    ORDER_ID VARCHAR, \r\n    ORDER_TS DATE, \r\n    ORDER_CURRENCY VARCHAR, \r\n    ORDER_AMOUNT FLOAT, \r\n    ORDER_TAX FLOAT, \r\n    ORDER_TOTAL FLOAT\r\n);\r\n-- Load data from S3 into target tables. Then perform GRANTS.\r\nCOPY INTO HRZN_DB.HRZN_SCH.CUSTOMER\r\nFROM s3://sfquickstarts/summit_2024_horizon_hol/CustomerDataRaw.csv\r\nFILE_FORMAT = (TYPE = 'CSV', SKIP_HEADER = 1)\r\n;\r\n\r\nCOPY INTO HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS\r\nFROM s3://sfquickstarts/summit_2024_horizon_hol/CustomerOrders.csv\r\nFILE_FORMAT = (TYPE = 'CSV', SKIP_HEADER = 1)\r\n;\r\nGRANT ALL ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT SELECT ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER TO ROLE HRZN_DATA_USER;\r\nGRANT SELECT ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER TO ROLE HRZN_IT_ADMIN;\r\n\r\nGRANT ALL ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT SELECT ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS TO ROLE HRZN_DATA_USER;\r\nGRANT SELECT ON TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS TO ROLE HRZN_IT_ADMIN;\r\n\r\n\r\n\r\nUSE ROLE ACCOUNTADMIN;\r\nGRANT APPLY TAG on ACCOUNT to ROLE HRZN_DATA_GOVERNOR;\r\nGRANT APPLY MASKING POLICY on ACCOUNT to ROLE HRZN_DATA_GOVERNOR;\r\nGRANT APPLY ROW ACCESS POLICY ON ACCOUNT TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT APPLY AGGREGATION POLICY ON ACCOUNT TO ROLE HRZN_DATA_GOVERNOR;\r\nGRANT APPLY PROJECTION POLICY ON ACCOUNT TO ROLE HRZN_DATA_GOVERNOR;\r\n\r\nGRANT DATABASE ROLE SNOWFLAKE.CLASSIFICATION_ADMIN TO ROLE HRZN_DATA_GOVERNOR;\r\n\r\n\r\n--USE ROLE HRZN_DATA_ENGINEER;\r\n--truncate table HRZN_DB.HRZN_SCH.CUSTOMER;\r\n--truncate table HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\r\n\r\n\r\n--Create Lineage\r\nUSE ROLE HRZN_DATA_ENGINEER;\r\n\r\n\r\nuse database HRZN_DB;\r\nuse schema HRZN_DB.HRZN_SCH;\r\nUSE WAREHOUSE HRZN_WH;\r\n\r\n-- create new table, then populate it with dynamic content\r\ncreate OR REPLACE table HRZN_DB.HRZN_SCH.Customer_NY as\r\nselect *  EXCLUDE ZIP from HRZN_DB.HRZN_SCH.CUSTOMER where state='NY';\r\n\r\ncreate OR REPLACE table HRZN_DB.HRZN_SCH.Customer_DC as\r\nselect *  EXCLUDE ZIP from HRZN_DB.HRZN_SCH.CUSTOMER where state='DC';\r\n\r\ncreate OR REPLACE table HRZN_DB.HRZN_SCH.Customer_AR as\r\nselect *  EXCLUDE ZIP from HRZN_DB.HRZN_SCH.CUSTOMER where state='AR';\r\n\r\n\r\nCREATE OR REPLACE VIEW HRZN_DB.HRZN_SCH.CUSTOMER_ORDER_SUMMARY AS\r\nSELECT C.ID, C.FIRST_NAME, C.LAST_NAME, COUNT(CO.ORDER_ID) ORDERS_COUNT, SUM(CO.ORDER_TOTAL) ORDER_TOTAL\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER C, HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS CO\r\nWHERE C.ID  = CO.CUSTOMER_ID\r\nGROUP BY 1,2,3;\r\n\r\nCREATE OR REPLACE TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDER_SUMMARY_NY AS\r\nSELECT CS.*\r\nFROM HRZN_DB.HRZN_SCH.Customer_NY C, HRZN_DB.HRZN_SCH.CUSTOMER_ORDER_SUMMARY CS\r\nWHERE C.ID  = CS.ID;\r\n\r\n-- create new user stage, then copy CUSTOMER data here\r\ncreate OR REPLACE stage CustomerNYStage;\r\ncopy into @CustomerNYStage from HRZN_DB.HRZN_SCH.CUSTOMER_ORDER_SUMMARY_NY;\r\n````\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Horizon as a Data Engineer - Data Quality Monitoring\r\n\r\n### Overview\r\nData Governance doesn't need to be a daunting undertaking. This section is all about how to get started with curating assets to understand common problems that most data organizations want to solve such as data quality monitoring. We will show you how easily all roles benefit from Horizon and Snowflake's RBAC Framework. \r\n\r\nBefore we begin, the Snowflake Access Control Framework is based on:\r\n- Role-based Access Control (RBAC): Access privileges are assigned to roles, which \r\n    are in turn assigned to users.\r\n- Discretionary Access Control (DAC): Each object has an owner, who can in turn \r\n    grant access to that object.\r\n\r\n\u003E aside negative\r\n\u003EThe key concepts to understanding access control in Snowflake are:\r\n\u003E- Securable Object: An entity to which access can be granted. Unless allowed by a \r\n    grant, access is denied. Securable Objects are owned by a Role (as opposed to a User)\r\n\u003E\u003E- Examples: Database, Schema, Table, View, Warehouse, Function, etc\r\n\u003E- Role: An entity to which privileges can be granted. Roles are in turn assigned \r\n    to users. Note that roles can also be assigned to other roles, creating a role \r\n    hierarchy.\r\n\u003E- Privilege: A defined level of access to an object. Multiple distinct privileges \r\n    may be used to control the granularity of access granted.\r\n \u003E- User: A user identity recognized by Snowflake, whether associated with a person \r\n    or program.\r\n\r\nIn Summary:\r\n- In Snowflake, a Role is a container for Privileges to a Securable Object.\r\n- Privileges can be granted Roles\r\n- Roles can be granted to Users\r\n- Roles can be granted to other Roles (which inherit that Roles Privileges)\r\n- When Users choose a Role, they inherit all the Privileges of the Roles in the hierarchy.\r\n\r\n\r\n### System Defined Roles and Privileges\r\n\u003E aside positive\r\n \u003EBefore beginning to deploy Role Based Access Control (RBAC) for Horizon HOL,\r\n let's first take a look at the Snowflake System Defined Roles and their privileges\r\n\r\nIn Snowsight create a new worksheet and rename it 1_Data_Engineer. Copy and paste each code block below and execute. You can also find the entire Data Engineer Script at [ 1-DataEngineer.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/hol-lab/1-DataEngineer.sql) \r\n\r\nLet's start by assuming the Data Engineer role and our Snowflake Development Warehouse (synonymous with compute) and we will set the context with the appropriate Database and Schema. \r\n```\r\nUSE ROLE HRZN_DATA_ENGINEER;\r\nUSE WAREHOUSE HRZN_WH;\r\nUSE DATABASE HRZN_DB;\r\nUSE SCHEMA HRZN_SCH;\r\n```\r\n\r\nTo follow best practices we will begin to investigate and deploy RBAC (Role-Based Access Control)\r\n\r\nFirst, let's take a look at the Roles currently in our account\r\n```\r\nSHOW ROLES;\r\n```\r\n\r\nThis next query, will turn the output of our last SHOW command and allow us to filter on the Snowflake System Roles provided by default in all Snowflake Accounts\r\n  \u003E Note: Depending on your permissions you may not see a result for every Role in the Where clause below.\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#### Snowflake System Defined Role Definitions:\r\n1. **ORGADMIN**: Role that manages operations at the organization level.\r\n2. **ACCOUNTADMIN**: Role that encapsulates the SYSADMIN and SECURITYADMIN system-defined roles.\r\n            It is the top-level role in the system and should be granted only to a limited/controlled number of users\r\n            in your account.\r\n3. **SECURITYADMIN**: Role that can manage any object grant globally, as well as create, monitor,\r\n          and manage users and roles.\r\n4. **USERADMIN**: Role that is dedicated to user and role management only.\r\n5. **SYSADMIN**: Role that has privileges to create warehouses and databases in an account.\r\n\u003E aside negative\r\n\u003EIf, as recommended, you create a role hierarchy that ultimately assigns all custom roles to the SYSADMIN role, this role also has the ability to grant privileges on warehouses, databases, and other objects to other roles.\r\n6. PUBLIC: Pseudo-role that is automatically granted to every user and every role in your account. The PUBLIC role can own securable objects, just like any other role; however, the objects owned by the role are available to every other user and role in your account.\r\n\r\n \r\n                       \r\n### Role Creation, GRANTS and SQL Variables\r\n\r\nNow that we understand System Defined Roles, let's begin leveraging them to create a Test Role and provide it access to the Customer Order data we will deploy our initial Snowflake Horizon Governance features against.\r\n\r\nWe will use the Useradmin Role to create a Data Analyst Role\r\n```\r\nUSE ROLE USERADMIN;\r\n\r\nCREATE OR REPLACE ROLE HRZN_DATA_ANALYST\r\n    COMMENT = 'Analyst Role';\r\n```\r\n\r\nNow we will switch to Securityadmin to handle our privilege GRANTS\r\n```\r\nUSE ROLE SECURITYADMIN;\r\n```\r\nFirst we will grant ALL privileges on the Development Warehouse to our Sysadmin\r\n```\r\nGRANT ALL ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_ANALYST;\r\n```\r\n\r\nNext we will grant only OPERATE and USAGE privileges to our Test Role\r\n```\r\nGRANT OPERATE, USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_ANALYST;\r\n```\r\n\r\n\u003E aside positive\r\n\u003E**Snowflake Warehouse Privilege Grants**\r\n\u003E1. MODIFY: Enables altering any properties of a warehouse, including changing its size.\r\n\u003E2. MONITOR: Enables viewing current and past queries executed on a warehouse as well as usage statistics on that warehouse.\r\n\u003E3. 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\u003E4. USAGE: Enables using a virtual warehouse and, as a result, executing queries on the warehouse.\r\nIf 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\u003E5. ALL: Grants all privileges, except OWNERSHIP, on the warehouse.\r\n\r\nNow we will grant USAGE on our Database and all Schemas within it\r\n```\r\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_DATA_ANALYST;\r\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_DATA_ANALYST;\r\n```\r\n\u003E aside positive\r\n\u003E **Snowflake Database and Schema Grants**\r\n\u003E1. MODIFY: Enables altering any settings of a database.\r\n\u003E2. MONITOR: Enables performing the DESCRIBE command on the database.\r\n\u003E3. 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\u003E4. ALL: Grants all privileges, except OWNERSHIP, on a database.\r\n\r\nWe are going to test Data Governance features as our Test Role, so let's ensure it can run SELECT statements against our Data Model\r\n```\r\nGRANT SELECT ON ALL TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_ANALYST;\r\nGRANT SELECT ON ALL VIEWS IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_ANALYST;\r\n```\r\n\u003E aside positive\r\n\u003E**Snowflake View and Table Privilege Grants**\r\n\u003E1. SELECT: Enables executing a SELECT statement on a table/view.\r\n\u003E2. INSERT: Enables executing an INSERT command on a table. \r\n\u003E3. UPDATE: Enables executing an UPDATE command on a table.\r\n\u003E4. TRUNCATE: Enables executing a TRUNCATE TABLE command on a table.\r\n\u003E5. DELETE: Enables executing a DELETE command on a table.\r\n    **/\r\n\r\nBefore we proceed, let's SET a SQL Variable to equal our CURRENT_USER()\r\n```\r\nSET MY_USER_ID  = CURRENT_USER();\r\n```\r\n\r\nNow we can GRANT our Role to the User we are currently logged in as and use that role\r\n```\r\nGRANT ROLE HRZN_DATA_ANALYST TO USER identifier($MY_USER_ID);\r\n```\r\n\r\n### Data Quality Monitoring \r\n\r\nWithin Snowflake, you can measure the quality of your data by using Data Metric Functions. Using these, we want to ensure that there are not duplicate or invalid Customer Email Addresses present in our system. While our team works to resolve any existing bad records, as a data engineer, we will work to monitor these occuring moving forward.\r\n\r\n#### Creating Data Metric functions\r\nWithin this step, we will walk through adding Data Metric Functions to our Customer Order Table to capture Duplicate and Invalid Email Address counts everytime data is updated.\r\n\r\nCreating a System DMF by first setting a schedule on the table and then setting the metrics\r\n```\r\nUSE ROLE HRZN_DATA_ENGINEER;\r\n--Schedule\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';\r\n\r\n--Accuracy\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT on (EMAIL);\r\n\r\n--Uniqueness\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.UNIQUE_COUNT on (EMAIL);\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.DUPLICATE_COUNT on (EMAIL);;\r\n\r\n--Volume\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.ROW_COUNT on ();\r\n\r\n\r\n--Review Counts\r\nSELECT SNOWFLAKE.CORE.NULL_COUNT(SELECT EMAIL FROM HRZN_DB.HRZN_SCH.CUSTOMER);\r\nSELECT SNOWFLAKE.CORE.UNIQUE_COUNT(SELECT EMAIL FROM HRZN_DB.HRZN_SCH.CUSTOMER);\r\nSELECT SNOWFLAKE.CORE.DUPLICATE_COUNT (SELECT EMAIL FROM HRZN_DB.HRZN_SCH.CUSTOMER) AS duplicate_count;\r\n```\r\n\r\nBefore moving on, let's validate Trigger on Changes Schedule is in place\r\n```\r\nSHOW PARAMETERS LIKE 'DATA_METRIC_SCHEDULE' IN TABLE HRZN_DB.HRZN_SCH.CUSTOMER;\r\n```\r\n\r\n#### Creating a custom DMF\r\nTo accompany the Duplicate Count DMF, let's also create a Custom Data Metric Function that uses Regular Expression (RegEx) to Count Invalid Email Addresses\r\n```\r\nCREATE DATA METRIC FUNCTION HRZN_DB.HRZN_SCH.INVALID_EMAIL_COUNT(IN_TABLE TABLE(IN_COL STRING))\r\nRETURNS NUMBER \r\nAS\r\n'SELECT COUNT_IF(FALSE = (IN_COL regexp ''^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Za-z]{2,4}$'')) FROM IN_TABLE';\r\n```\r\n\r\nFor demo purposes, let's grant this to everyone\r\n```\r\nGRANT ALL ON FUNCTION HRZN_DB.HRZN_SCH.INVALID_EMAIL_COUNT(TABLE(STRING)) TO ROLE PUBLIC;\r\n```\r\n\r\nAs we did above, let's see how many Invalid Email Addresses currently exist\r\n```\r\nSELECT HRZN_DB.HRZN_SCH.INVALID_EMAIL_COUNT(SELECT EMAIL FROM HRZN_DB.HRZN_SCH.CUSTOMER) AS INVALID_EMAIL_COUNT;\r\n```\r\n\r\nBefore we can apply our DMF's to the table, we must first set the Data Metric Schedule. For our demo we will trigger this to run every 5 minutes\r\n```\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER SET DATA_METRIC_SCHEDULE = '5 minute'; \r\n```\r\n\u003E aside negative\r\n\u003EData Metric Schedule specifies the schedule for running Data Metric Functions\r\nfor tables and can leverage MINUTE, USING CRON or TRIGGER_ON_CHANGES\r\n\r\nNow we will add our Invalid Email Count Data Metric Function (DMF) to our table\r\n```\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER \r\n    ADD DATA METRIC FUNCTION HRZN_DB.HRZN_SCH.INVALID_EMAIL_COUNT ON (EMAIL);\r\n```\r\n\r\nBefore moving on, let's validate the Schedule is in place\r\n```\r\nSHOW PARAMETERS LIKE 'DATA_METRIC_SCHEDULE' IN TABLE HRZN_DB.HRZN_SCH.CUSTOMER;\r\n```\r\n\r\nReview the schedule\r\nby selecting metric_name, ref_entity_name, schedule, schedule_status from table(information_schema.data_metric_function_references(\r\n    ref_entity_name =\u003E 'HRZN_DB.HRZN_SCH.CUSTOMER', \r\n    ref_entity_domain =\u003E 'TABLE'));\r\n\r\n\r\nThe results our Data Metric Functions are written to an Event table, let's start by taking a look at the Raw output\r\n\u003E Note: Latency can be up to a few minutes. If the queries below are empty please wait a few minutes.\r\n\r\nFor ease of use, a flattened View is also provided so let's take a look at this as well\r\n\r\n\u003E aside negative\r\n\u003EThis view will not work in a trial account.\r\n```\r\nSELECT \r\n    change_commit_time,\r\n    measurement_time,\r\n    table_schema,\r\n    table_name,\r\n    metric_name,\r\n    value\r\nFROM SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS\r\nWHERE table_database = 'HRZN_DB'\r\nORDER BY change_commit_time DESC;\r\n```\r\n\r\n\r\n With the Data Quality metrics being logged every time our table changes we will be able to monitor the counts as new data flows in and existing e-mail updates are run.\r\n\r\n\u003E aside negative\r\n\u003EIn a production scenario a logical next step would be to configure alerts to notify you when changes to data quality occur. By combining the DMF and alert functionality, you can have consistent threshold notifications for data quality on the tables that you measure. \r\n\r\n\r\n\r\n## Horizon as Data Governor - Know & protect your data\r\n\r\n**Overview**\r\n\r\nIn today's world of data management, it is common to have policies and procedures that range from data quality and retention to personal data protection. A Data Governor within an organization defines and applies data policies. Here we will explore Horizon features such as **universal search** that makes it easier to find Account objects,Snowflake Marketplace listings, relevant Snowflake Documentation and Snowflake Community Knowledge Base articles.\r\n\r\n\u003E aside negative\r\n\u003E\r\n\u003ENote: Universal Search understands your query and information about your database objects and can find objects with names that differ from your search terms.\r\n\u003EEven if you misspell or type only part of your search term, you can still see\r\nuseful results.\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    - Snowflake Best Practices\r\n    - How to use Snowflake Column Masking\r\n\r\n### Create a new worksheet\r\n  In snowsight create a new worksheet and rename it 2_Data_Governor. Copy and paste each code block below and execute. You can also find the entire Data Governor Script at [ 2-DataGovernor_DataUser.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/hol-lab/2-DataGovernor_DataUser.sql)  \r\n\r\nLet's start by assuming the Data User role and using our Horizon Warehouse (synonymous with compute). This lets us see what access our Data Users have to our customer data.\r\n\r\n````\r\nUSE ROLE HRZN_DATA_USER;\r\nUSE WAREHOUSE HRZN_WH;\r\nUSE DATABASE HRZN_DB;\r\nUSE SCHEMA HRZN_SCH;\r\n````\r\n\r\nNow, Let’s look at the customer details\r\n````\r\nSELECT FIRST_NAME, LAST_NAME, STREET_ADDRESS, STATE, CITY, ZIP, PHONE_NUMBER, EMAIL, SSN, BIRTHDATE, CREDITCARD\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER\r\nSAMPLE (100 ROWS);\r\n````\r\n\r\n### Protecting Sensitive Information\r\nLooking at this table we can see there is a lot of PII and sensitive data that needs to be protected. However, as a Data user, we may not understand what fields contain the sensitive data.\r\n\r\nTo set this straight, we need to ensure that the right fields are classified and tagged properly. Further, we need to mask PII and other senstive data. Lets switch to the Data governor role and we can explore the Horizon features for classification, tagging and masking.\r\n````\r\nUSE ROLE HRZN_DATA_GOVERNOR;\r\nUSE WAREHOUSE HRZN_WH;\r\nUSE DATABASE HRZN_DB;\r\nUSE SCHEMA HRZN_SCH;\r\n````\r\n#### Sensitive Data Classification\r\n\r\n In some cases, you may not know if there is sensitive data in a table. Snowflake Horizon provides the capability to automatically detect\r\n sensitive information and apply relevant Snowflake system defined privacy tags. \r\n\r\n Classification is a multi-step process that associates Snowflake-defined system\r\n tags to columns by analyzing the fields and metadata for personal data. Data \r\n Classification can be done via SQL or the Snowsight interface.\r\n\r\n Within this step we will be using SQL to classify a single table as well as all\r\n tables within a schema.\r\n\r\n To learn how to complete Data Classification within the Snowsight interface,\r\n please see the following documentation: \r\n\r\n [Using 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#### Autoclassification for Sensitive information\r\n\u003Easide negative\r\n\u003EOPTIONAL: You can perform classification through the UI as well.\r\n--Databases -\u003E HRZN_DB -\u003E HRZN_SCH --\u003E Click \"...\" -\u003E Classify and Tag Sensitive Data\r\n\r\nAs our Raw Customer Schema only includes one table, let's use SYSTEM$CLASSIFY against it\r\n````\r\nCALL SYSTEM$CLASSIFY('HRZN_DB.HRZN_SCH.CUSTOMER', {'auto_tag': true});\r\n````\r\n\r\nNow let's view the new Tags Snowflake applied automatically via Data Classification\r\n````\r\nSELECT TAG_DATABASE, TAG_SCHEMA, OBJECT_NAME, COLUMN_NAME, TAG_NAME, TAG_VALUE\r\nFROM TABLE(\r\n  HRZN_DB.INFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS(\r\n    'HRZN_DB.HRZN_SCH.CUSTOMER',\r\n    'table'\r\n));\r\n````\r\n\u003Easide negative\r\n\u003EOPTIONAL You can perform classification through the UI as well.\r\n--Databases -\u003E HRZN_DB -\u003E HRZN_SCH --\u003E Click \"...\" -\u003E Classify and Tag Sensitive Data\r\n\r\nAs our Raw Point-of-Sale Schema includes numerous tables, let's use SYSTEM$CLASSIFY_SCHEMA against it\r\n````\r\nCALL SYSTEM$CLASSIFY_SCHEMA('HRZN_DB.HRZN_SCH', {'auto_tag': true});\r\n````\r\n\r\nOnce again, let's view the Tags applied using the Customer table within the Schema\r\n````\r\n    SELECT * FROM TABLE(HRZN_DB.information_schema.tag_references_all_columns('HRZN_DB.HRZN_SCH.CUSTOMER','table'));\r\n````\r\n#### Custom Classification\r\n\r\n Snowflake provides the CUSTOM_CLASSIFIER class in the SNOWFLAKE.DATA_PRIVACY schema\r\n to enable Data Engineers / Governors to extend their Data Classification capabilities based on their own knowledge of their data.\r\n\r\n````\r\nUSE SCHEMA HRZN_DB.CLASSIFIERS;\r\n\r\ncreate or replace snowflake.data_privacy.custom_classifier CREDITCARD();\r\n\r\nShow snowflake.data_privacy.custom_classifier;\r\n\r\nCall creditcard!add_regex('MC_PAYMENT_CARD','IDENTIFIER','^(?:5[1-5][0-9]{2}|222[1-9]|22[3-9][0-9]|2[3-6][0-9]{2}|27[01][0-9]|2720)[0-9]{12}$');\r\nCall creditcard!add_regex('AMX_PAYMENT_CARD','IDENTIFIER','^3[4-7][0-9]{13}$');\r\n\r\nSelect creditcard!list();\r\n\r\nselect CREDITCARD from HRZN_DB.HRZN_SCH.CUSTOMER where CREDITCARD regexp '^3[4-7][0-9]{13}$';\r\n\r\nCALL SYSTEM$CLASSIFY('HRZN_DB.HRZN_SCH.CUSTOMER',{'auto_tag': true, 'custom_classifiers': ['HRZN_DB.CLASSIFIERS.CREDITCARD']});\r\n````\r\n\u003Easide negative\r\n\u003ENote: This statement shows if a column is classified as a particular tag\r\n````\r\nSelect SYSTEM$GET_TAG('snowflake.core.semantic_category','HRZN_DB.HRZN_SCH.CUSTOMER.CREDITCARD','column');\r\n````\r\n\u003Easide positive\r\n\u003E\r\n Moving 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#### Tagging\r\n\r\nA tag-based masking policy 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````\r\nUSE SCHEMA TAG_SCHEMA;\r\n````\r\nCreate cost_center tag and add comment\r\n````\r\ncreate tag HRZN_DB.TAG_SCHEMA.cost_center allowed_values 'Sales','Marketing','Support';\r\nalter tag HRZN_DB.TAG_SCHEMA.cost_center set comment = 'Respective Cost center for chargeback';\r\n````\r\nCreate on sensitive datasets and add comments\r\n````\r\ncreate tag HRZN_DB.TAG_SCHEMA.confidential allowed_values 'Sensitive','Restricted','Highly Confidential';\r\nalter tag HRZN_DB.TAG_SCHEMA.confidential set comment = 'Confidential information';\r\n                                      \r\ncreate tag HRZN_DB.TAG_SCHEMA.pii_type allowed_values 'Email','Phone Number','Last Name';\r\nalter tag HRZN_DB.TAG_SCHEMA.pii_type set comment = 'PII Columns';\r\n````\r\n\r\nApply tag on warehouse dev_demo_wh\r\n````\r\nalter warehouse HRZN_WH set tag cost_center = 'Sales';\r\n````\r\nApply tags at the table and column level\r\n````\r\n--Table Level\r\nalter table HRZN_DB.HRZN_SCH.customer set tag HRZN_DB.TAG_SCHEMA.confidential ='Sensitive';  \r\nalter table HRZN_DB.HRZN_SCH.customer set tag HRZN_DB.TAG_SCHEMA.cost_center ='Sales';  \r\n--Column Level\r\nalter table HRZN_DB.HRZN_SCH.customer modify email set tag HRZN_DB.TAG_SCHEMA.pii_type ='Email';\r\nalter table HRZN_DB.HRZN_SCH.customer modify phone_number set tag HRZN_DB.TAG_SCHEMA.pii_type ='Phone Number';\r\nalter table HRZN_DB.HRZN_SCH.customer modify last_name set tag HRZN_DB.TAG_SCHEMA.pii_type ='Last Name';\r\n````\r\n\r\nQuery account usage view to check tags and reference\r\n\u003Easide negative\r\n\u003ENote: The following VIEWs have a latency of about 20 min after creating TAG objects before they will be able to display data.\r\n````\r\nselect * from snowflake.account_usage.tag_references where tag_name ='CONFIDENTIAL' ;\r\nselect * from snowflake.account_usage.tag_references where tag_name ='PII_TYPE' ;\r\nselect * from snowflake.account_usage.tag_references where tag_name ='COST_CENTER' ;\r\n````\r\n\r\nNow we can use the TAG_REFERENCE_ALL_COLUMNS function to return the Tags associated with our customer order table.\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    ('HRZN_DB.HRZN_SCH.customer','table'));\r\n````\r\n#### Dynamic Data Masking\r\nIn Snowflake it is possible to use Column-Level Security to mask dynamically and create a conditional policy. Lets see how we can combine these to create a conditional masking policy.\r\n````\r\n--Create masking policy for PII\r\nCREATE OR REPLACE MASKING POLICY HRZN_DB.TAG_SCHEMA.MASK_PII AS\r\n  (VAL CHAR) RETURNS CHAR -\u003E\r\n  CASE\r\n    WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN', 'HRZN_DATA_GOVERNOR') THEN VAL\r\n      ELSE '***PII MASKED***'\r\n    END;\r\n\r\n\r\n CREATE OR REPLACE MASKING POLICY HRZN_DB.TAG_SCHEMA.MASK_SENSITIVE AS\r\n  (VAL CHAR) RETURNS CHAR -\u003E\r\n  CASE\r\n    WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN', 'HRZN_DATA_GOVERNOR') THEN VAL\r\n      ELSE '***SENSITIVE***'\r\n    END;\r\n\r\n--Apply policies to specific columns\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN SSN SET MASKING POLICY HRZN_DB.TAG_SCHEMA.MASK_PII;\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN CREDITCARD SET MASKING POLICY HRZN_DB.TAG_SCHEMA.MASK_SENSITIVE;\r\n\r\n\r\nSELECT SSN,CREDITCARD FROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\n\r\nNow we can switch back to our Data User role from the beginning of the script. LEts see if the Data User still has access to sensitive data. \r\n````\r\nUSE ROLE HRZN_DATA_USER;\r\nUSE WAREHOUSE HRZN_WH;\r\nSELECT SSN,CREDITCARD FROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\nThe data is masked for the Data User. \r\n````\r\nUSE ROLE HRZN_DATA_GOVERNOR;\r\nUSE SCHEMA HRZN_DB.TAG_SCHEMA;\r\n````\r\nThe Data Governor can create opt-in masking based on condition\r\n````\r\ncreate or replace masking policy HRZN_DB.TAG_SCHEMA.conditionalPolicyDemo \r\n   as (phone_nbr string, optin string) returns string -\u003E\r\n   case\r\n      when optin = 'Y' then phone_nbr\r\n      else '***OPT OUT***'\r\n   end;\r\n\r\nalter table HRZN_DB.HRZN_SCH.CUSTOMER modify column PHONE_NUMBER set\r\n   masking policy HRZN_DB.TAG_SCHEMA.conditionalPolicyDemo  using (PHONE_NUMBER, OPTIN);\r\n\r\nSELECT PHONE_NUMBER,OPTIN FROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\n\r\n\r\nSnowflake makes it possible to streamline the masking process by grouping all these sensitive or PII columns under a common tag and apply masking for that tag.\r\n````\r\n--Create a Tag\r\nCREATE OR REPLACE TAG HRZN_DB.TAG_SCHEMA.PII_COL ALLOWED_VALUES 'PII-DATA','NON-PII';\r\n\r\n--Apply to the table\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN LAST_NAME SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN BIRTHDATE SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN STREET_ADDRESS SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN CITY SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN STATE SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN ZIP SET TAG  HRZN_DB.TAG_SCHEMA.PII_COL = 'PII-DATA';\r\n\r\n\r\n--Create Masking Policy\r\nCREATE OR REPLACE MASKING POLICY HRZN_DB.TAG_SCHEMA.PII_DATA_MASK AS (VAL string) RETURNS string -\u003E\r\nCASE\r\nWHEN SYSTEM$GET_TAG_ON_CURRENT_COLUMN('HRZN_DB.TAG_SCHEMA.PII_COL') = 'PII-DATA' \r\n    AND CURRENT_ROLE() NOT IN ('HRZN_DATA_GOVERNOR','ACCOUNTADMIN') \r\n    THEN '**PII TAG MASKED**'\r\nELSE VAL\r\nEND;\r\n\r\n\r\n--Apply Masking policy to the tag\r\nALTER TAG HRZN_DB.TAG_SCHEMA.PII_COL SET MASKING POLICY HRZN_DB.TAG_SCHEMA.PII_DATA_MASK;\r\n````\r\nLets switch back to the Data User role and Check if the sensitive data is visible or masked\r\n````\r\nUSE ROLE HRZN_DATA_USER;\r\nSELECT FIRST_NAME, LAST_NAME, STREET_ADDRESS, CITY, STATE, ZIP \r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\nWhen we switch back to the Data Governor role we can see that the data is still present, just masked when required.\r\n\r\n````\r\nUSE ROLE HRZN_DATA_GOVERNOR;\r\nSELECT FIRST_NAME, LAST_NAME, STREET_ADDRESS, CITY, STATE, ZIP \r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\n\r\n#### Row-Access Policies\r\n\r\n Now that our Data Governor is happy with our Tag Based Dynamic Masking controlling masking at the column level, we will now look to restrict access at the row level for our Data Analyst role.\r\n\r\n Within our Customer  table, our role should only see Customers who are\r\n based in Massachussets(MA).\r\n\r\nFirst, We need to unset any exising masking policies on the column\r\n````USE ROLE HRZN_DATA_GOVERNOR;\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN STATE UNSET TAG  HRZN_DB.TAG_SCHEMA.PII_COL;\r\n````\r\nLets see what the data user can see.\r\n````\r\nUSE ROLE HRZN_DATA_USER;\r\nSELECT FIRST_NAME, STREET_ADDRESS, STATE, OPTIN, PHONE_NUMBER, EMAIL, JOB, COMPANY FROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\nWe will need to use row level security to show only the Data for Massachusetts.\r\n````\r\nUSE ROLE HRZN_DATA_GOVERNOR;\r\n--The mapping for the user is in the table ROW_POLICY_MAP\r\nSELECT * FROM HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP; \r\n````\r\n\r\n\u003Easide negative\r\n\u003ENote: 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````\r\nCREATE OR REPLACE ROW ACCESS POLICY HRZN_DB.TAG_SCHEMA.CUSTOMER_STATE_RESTRICTIONS\r\n    AS (STATE STRING) RETURNS BOOLEAN -\u003E\r\n       CURRENT_ROLE() IN ('ACCOUNTADMIN','HRZN_DATA_ENGINEER','HRZN_DATA_GOVERNOR') -- 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 HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP rp\r\n            WHERE 1=1\r\n                AND rp.ROLE = CURRENT_ROLE()\r\n                AND rp.STATE_VISIBILITY = STATE\r\n            )\r\nCOMMENT = 'Policy to limit rows returned based on mapping table of ROLE and STATE: governance.row_policy_map';\r\n\r\n\r\n\r\n -- let's now apply the Row Access Policy to our City column in the Customer Order table\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER\r\n    ADD ROW ACCESS POLICY HRZN_DB.TAG_SCHEMA.CUSTOMER_STATE_RESTRICTIONS ON (STATE);\r\n````\r\n\r\nWith the policy successfully applied, let's test it using the Data User Role\r\n````\r\nUSE ROLE HRZN_DATA_USER;\r\nSELECT FIRST_NAME, STREET_ADDRESS, STATE, OPTIN, PHONE_NUMBER, EMAIL, JOB, COMPANY FROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\n\r\n\r\n#### Aggregation Policies\r\n\r\n Outside of the Data Access Policies (Masking and Row Access) we have covered,\r\n Snowflake Horizon also provides Privacy Policies. In this section we will cover\r\n the ability to set Aggregation Policies on Database Objects which can restrict\r\n certain roles to only aggregate data by only allowing for queries that aggregate\r\n data into groups of a minimum size versus retrieving individual roles.\r\n\r\n For the Data User role we have created, let's test an Aggregation Policy out against our Raw Order Header table.\r\n\r\n\r\n\u003Easide positive\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\r\n\r\nFor 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\r\n\r\n````\r\nUSE ROLE HRZN_DATA_GOVERNOR;\r\n\r\nCREATE OR REPLACE AGGREGATION POLICY HRZN_DB.TAG_SCHEMA.aggregation_policy\r\n  AS () RETURNS AGGREGATION_CONSTRAINT -\u003E\r\n    CASE\r\n      WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','HRZN_DATA_ENGINEER','HRZN_DATA_GOVERNOR')\r\n      THEN NO_AGGREGATION_CONSTRAINT()  \r\n      ELSE AGGREGATION_CONSTRAINT(MIN_GROUP_SIZE =\u003E 100) -- atleast 100 rows in aggregate\r\n    END;\r\n````\r\nWith the Aggregation Policy created, let's apply it to our Order Header table\r\n````\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS\r\n    SET AGGREGATION POLICY HRZN_DB.TAG_SCHEMA.aggregation_policy;\r\n````    \r\n\r\n- Lets try running a simple SELECT *?\r\n````\r\nSELECT TOP 10 * FROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\r\n````\r\n- What happens if we include over 100 rows?\r\n````\r\nSELECT TOP 101 * FROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\r\n````\r\nNow, lets switch to the Data User role and try those same queries.\r\n````\r\nUSE ROLE HRZN_DATA_USER;\r\n````\r\n- Lets try running a simple SELECT *?\r\n````\r\nSELECT TOP 10 * FROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\r\n````\r\n- What happens if we include over 100 rows?\r\n````\r\nSELECT TOP 101 * FROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS;\r\n````\r\n\r\n\r\nLets answer a few aggregate business questions on on the Customer Order table that we have previously:\r\n1. Deployed Masking against PII columns\r\n2. Deployed Row Level Security to restrict our Test Role to only Massachusetts results\r\n\r\n- What are the total order amounts?\r\n````\r\nSELECT \r\n    cl.state,\r\n    cl.city,\r\n    COUNT(oh.order_id) AS count_order,\r\n    SUM(oh.order_amount) AS order_total\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\r\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\r\n    ON oh.customer_id = cl.id\r\nGROUP BY ALL\r\nORDER BY order_total DESC;\r\n\r\nSELECT \r\n    cl.state,\r\n    cl.city,\r\n    COUNT(oh.order_id) AS count_order,\r\n    SUM(oh.order_amount) AS order_total\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\r\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\r\n    ON oh.customer_id = cl.id\r\nWHERE oh.order_amount \u003E 64\r\nGROUP BY ALL\r\nORDER BY order_total DESC;\r\n\r\nSELECT \r\n    cl.state,\r\n    cl.city,\r\n    COUNT(oh.order_id) AS count_order,\r\n    SUM(oh.order_amount) AS order_total\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\r\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\r\n    ON oh.customer_id = cl.id\r\nWHERE oh.order_amount \u003E 3\r\nGROUP BY ALL\r\nORDER BY order_total DESC;\r\n````\r\n\r\n\r\n-What are the total order amounts by company and job?\r\n \u003Easide negative\r\n \u003ENote: 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 ````\r\nSELECT \r\n    cl.company,\r\n    cl.job,\r\n    COUNT(oh.order_id) AS count_order,\r\n    SUM(oh.order_amount) AS order_total\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\r\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\r\n    ON oh.customer_id = cl.id\r\nGROUP BY ALL\r\nORDER BY order_total DESC;\r\n````\r\n\r\nNow lets try switching to our Data Governor Role, and now run that same query to see what the results look like in a privileged Role not restricted by Row Access and Aggregation policies.\r\n````\r\nUSE ROLE HRZN_DATA_GOVERNOR;\r\nUSE SCHEMA HRZN_DB.TAG_SCHEMA;\r\n\r\nSELECT \r\n    cl.company,\r\n    cl.job,\r\n    COUNT(oh.order_id) AS count_order,\r\n    SUM(oh.order_amount) AS order_total\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS oh\r\nJOIN HRZN_DB.HRZN_SCH.CUSTOMER cl\r\n    ON oh.customer_id = cl.id\r\nGROUP BY ALL\r\nORDER BY order_total DESC;\r\n````\r\n#### Projection Policies\r\nWithin this step, we will cover another Privacy Policy framework provided by Snowflake Horizon this time diving into Projection Policies which in short will prevent queries from using a SELECT statement to project values from a column.\r\n\r\n\u003Easide 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\r\n\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\r\n````\r\nCREATE OR REPLACE PROJECTION POLICY HRZN_DB.TAG_SCHEMA.projection_policy\r\n  AS () RETURNS PROJECTION_CONSTRAINT -\u003E \r\n  CASE\r\n    WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN','HRZN_DATA_ENGINEER', 'HRZN_DATA_GOVERNOR')\r\n    THEN PROJECTION_CONSTRAINT(ALLOW =\u003E true)\r\n    ELSE PROJECTION_CONSTRAINT(ALLOW =\u003E false)\r\n  END;\r\n````\r\nWe need to unset any exising masking poilcies on the column\r\n````\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER MODIFY COLUMN ZIP UNSET TAG  HRZN_DB.TAG_SCHEMA.PII_COL;\r\n````\r\nWith the Projection Policy in place, let's assign it to our Postal Code column\r\n````\r\nALTER TABLE HRZN_DB.HRZN_SCH.CUSTOMER\r\n MODIFY COLUMN ZIP\r\n SET PROJECTION POLICY HRZN_DB.TAG_SCHEMA.projection_policy;\r\n````\r\nLets see how our projection policy works for the Data User. What does a \r\nSELECT * against the table yield?\r\n````\r\n USE ROLE HRZN_DATA_USER;\r\nSELECT TOP 100 * FROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\n\r\nWhat if we EXCLUDE the postal_code column?\r\n````\r\nSELECT TOP 100 * EXCLUDE ZIP FROM HRZN_DB.HRZN_SCH.CUSTOMER;\r\n````\r\n\r\n Although our Projection Policy blocks our Data User Role from including the Postal Code column\r\n in the SELECT clause it can still be used in the WHERE clause to assist with analysis.\r\n\r\nKnowing this, let's now help our marketing team by addressing a few of their questions\r\n\r\nWhich CUSTOMERS from postal_code other than 97135 AND 95357 should recieve a program anniversary promotion this month? \r\n````\r\nSELECT \r\n* EXCLUDE ZIP\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER\r\nWHERE ZIP NOT IN ('97135', '95357');\r\n````\r\n\r\nWhich members from postal_code 97135 AND 95357 have have opted in for text messages?\r\n````\r\nSELECT \r\n    ID,FIRST_NAME,PHONE_NUMBER,EMAIL, COMPANY\r\nFROM HRZN_DB.HRZN_SCH.CUSTOMER\r\nWHERE ZIP IN ('97135', '95357')\r\n    AND OPTIN = 'Y';\r\n````\r\nNow that we've protected our data and our users can access it appropriately to address their questions, let's move on to explore the Governor Admin role.\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Governor Admin - Access & Audit\r\n\r\n**Overview**\r\n\r\nAccess History provides insights into user queries encompassing what data was \r\nread and when, as well as what statements have performed a write operations. 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\nIn Snowsight create a new worksheet and rename it 3_Governor_Admin. Copy and paste each code block below and execute. You can also find the entire Data Governor Admin Script at [ 3-Data-governor-Admin.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/hol-lab/3-Data-governor-Admin.sql) \r\n\r\n\u003Easide positive\r\n\u003ENote: Access History latency is up to 3 hours.  So, some of the queries below may not have results right away. \r\n````\r\nUSE ROLE HRZN_IT_ADMIN;\r\n USE DATABASE HRZN_DB;\r\n USE SCHEMA HRZN_SCH;\r\n USE WAREHOUSE HRZN_WH;\r\n````\r\n\r\nLet's check out how our data is being accessed\r\n- How many queries have accessed each of our Raw layer tables directly?\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 'HRZN%'\r\nGROUP BY object_name\r\nORDER BY number_of_queries DESC;\r\n````\r\n\r\n- What is the breakdown between Read and Write queries and when did they last occur?\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 'HRZN%'\r\nGROUP BY object_name, query_type\r\nORDER BY object_name, number_of_queries DESC;\r\n\r\n-- last few \"read\" queries\r\nSELECT\r\n    qh.user_name,    \r\n    qh.query_text,\r\n    value:objectName::string as \"TABLE\"\r\nFROM snowflake.account_usage.query_history AS qh\r\nJOIN snowflake.account_usage.access_history AS ah\r\nON qh.query_id = ah.query_id,\r\n    LATERAL FLATTEN(input =\u003E ah.base_objects_accessed)\r\nWHERE query_type = 'SELECT' AND\r\n    value:objectName = 'HRZN_DB.HRZN_SCH.CUSTOMER' AND\r\n    start_time \u003E dateadd(day, -90, current_date());\r\n\r\n-- last few \"write\" queries\r\nSELECT\r\n    qh.user_name,    \r\n    qh.query_text,\r\n    value:objectName::string as \"TABLE\"\r\nFROM snowflake.account_usage.query_history AS qh\r\nJOIN snowflake.account_usage.access_history AS ah\r\nON qh.query_id = ah.query_id,\r\n    LATERAL FLATTEN(input =\u003E ah.base_objects_accessed)\r\nWHERE query_type != 'SELECT' AND\r\n    value:objectName = 'HRZN_DB.HRZN_SCH.CUSTOMER' AND\r\n    start_time \u003E dateadd(day, -90, current_date());\r\n````\r\n\r\n- Find longest running queries\r\n````\r\nSELECT\r\nquery_text,\r\nuser_name,\r\nrole_name,\r\ndatabase_name,\r\nwarehouse_name,\r\nwarehouse_size,\r\nexecution_status,\r\nround(total_elapsed_time/1000,3) elapsed_sec\r\nFROM snowflake.account_usage.query_history\r\nORDER BY total_elapsed_time desc\r\nLIMIT 10;\r\n````\r\n- Find queries that have been executed against sensitive tables\r\n````\r\nSELECT\r\n  q.USER_NAME,\r\n  q.QUERY_TEXT,\r\n  q.START_TIME,\r\n  q.END_TIME\r\nFROM\r\n  SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY q \r\nWHERE\r\n  q.QUERY_TEXT ILIKE '%HRZN_DB.HRZN_SCH.CUSTOMER%'\r\nORDER BY\r\n  q.START_TIME DESC;\r\n````\r\n\r\n- Show the flow of sensitive data\r\n````\r\nSELECT\r\n    *\r\nFROM\r\n(\r\n    select\r\n      directSources.value: \"objectId\"::varchar as source_object_id,\r\n      directSources.value: \"objectName\"::varchar as source_object_name,\r\n      directSources.value: \"columnName\"::varchar as source_column_name,\r\n      'DIRECT' as source_column_type,\r\n      om.value: \"objectName\"::varchar as target_object_name,\r\n      columns_modified.value: \"columnName\"::varchar as target_column_name\r\n    from\r\n      (\r\n        select\r\n          *\r\n        from\r\n          snowflake.account_usage.access_history\r\n      ) t,\r\n      lateral flatten(input =\u003E t.OBJECTS_MODIFIED) om,\r\n      lateral flatten(input =\u003E om.value: \"columns\", outer =\u003E true) columns_modified,\r\n      lateral flatten(\r\n        input =\u003E columns_modified.value: \"directSources\",\r\n        outer =\u003E true\r\n      ) directSources\r\n    union\r\n// 2\r\n    select\r\n      baseSources.value: \"objectId\" as source_object_id,\r\n      baseSources.value: \"objectName\"::varchar as source_object_name,\r\n      baseSources.value: \"columnName\"::varchar as source_column_name,\r\n      'BASE' as source_column_type,\r\n      om.value: \"objectName\"::varchar as target_object_name,\r\n      columns_modified.value: \"columnName\"::varchar as target_column_name\r\n    from\r\n      (\r\n        select\r\n          *\r\n        from\r\n          snowflake.account_usage.access_history\r\n      ) t,\r\n      lateral flatten(input =\u003E t.OBJECTS_MODIFIED) om,\r\n      lateral flatten(input =\u003E om.value: \"columns\", outer =\u003E true) columns_modified,\r\n      lateral flatten(\r\n        input =\u003E columns_modified.value: \"baseSources\",\r\n        outer =\u003E true\r\n      ) baseSources\r\n) col_lin\r\n   WHERE\r\n       (SOURCE_OBJECT_NAME = 'HRZN_DB.HRZN_SCH.CUSTOMER' OR TARGET_OBJECT_NAME='HRZN_DB.HRZN_SCH.CUSTOMER')\r\n    AND\r\n        (SOURCE_COLUMN_NAME IN (\r\n                SELECT\r\n                    COLUMN_NAME\r\n                FROM\r\n                (\r\n                    SELECT\r\n                        *\r\n                    FROM TABLE(\r\n                      HRZN_DB.INFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS(\r\n                        'HRZN_DB.HRZN_SCH.CUSTOMER',\r\n                        'table'\r\n                      )\r\n                    )\r\n                )\r\n                WHERE TAG_NAME IN ('CONFIDENTIAL','PII_COL','PII_TYPE') \r\n            )\r\n            OR\r\n            TARGET_COLUMN_NAME IN (\r\n                SELECT\r\n                    COLUMN_NAME\r\n                FROM\r\n                (\r\n                    SELECT\r\n                        *\r\n                    FROM TABLE(\r\n                      HRZN_DB.INFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS(\r\n                        'HRZN_DB.HRZN_SCH.CUSTOMER',\r\n                        'table'\r\n                      )\r\n                    )\r\n                )\r\n                WHERE TAG_NAME IN ('CONFIDENTIAL','PII_COL','PII_TYPE') --Enter the relevant tag(s) to check against.\r\n            )\r\n            );\r\n````\r\n    \r\n- How many queries have accessed each of our tables indirectly?\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 'HRZN%'\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\u003Easide positive\r\n\u003EDirect Objects Accessed: Data objects directly named in the query explicitly.\r\nBase Objects Accessed: Base data objects required to execute a query.\r\n \r\n \u003Easide negative\r\n \u003E Clean up (Optional).\r\n \u003ECreate a new worksheet named 99_lab_teardown. Copy and paste the entire Teardown Script at [ 99-lab-teardown.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-horizon-data-governance-in-snowflake/blob/main/99-lab-teardown.sql) \r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion And Resources\r\n\r\nYou did it! In this comprehensive lab, you have seen how Horizon:\r\n- Secures data with role-based access control, governance policies, and more \r\n\r\n- Monitors data quality with both out-of-the-box and custom metrics\r\n\r\n- Audits data usage through Access History and Schema Change Tracking\r\n\r\n- Understands the flow of data through object dependencies and lineage\r\n\r\n### What You Learned\r\n- How to create stages, databases, tables, views, and virtual warehouses.\r\n- As a Data Engineer, how to implement Data Quality Monitoring and data metric functions\r\n- As a Data Governor, how to apply column-level and row-level security and how to use projection and aggregation constraints \r\n- As a Governor Admin, how to use data lineage and dependencies to audit access and understand the flow of data\r\n\r\n\r\n### Resources\r\n- Check out more Horizon [resources] (/en/data-cloud/horizon/) and [documentation](https://docs.snowflake.com/en/guides-overview-govern)\r\n- Read the [Definitive Guide to Governance in Snowflake](/resource/the-definitive-guide-to-governance-in-snowflake)\r\n- Join the [Snowflake Community](https://community.snowflake.com/s/)\r\n- Sign up for [Snowflake University](http://https://community.snowflake.com/s/snowflake-university)\r\n- Fork the [Repo on GitHub](https://github.com/Snowflake-Labs/sfquickstarts/blob/cortex-fine-tuning/site/sfguides/src/getting_started_with_llm_finetuning_using_cortex_ai/getting_started_with_llm_finetuning_using_cortex_ai.md)\r\n- Download [Reference Architecture](/content/dam/snowflake-site/developers/2024/06/LLM-Serverless-Fine-tuning-With-Snowflake-Cortex-AI-.pdf)\r\n- [Read the Blog](https://medium.com/snowflake/re-imagine-data-governance-with-snowflake-horizon-and-powered-by-ai-9ac1ead51b6f)\r\n- [Watch the Demo](https://youtu.be/CML-mhhCvOA?list=TLGGiSkeWqigbJ4yNDA5MjAyNQ)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-12e32a7988","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-7fc7af3786",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-58f3019a5a","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-09-17",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-2e0d365c8f","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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