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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-dac643d617","quickstartHeroTitle":{"lines":["Defensible Analytics using Data Vault and Snowflake"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Paul Hooper","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Business 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Analytics using Data Vault and Snowflake","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EDefensible Analytics\u003C/h2\u003E\n","\u003Cp\u003EToday, AI tools interact with data on a daily basis, and enterprises are increasingly recognizing the need for a mature \u003Cstrong\u003Esystem of information management\u003C/strong\u003E that provides \u003Cstrong\u003Edefensible analytics\u003C/strong\u003E -- analytics based upon auditable, trustworthy enterprise memory, with unambiguous business context.\u003C/p\u003E\n","\u003Cp\u003EInstead of an adhoc approach, making it up as we go along, enterprises require proven patterns for a \u003Ca href=\"https://datavaultalliance.com/strategy-operating/system-information-management/\"\u003Esystem of information management\u003C/a\u003E. We need to rely upon a system that not only transforms data, but provides continuous trustworthy \u003Cstrong\u003Einformation\u003C/strong\u003E, aligned to business needs, accompanied by business context, using business vocabulary, with auditable lineage to the originating source. The system must generate the evidence needed to make confident decisions, defend those decisions, and enable consistent answers to questions given to AI agents. The system must be responsive to change, which comes at the speed of business, compatible with an agile approach to implementation, maximizing reuse and avoiding duplication of effort, yet never destroying the auditability and reliability of what has already been delivered.\u003C/p\u003E\n","\u003Cp\u003EData are assets, relevant to our decision-making processes, and can reduce the effort of decision-making, while also increasing the quality, speed, and execution of our decisions. Improving decision effectiveness improves the performance of our enterprise for all stakeholders. Knowing that, how do we manage information appropriately?\u003C/p\u003E\n","\u003Ch3\u003EWhat is Data Vault?\u003C/h3\u003E\n","\u003Cp\u003EIn 2018, at the World-Wide Data Vault Consortium (WWDVC), \u003Ca href=\"https://en.wikipedia.org/wiki/Bill_Inmon\"\u003EBill Inmon\u003C/a\u003E updated his classic definition of the Data Warehouse.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/inmondw2018.png?v=7a7bb18b\" alt=\"A data warehouse is a subject-oriented, integrated (by business key), time-variant and non-volatile collection of data in support of management’s decision-making process, and/or in support of auditability as a system-of-record - William Inmon 2018\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis was only a minor variation, and this definition remains highly relevant today. He followed this update with his recommendation of the Data Vault system to build it. His definition does not contain the words schema-on-write or structured. His phrase "collection of data" includes \u003Ca href=\"https://docs.snowflake.com/en/user-guide/unstructured-intro\"\u003Eunstructured\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/semistructured-intro\"\u003Esemi-structured\u003C/a\u003E data types. Whether we call that a data warehouse, data lake, or data lakehouse, nothing beats the Snowflake AI Data Cloud when it comes to handling those diverse types of data.\u003C/p\u003E\n","\u003Cp\u003EData Vault, as invented by \u003Ca href=\"https://datavaultalliance.com/#about\"\u003EDan Linstedt\u003C/a\u003E, is not just a collection of data modeling standards, but a complete system of information management. That system has key pillars of methodology, architecture, and model, always supporting informed decision-making and delivering business outcomes. This guide cannot possibly detail the entire Data Vault system. To gain a full understanding of Data Vault, we recommend working with experts & partners from \u003Ca href=\"https://datavaultalliance.com/\"\u003EData Vault Alliance\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn Data Vault 2.1, Linstedt has clearly articulated a logical architecture consisting of gated \u003Ca href=\"https://www.youtube.com/watch?v=OkI1LWsz9Nc\"\u003Ezones\u003C/a\u003E. At the core are the Landing Zone, the Enterprise Memory Zone, and the Information Delivery Zone. However, before a functional implementation, the logical architecture must progress to physical. This guide is an introduction to how that may be accomplished in the Snowflake AI Data Cloud.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-key-concepts\"\u003ESnowflake key concepts and architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EFamiliarity with \u003Ca href=\"https://datavaultalliance.com/#resources\"\u003EData Vault methodology and architecture\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to structure a Snowflake account for Data Vault\u003C/li\u003E\u003Cli\u003EHow to enable domain-oriented development and governance in your Data Vault\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account -- we recommend starting with a fresh Enterprise Edition \u003Ca href=\"https://trial.snowflake.com/\"\u003Etrial\u003C/a\u003E account for this guide\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Data Vault environment using Snowflake\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EReference Architecture\u003C/h2\u003E\n","\u003Cp\u003ELet’s start with the target architecture.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/multitierdatavaultarchitecture.png?v=7a7bb18b\" alt=\"Multi-Tier Data Vault Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOn the very left of figure above we have a list of \u003Cstrong\u003Edata sources\u003C/strong\u003E that typically include a mix of operational databases, files, streaming event sources, SaaS apps, and more. The \u003Ca href=\"https://www.snowflake.com/en/product/features/marketplace/\"\u003ESnowflake Marketplace\u003C/a\u003E allows us to tap into 3rd party data to augment our own.\u003C/p\u003E\n","\u003Cp\u003EOn the very right we have our ultimate \u003Cstrong\u003Edata consumers\u003C/strong\u003E: business users, AI agents, data scientists, IT systems or even other companies you decide to share your data with.\u003C/p\u003E\n","\u003Cp\u003EArchitecturally, we will consider the following Data Vault 2.1 zones:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETransient Zone\u003C/strong\u003E: used to transport ephemeral data from source systems and make it accessible for ingestion into Snowflake. We won't dive deep into this zone in this guide, but be sure to check out \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-integration/openflow/about\"\u003ESnowflake Openflow\u003C/a\u003E and the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ecosystem\"\u003ESnowflake Ecosystem\u003C/a\u003E as key enablers for this zone.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELanding Zone\u003C/strong\u003E: a managed persistent staging area (PSA) where data is ingested and kept as close as possible to its original state, as established by the source systems it came from. For this Snowflake has \u003Ca href=\"https://docs.snowflake.com/en/guides-overview-loading-data\"\u003Emultiple options\u003C/a\u003E, including \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-local-file-system\"\u003Ebulk loading of files\u003C/a\u003E, continuous loading of micro-batches of files through \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro\"\u003ESnowpipe\u003C/a\u003E, or continuous rows of data through \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview\"\u003ESnowpipe Streaming\u003C/a\u003E. Snowflake allows you to load and store structured, unstructured, and semi-structured data in the original format whilst automatically optimizing the physical structure for efficient query access. But this zone isn't just a data dump. Per Data Vault 2.1, in this zone the data is immutable, stored as it was received from source, with no changes to the content. Here, data are \u003Ca href=\"https://docs.snowflake.com/en/guides-overview-govern\"\u003Egoverned as assets\u003C/a\u003E. Metadata may be documented, data may be tagged, profiled, and encrypted. Snowflake's \u003Ca href=\"https://docs.snowflake.com/en/user-guide/storage-management/storage-lifecycle-policies\"\u003Estorage lifecycle policies\u003C/a\u003E may be used to automatically move older data to more cost-effective cool and cold archival tiers, keeping expenses down.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEnterprise Memory Zone\u003C/strong\u003E: where data become subject-oriented, integrated by business key, time-variant and non-volatile. This is where the data vault modeling patterns -- such as hubs, links, and satellites -- begin to be applied. Data enters the raw vault, sparsely built, where only hard business rules are applied, loading all records received from source.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInformation Delivery Zone\u003C/strong\u003E: a collection of consumer-oriented models, designed to inform decision-making processes. This can be implemented as a set (or multiple sets) of views. It is common to see the use of dimensional models (facts and dimensions, star or snowflake) or denormalized flat tables (for data science or sharing) but it could be any other modeling style that fits best for your data consumer. The Business Vault contains data vault objects with soft business rules applied, augmenting the intelligence of the system, and potentially enhancing the performance of the consumer-facing views. Soft business rules may include the calculation of metrics, commonly used aggregations, master data records, PIT and Bridge tables helping to simplify access to information with highly performant views of facts and dimensions. Snowflake’s scalability will support the required speed of access at any point of this data lifecycle. You should consider materialization of Business Vault and other Information Delivery objects as optional.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWith a brand new Snowflake account, objects are not automatically created to represent these logical concepts. We must create them, keeping in mind Snowflake's object hierarchy.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/objecthierarchy.png?v=7a7bb18b\" alt=\"Snowflake Object Hierarchy: Organization - Account - Database  - Schema - Object\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFrom Logical Architecture Zones to Implemented Snowflake Objects\u003C/h2\u003E\n","\u003Cp\u003EIf you're familiar with Snowflake's \u003Ca href=\"https://www.snowflake.com/en/developers/guides/dcdf-incremental-processing/\"\u003EData Cloud Deployment Framework (DCDF)\u003C/a\u003E, you might recognize parallels between the Data Vault zones and the DCDF databases: Raw, Integration, and Presentation. This is because databases are a great place to start for the physical implementation of the logical zones. Snowflake Databases are not simply containers, but a key aspect of the physical architecture with significant governance implications.\u003C/p\u003E\n","\u003Cp\u003EIn addition to the databases related to our Data Vault zones, a common platform database can serve to hold objects not specific to any single zone. We'll create this, as well.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EFor the sake of simplicity, this guide will not delve into utilizing multiple Snowflake accounts. However, adopting a multi-account strategy, taking advantage of Snowflake's remarkable \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-sharing-intro\"\u003ESecure Data Sharing\u003C/a\u003E, could unlock significant value for your organization. A multi-account strategy is one where a single \u003Ca href=\"https://docs.snowflake.com/en/user-guide/organizations\"\u003EOrganization\u003C/a\u003E has multiple Accounts, each serving a specific purpose. This provides you with the flexibility to distribute databases across multiple accounts, where \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-sharing-intro\"\u003Eshared read-only access to data\u003C/a\u003E with zero copying, and thus enabling the use of different \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-editions\"\u003EEditions\u003C/a\u003E for different Accounts, which enable different feature sets and have different compute pricing. Databases may also be \u003Ca href=\"https://docs.snowflake.com/en/user-guide/account-replication-config\"\u003Ereplicated for business continuity and disaster recovery purposes\u003C/a\u003E to other accounts.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 0: Login to your Snowflake Account, Create a Workspace File\u003C/h3\u003E\n","\u003Cp\u003ELogin to your Snowflake trial account. You will need to use the same user and password that you used to login to your Snowflake account the first time.\u003C/p\u003E\n","\u003Cp\u003ETo get started on the implementation, click on Projects -> Workspaces to open the Workspaces interface.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/menuworkspaces.png?v=7a7bb18b\" alt=\"An Empty SQL File\"\u003E\u003C/p\u003E\n","\u003Cp\u003EMake a mental note of the content on the Home tab. Click the + Add new button, then SQL File, and name this new file DVArchitecture.sql.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/workspaceshome.png?v=7a7bb18b\" alt=\"An Empty SQL File\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is an intuitive SQL workbench. It has a section for the code we'll copy from the guide and paste into the SQL file. It also has a 'run' button to execute the code, the result panel at the bottom, and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code#cortex-code-in-sf-web-interface\"\u003ESnowflake CoCo\u003C/a\u003E to the right to provide natural language assistance.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/emptysqlfile.png?v=7a7bb18b\" alt=\"An Empty SQL File\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAt the end of this guide, you will explore what you've created through the eyes of different roles. However, with a new trial account, your user will default to using all secondary roles. So, we'll start by changing that to using none. This ensures secondary roles are not automatically activated at login, so you can activate them explicitly when needed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Defaulting Secondary Roles to None ------------------------------------------\nUSE ROLE ACCOUNTADMIN;\n\nALTER USER SET DEFAULT_SECONDARY_ROLES = ();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis setting is checked at sign in, so click your user icon at the bottom left corner, Sign Out, and then sign back in. Once signed back in, open your DVArchitecture.sql SQL File.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Platform Role, Warehouse, and Database\u003C/h3\u003E\n","\u003Cp\u003EAssuming you are using a new trial account, we'll start with some basics. We'll create a Platform Administrator role, as well as a virtual warehouse to use for basic administrative tasks, and a common platform database for common objects. This is meant to serve only as an example. Your role-based access control (RBAC) strategy and design may differ, but we'll use this example later in the guide. We'll grant the ability for any user to see the Platform database using the PUBLIC role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Platform Administration Role ------------------------------------------------\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS PLT_ADMIN\n  COMMENT = 'Platform administrator role for managing shared platform objects';\n\nGRANT ROLE PLT_ADMIN TO ROLE SYSADMIN;\n\n-- Administration Warehouse ----------------------------------------------------\nUSE ROLE SYSADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS ADMIN_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE\n  COMMENT = 'Administration warehouse for platform admin tasks';\n\nGRANT ALL PRIVILEGES ON WAREHOUSE ADMIN_WH TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\n-- Platform Database -----------------------------------------------------------\nCREATE DATABASE IF NOT EXISTS PLT\n  COMMENT = 'Common centralized platform database for shared objects and utilities';\n\nDROP SCHEMA IF EXISTS PLT.PUBLIC; -- Removing automatically generated schema\n\nGRANT ALL PRIVILEGES ON DATABASE PLT TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Platform Governance Schema\u003C/h3\u003E\n","\u003Cp\u003ELet's create a schema in the platform database for governance, containing common objects that we'll provide as the platform administrator, or as a central enablement team.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Platform Database: Governance -----------------------------------------------\nUSE ROLE PLT_ADMIN;\nUSE WAREHOUSE ADMIN_WH;\n\nCREATE SCHEMA IF NOT EXISTS PLT.GOVERNANCE\n  WITH MANAGED ACCESS\n  COMMENT = 'Platform governance objects, such as classification tags for annotating data assets';\n\nCREATE DATABASE ROLE IF NOT EXISTS PLT.GOVERNANCE_R\n  COMMENT = 'Read access to platform governance objects and tag definitions';\nCREATE DATABASE ROLE IF NOT EXISTS PLT.GOVERNANCE_A\n  COMMENT = 'Apply and invoke access to platform governance objects';\nCREATE DATABASE ROLE IF NOT EXISTS PLT.GOVERNANCE_W\n  COMMENT = 'Create and manage platform governance objects';\n\nGRANT DATABASE ROLE PLT.GOVERNANCE_R TO DATABASE ROLE PLT.GOVERNANCE_A;\nGRANT DATABASE ROLE PLT.GOVERNANCE_A TO DATABASE ROLE PLT.GOVERNANCE_W;\n\nGRANT DATABASE ROLE PLT.GOVERNANCE_R TO ROLE PUBLIC;\nGRANT DATABASE ROLE PLT.GOVERNANCE_W TO ROLE PLT_ADMIN;\n\n-- GOVERNANCE_R: read access to governance schema and tag values\nGRANT USAGE ON DATABASE PLT TO DATABASE ROLE PLT.GOVERNANCE_R;\nGRANT USAGE ON SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_R;\n\n-- GOVERNANCE_A: apply platform governance objects\nGRANT MONITOR ON SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_A;\nGRANT USAGE ON FUTURE DATA METRIC FUNCTIONS IN SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_A;\n-- Note that the APPLY privilege may not be granted to future tags, but granted after tags are created\n\n-- GOVERNANCE_W: create and manage platform governance objects\nGRANT CREATE DATA METRIC FUNCTION ON SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_W;\nGRANT CREATE TAG ON SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_W;\n\nCREATE TAG IF NOT EXISTS PLT.GOVERNANCE.INFO_CLASSIFICATION\n  ALLOWED_VALUES 'Public', 'Internal', 'Restricted'\n  COMMENT = 'Information classification level of the tagged data object or column';\nGRANT READ ON TAG PLT.GOVERNANCE.INFO_CLASSIFICATION TO DATABASE ROLE PLT.GOVERNANCE_R;\nGRANT APPLY ON TAG PLT.GOVERNANCE.INFO_CLASSIFICATION TO DATABASE ROLE PLT.GOVERNANCE_A;\n\nCREATE TAG IF NOT EXISTS PLT.GOVERNANCE.DOMAIN\n  COMMENT = 'Domain association of the tagged data object or column';\nGRANT READ ON TAG PLT.GOVERNANCE.DOMAIN TO DATABASE ROLE PLT.GOVERNANCE_R;\nGRANT APPLY ON TAG PLT.GOVERNANCE.DOMAIN TO DATABASE ROLE PLT.GOVERNANCE_A;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis guide could not possibly cover the vast array of what is possible using the powerful governance features available. \u003Ca href=\"https://docs.snowflake.com/en/user-guide/governance-skills\"\u003ESnowflake CoCo includes built-in data governance skills\u003C/a\u003E designed to help you understand, protect, and monitor the data in your Snowflake account.\u003C/p\u003E\n","\u003Cp\u003ESnowflake provides a set of built-in DMFs — such as \u003Ccode\u003ESNOWFLAKE.CORE.NULL_COUNT\u003C/code\u003E, \u003Ccode\u003ESNOWFLAKE.CORE.DUPLICATE_COUNT\u003C/code\u003E, and \u003Ccode\u003ESNOWFLAKE.CORE.FRESHNESS\u003C/code\u003E — that cover generic quality checks out of the box. Custom DMFs in \u003Ccode\u003EPLT.GOVERNANCE\u003C/code\u003E should encode Data Vault-specific quality standards that have no system equivalent, such as LDTS staleness, future LDTS values, hash key and business key duplicates, and ghost records.\u003C/p\u003E\n","\u003Cp\u003ESnowflake provides a set of built-in Tags — such as \u003Ccode\u003ESNOWFLAKE.CORE.SEMANTIC_CATEGORY\u003C/code\u003E and \u003Ccode\u003ESNOWFLAKE.CORE.PRIVACY_CATEGORY\u003C/code\u003E — that cover generic sensitive data classification out of the box. Custom Tags stored in \u003Ccode\u003EPLT.GOVERNANCE\u003C/code\u003E should encode your organization's specific or Data Vault-specific tags that have no system equivalent. The \u003Ccode\u003EINFO_CLASSIFICATION\u003C/code\u003E tag is an example. You can map user-defined tags to system-defined classification tags. For example, you can set up a tag map so that every time the system tag SNOWFLAKE.CORE.SEMANTIC_CATEGORY = 'NAME' is applied to a column, the custom tag INFO_CLASSIFICATION = 'Restricted' is also applied. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/classify-intro\"\u003ESensitive Data Classification\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ESnowflake's \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tag-based-masking-policies\"\u003EData Protection Policies\u003C/a\u003E — masking, row access, aggregation, projection — enforce the information classification at the column or row level. Restricted data is typically restricted to a list of named persons, where that list is typically given a domain-specific role and governed in domain-specific manner. Thus, these policies will be created later in the domain-oriented schemas, not in \u003Ccode\u003EPLT.GOVERNANCE\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ESnowflake does not support future grants for tags — \u003Ccode\u003EGRANT APPLY ON TAG\u003C/code\u003E is issued per object immediately after each \u003Ccode\u003ECREATE TAG\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3: Platform Admin Tools Schema\u003C/h3\u003E\n","\u003Cp\u003ELet's create a schema in the platform database for administration tools, containing helper procedures that we'll later use as the platform administrator, reducing repetition and helping achieve consistency later in our deployment. This is a long block of code, with content that might make more sense later, so don't feel the need to understand every detail.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Platform Database: Admin Tools ----------------------------------------------\nUSE ROLE PLT_ADMIN;\nUSE WAREHOUSE ADMIN_WH;\n\nCREATE SCHEMA IF NOT EXISTS PLT.ADMIN_TOOLS\n  WITH MANAGED ACCESS\n  COMMENT = 'Common platform administration tools and utilities';\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    SCHEMA_SUBJECT  VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Base helper: creates a managed-access schema, paired _R/_W database roles, and standard privilege grants. Called by zone-specific wrappers.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db        VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema    VARCHAR DEFAULT UPPER(SCHEMA_NAME);\n    v_subject   VARCHAR DEFAULT SCHEMA_SUBJECT;\nBEGIN\n\n    -- Ensure database-wide roles exist\n    EXECUTE IMMEDIATE 'CREATE DATABASE ROLE IF NOT EXISTS ' || v_db || '.DB_R'\n        || ' COMMENT = ''Database-wide read access''';\n    EXECUTE IMMEDIATE 'CREATE DATABASE ROLE IF NOT EXISTS ' || v_db || '.DB_W'\n        || ' COMMENT = ''Database-wide write and create access''';\n\n    -- Create the managed access schema\n    EXECUTE IMMEDIATE\n        'CREATE SCHEMA IF NOT EXISTS ' || v_db || '.' || v_schema\n        || ' WITH MANAGED ACCESS'\n        || ' COMMENT = ''' || REPLACE(v_subject, '''', '''''') || '''';\n\n    -- Create schema-specific database roles\n    EXECUTE IMMEDIATE\n        'CREATE DATABASE ROLE IF NOT EXISTS ' || v_db || '.' || v_schema || '_R'\n        || ' COMMENT = ''Read access to ' || REPLACE(v_subject, '''', '''''') || ' (' || v_schema || ')''';\n    EXECUTE IMMEDIATE\n        'CREATE DATABASE ROLE IF NOT EXISTS ' || v_db || '.' || v_schema || '_W'\n        || ' COMMENT = ''Write and create access to ' || REPLACE(v_subject, '''', '''''') || ' (' || v_schema || ')''';\n\n    -- _R granted to DB_R and to _W (so _W inherits _R)\n    EXECUTE IMMEDIATE 'GRANT DATABASE ROLE ' || v_db || '.' || v_schema || '_R TO DATABASE ROLE ' || v_db || '.DB_R';\n    EXECUTE IMMEDIATE 'GRANT DATABASE ROLE ' || v_db || '.' || v_schema || '_R TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    -- _W granted to DB_W\n    EXECUTE IMMEDIATE 'GRANT DATABASE ROLE ' || v_db || '.' || v_schema || '_W TO DATABASE ROLE ' || v_db || '.DB_W';\n\n    -- Grant USAGE and MONITOR on database and schema to _R\n    EXECUTE IMMEDIATE 'GRANT USAGE, MONITOR ON DATABASE ' || v_db\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT USAGE, MONITOR ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n\n    RETURN 'SUCCESS: Created schema ' || v_db || '.' || v_schema\n        || ' with roles ' || v_schema || '_R, ' || v_schema || '_W.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_LZ_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    SOURCE_SYSTEM   VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Creates a Landing Zone schema and roles for a source system, adding insert-only write and stage/pipe future grants to enforce immutability after initial load.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db     VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema VARCHAR DEFAULT UPPER(SCHEMA_NAME);\nBEGIN\n\n    CALL PLT.ADMIN_TOOLS.CREATE_SCHEMA_AND_ROLES(:v_db, :v_schema, :SOURCE_SYSTEM);\n\n    -- _R: read-only access to landing zone objects\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n\n    -- _W: insert-only write access (LZ data is immutable after initial load)\n    EXECUTE IMMEDIATE 'GRANT INSERT ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT MONITOR, OPERATE ON FUTURE PIPES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT USAGE, READ, WRITE ON FUTURE STAGES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    \n    EXECUTE IMMEDIATE 'GRANT CREATE TABLE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE PIPE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE STAGE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    RETURN 'SUCCESS: Created schema ' || v_db || '.' || v_schema\n        || ' for source system ' || SOURCE_SYSTEM || ' with future grants applied.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_DOMAIN_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    DOMAIN          VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Creates a domain-oriented schema and roles with full analytical object future grants (tables, views, dynamic tables, functions, procedures, streams, tasks, DMFs, and policies). Base procedure for DV and DW zone wrappers.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db     VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema VARCHAR DEFAULT UPPER(SCHEMA_NAME);\nBEGIN\n    \n    CALL PLT.ADMIN_TOOLS.CREATE_SCHEMA_AND_ROLES(:v_db, :v_schema, :DOMAIN);\n\n    EXECUTE IMMEDIATE 'ALTER SCHEMA ' || v_db || '.' || v_schema\n        || ' SET TAG PLT.GOVERNANCE.DOMAIN = ''' || REPLACE(DOMAIN, '''', '''''') || '''';\n\n    -- _R: read access to domain schema objects\n    EXECUTE IMMEDIATE 'GRANT SELECT, REFERENCES ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE DYNAMIC TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE VIEWS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT USAGE ON FUTURE FUNCTIONS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT USAGE ON FUTURE PROCEDURES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n\n    -- _W: write access and pipeline management\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE STREAMS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT MONITOR, OPERATE ON FUTURE TASKS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT MONITOR, OPERATE ON FUTURE DYNAMIC TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    -- _W: schema-level CREATE privileges\n    EXECUTE IMMEDIATE 'GRANT CREATE TABLE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE DYNAMIC TABLE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE VIEW ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE FUNCTION ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE PROCEDURE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE STREAM ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE TASK ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE STAGE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE DATA METRIC FUNCTION ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE MASKING POLICY ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE ROW ACCESS POLICY ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE TAG ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    RETURN 'SUCCESS: Created domain schema ' || v_db || '.' || v_schema\n        || ' for ' || DOMAIN || '.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_DV_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    DOMAIN          VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Creates a Data Vault schema and roles for a domain, enforcing insert-only writes to preserve Data Vault immutability.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db     VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema VARCHAR DEFAULT UPPER(SCHEMA_NAME);\nBEGIN\n    \n    CALL PLT.ADMIN_TOOLS.CREATE_DOMAIN_SCHEMA_AND_ROLES(:v_db, :v_schema, :DOMAIN);\n\n    -- _W: insert-only (DV tables are immutable)\n    EXECUTE IMMEDIATE 'GRANT INSERT ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    RETURN 'SUCCESS: Created Data Vault schema ' || v_db || '.' || v_schema\n        || ' for ' || DOMAIN || ' with future grants applied.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_DW_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    DOMAIN          VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Creates a Data Warehouse schema and roles for a domain, granting full DML write access and semantic view creation for AI-agent consumption.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db     VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema VARCHAR DEFAULT UPPER(SCHEMA_NAME);\nBEGIN\n\n    CALL PLT.ADMIN_TOOLS.CREATE_DOMAIN_SCHEMA_AND_ROLES(:v_db, :v_schema, :DOMAIN);\n\n    -- _W: full write access to DW tables\n    EXECUTE IMMEDIATE 'GRANT INSERT, UPDATE, DELETE, TRUNCATE ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    -- _R/_W: semantic view access (DW schemas are AI-agent consumable)\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE SEMANTIC VIEWS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT CREATE SEMANTIC VIEW ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    RETURN 'SUCCESS: Created Data Warehouse schema ' || v_db || '.' || v_schema\n        || ' for ' || DOMAIN || ' with future grants applied.';\nEND;\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4: Landing Zone Database\u003C/h3\u003E\n","\u003Cp\u003ELet's create a database that will serve as our Landing Zone, as well as a role and warehouse designed for ingesting data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Development LZ Ingestion Role -----------------------------------------------\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS DEV_LZ_INGEST\n  COMMENT = 'Ingestion role for the development landing zone';\n\nGRANT ROLE DEV_LZ_INGEST TO ROLE SYSADMIN;\n\n-- Development LZ Ingestion Warehouse ------------------------------------------\nUSE ROLE SYSADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS DEV_INGEST_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE\n  COMMENT = 'Warehouse for automated data ingestion workloads';\n\nGRANT ALL PRIVILEGES ON WAREHOUSE DEV_INGEST_WH TO ROLE PLT_ADMIN WITH GRANT OPTION;\nGRANT USAGE, OPERATE ON WAREHOUSE DEV_INGEST_WH TO ROLE DEV_LZ_INGEST;\n\n-- Development LZ Database -----------------------------------------------------\nCREATE DATABASE IF NOT EXISTS DEV_LZ\n  COMMENT = 'Development landing zone for raw data ingestion';\n\nDROP SCHEMA IF EXISTS DEV_LZ.PUBLIC; -- Removing automatically generated schema\n\nGRANT ALL PRIVILEGES ON DATABASE DEV_LZ TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDevelopment Environment\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EYou may note that this serves as a development environment example. Objects developed here might later be promoted to a TST_LZ for testing, and then a main LZ, for production use. We're leaving those out here for the sake of brevity. However, in those test and production environments, ingestion of data is typically performed by an automated Service User. In the event multiple Service Users will be used to ingest data using multiple solutions, you may wish to create multiple ingestion roles.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 5: Enterprise Memory and Information Delivery Zone Databases\u003C/h3\u003E\n","\u003Cp\u003ENow, let's create two more databases. The first will serve as our Data Vault, holding staging, raw vault, and business vault objects. The second will serve as our analyst-facing interface to the Information Delivery Zone. We'll also create a QA analyst role and a warehouse for engineering (development and testing) use, and a warehouse for automated data transformation.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- QA (Dev/Test) Analyst Role --------------------------------------------------\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS QA_ANALYST\n  COMMENT = 'General analyst role with read-only access to the information delivery zone';\n\nGRANT ROLE QA_ANALYST TO ROLE PLT_ADMIN;\n\n\n-- Data Transformation and Engineering Warehouses ------------------------------\nUSE ROLE SYSADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS DEV_XFORM_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE\n  COMMENT = 'Warehouse for automated data transformation workloads';\n\nGRANT ALL PRIVILEGES ON WAREHOUSE DEV_XFORM_WH TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\nCREATE WAREHOUSE IF NOT EXISTS ENGINEERING_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE\n  COMMENT = 'Warehouse for general development and testing use';\n\nGRANT ALL PRIVILEGES ON WAREHOUSE ENGINEERING_WH TO ROLE PLT_ADMIN WITH GRANT OPTION;\nGRANT USAGE, OPERATE ON WAREHOUSE ENGINEERING_WH TO ROLE QA_ANALYST;\n\n-- Data Vault Database ---------------------------------------------------------\nCREATE DATABASE IF NOT EXISTS DEV_DV\n  COMMENT = 'Enterprise memory of domain-oriented data vault models';\n\nDROP SCHEMA IF EXISTS DEV_DV.PUBLIC; -- Removing automatically generated schema\n\nGRANT ALL PRIVILEGES ON DATABASE DEV_DV TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\n-- Data Warehouse Database -----------------------------------------------------\nCREATE DATABASE IF NOT EXISTS DEV_DW\n  COMMENT = 'Information delivery of domain-oriented models';\n\nDROP SCHEMA IF EXISTS DEV_DW.PUBLIC; -- Removing automatically generated schema\n\nGRANT ALL PRIVILEGES ON DATABASE DEV_DW TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETransformation Role and Ingestion Privileges\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EYou may be wondering, why did we not grant privileges to the ingestion role, nor ever create a role for transforming data in the data vault? We will do this later, after the schemas in these databases are in place.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBusiness Architecture, Domains and Ontologies\u003C/h2\u003E\n","\u003Cp\u003EIt is critical that our Business Architecture inform our Information Architecture. Let's take a moment to think about how Business Architecture might inform these next examples.\u003C/p\u003E\n","\u003Ch3\u003EDomains\u003C/h3\u003E\n","\u003Cp\u003EBusiness architecture can be organized conceptually into a \u003Cstrong\u003Ehierarchy of domains\u003C/strong\u003E. Eric Evans, in the book Domain Driven Design, defines a \u003Cstrong\u003Edomain\u003C/strong\u003E as, "a sphere of knowledge, influence, or activity." It is important to recognize that, assuming your organization has been performing business activities regularly, these domains already exist. Typically, they can be recognized and diagramed in a hierarchy, where domains can be broken into sub-domains. The \u003Cstrong\u003Eactivities\u003C/strong\u003E in these domains \u003Cstrong\u003Eoriginate data\u003C/strong\u003E as valuable assets, not waste byproducts.\u003C/p\u003E\n","\u003Cp\u003EAlso, a domain's business activities can rarely function without using data originating from other domains as input. For example, the \u003Cstrong\u003EFinance\u003C/strong\u003E activity of accounting for revenue recognition in the prior quarter depends on invoicing information associated with fulfilled orders, that invoice data originating from activities in \u003Cstrong\u003ECustomer Service\u003C/strong\u003E. Before being invoiced, those orders were fulfilled by activities in \u003Cstrong\u003EManufacturing & Delivery\u003C/strong\u003E. Those orders originated with activities in \u003Cstrong\u003ESales & Marketing\u003C/strong\u003E. Placed but unfulfilled orders, or possibly just a Sales & Marketing expectation of future orders, might inform a Finance domain's Revenue Forecast. The people performing all those activities are all hired, tracked and managed through activities in the \u003Cstrong\u003EWorkforce\u003C/strong\u003E domain. And those workers were likely first given an email address through activities the \u003Cstrong\u003EIT Delivery\u003C/strong\u003E domain. The complex web of connections between domains is often difficult, if not impossible, to diagram or comprehend in total. Within the perspective of only a single domain, understanding is more easily achieved.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: The domain examples above are inspired by the \u003Ca href=\"https://www.tbmcouncil.org/learn-tbm/resource-center/the-tbm-taxonomy/\"\u003ETBM Taxonomy's\u003C/a\u003E Business Layer Solutions Hierarchy.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EDomain Taxonomy and Ontologies\u003C/h3\u003E\n","\u003Cp\u003EThe business processes, as performed in an organization, along with business keys (how the people performing those activities identify things), units of work that connect business keys, descriptive information produced by activities, what technology systems are used, and input/output connections to other domains, can all be formally described in \u003Cstrong\u003Eontologies\u003C/strong\u003E. These ontologies can be captured in simple text documents or aided by knowledge graph. And because each domain has a web of connections to other domains, not just hierarchical relationships, the domain hierarchy becomes a \u003Cstrong\u003Edomain taxonomy\u003C/strong\u003E. When getting started, don't attempt to "boil the ocean," but rather limit scope to a prioritized set of specific business objectives. However, that which is formally documented should be captured accurately, utilizing the domain expertise of those performing these business processes, reviewing with and gaining the formal approval of those who have dominion over the given domain(s). You don't want a documented ontology of the Finance domain that the CFO would not recognize or approve of. Every domain, when formally documented, should have an obvious authority figure, already having dominion over the activities, processes, and business vocabulary used within that domain.\u003C/p\u003E\n","\u003Cp\u003EAs an example, we'll consider the following simple domain hierarchy, starting with just two domains, Sales & Marketing and Customer Service.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/exampledomains.png?v=7a7bb18b\" alt=\"Example Domain Hierarchy\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWith Snowflake, schema objects -- which include tables, views, stages, file formats, pipes, streams, UDFs, stored procedures, and more -- always exist within a schema. While access control is possible at the schema object level, assigning privileges on each individual object, doing so at that grain quickly becomes tedious and costly to maintain. When objects with common access control objectives are grouped together into schemas, maintaining access controls becomes much easier. Real-world governance is almost always domain-oriented. Thus, we'll use domain-oriented schemas to organize the objects in our Enterprise Memory Zone and beyond, promoting domain-oriented governance.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFrom Logical Business Architecture to Implementation\u003C/h2\u003E\n","\u003Ch3\u003EStep 6: Sample Source System Schema\u003C/h3\u003E\n","\u003Cp\u003EIn a real world scenario, because the data in the Landing Zone is source-system-oriented, a schema found in a Landing Zone database should be associated with a source system. For the sake of simplicity, let's create a single schema designed to land ingested sample data from the TPC-H decision support benchmark.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Landing Zone Schemas --------------------------------------------------------\nUSE ROLE PLT_ADMIN;\nUSE WAREHOUSE ADMIN_WH;\n\nCALL PLT.ADMIN_TOOLS.CREATE_LZ_SCHEMA_AND_ROLES('DEV_LZ', 'TPCH_REF', 'Static Reference Data');\nCALL PLT.ADMIN_TOOLS.CREATE_LZ_SCHEMA_AND_ROLES('DEV_LZ', 'TPCH_CUSTOMER_SYS', 'Customer System');\nCALL PLT.ADMIN_TOOLS.CREATE_LZ_SCHEMA_AND_ROLES('DEV_LZ', 'TPCH_ORDERS_SYS', 'Orders System');\n\nGRANT DATABASE ROLE DEV_LZ.DB_W TO ROLE DEV_LZ_INGEST;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote the simplicity of creating the source system schemas, granting write access to the functional role DEV_LZ_INGEST, by utilizing a common stored procedure that creates the managed access schema, access roles, and grants privileges to those roles. When multiple service users are able to write to the landing zone, be sure use specialized functional roles, assigning those the schema-specific write access roles.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 7: Domain-Oriented Schemas\u003C/h3\u003E\n","\u003Cp\u003ENow that we know our domains, let's create domain-oriented schemas in the DV and DW databases, representing our Enterprise Memory and Information Delivery Zones.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Enterprise Memory and Information Delivery Schemas --------------------------\nUSE ROLE PLT_ADMIN;\nUSE WAREHOUSE ADMIN_WH;\n\nCALL PLT.ADMIN_TOOLS.CREATE_DV_SCHEMA_AND_ROLES('DEV_DV', 'SALESMKT', 'Sales & Marketing');\nCALL PLT.ADMIN_TOOLS.CREATE_DW_SCHEMA_AND_ROLES('DEV_DW', 'SALESMKT', 'Sales & Marketing');\nCALL PLT.ADMIN_TOOLS.CREATE_DV_SCHEMA_AND_ROLES('DEV_DV', 'CUSTSERV', 'Customer Service');\nCALL PLT.ADMIN_TOOLS.CREATE_DW_SCHEMA_AND_ROLES('DEV_DW', 'CUSTSERV', 'Customer Service');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote that while the stored procedure creates the schemas and access roles, we don't yet have domain-oriented functional roles to which we may grant the access roles.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 8: Domain-Oriented Roles and Privileges\u003C/h3\u003E\n","\u003Cp\u003ENow, let's create domain-oriented functional roles, and grant access allowing for reading from the landing zone and other domain's data vault objects, but writing only to the domain-specific schemas in the Enterprise Memory and Information Delivery Zones. We grant read access to the entire Landing Zone, because source systems are often not domain specific. A domain-specific Landing Zone database could be used for a set of source systems restricted to just one domain. We grant cross-domain read access to the data vault models to avoid duplication of efforts and promote effective governance, while allowing one domain to leverage curated information from another. We restrict writing to the domain-specific roles, promoting accountability and effective governance over what is built.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Domain-Oriented Data Engineer Roles -----------------------------------------\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS SALESMKT_ENGINEER\n  COMMENT = 'Data engineering role for the development of Sales & Marketing objects and transformations';\nGRANT ROLE SALESMKT_ENGINEER TO ROLE PLT_ADMIN;\n\nCREATE ROLE IF NOT EXISTS CUSTSERV_ENGINEER\n  COMMENT = 'Data engineering role for the development of Customer Service objects and transformations';\nGRANT ROLE CUSTSERV_ENGINEER TO ROLE PLT_ADMIN;\n\nUSE ROLE ACCOUNTADMIN;\n\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE SALESMKT_ENGINEER;\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE CUSTSERV_ENGINEER;\n\n-- Domain-Oriented Data Engineer Privileges ------------------------------------\nUSE ROLE PLT_ADMIN;\n\nGRANT USAGE, OPERATE ON WAREHOUSE ENGINEERING_WH TO ROLE SALESMKT_ENGINEER;\nGRANT USAGE, OPERATE ON WAREHOUSE DEV_XFORM_WH TO ROLE SALESMKT_ENGINEER;\n\nGRANT DATABASE ROLE DEV_LZ.DB_R TO ROLE SALESMKT_ENGINEER;\nGRANT DATABASE ROLE DEV_DV.SALESMKT_W TO ROLE SALESMKT_ENGINEER;\nGRANT DATABASE ROLE DEV_DV.DB_R TO ROLE SALESMKT_ENGINEER;\nGRANT DATABASE ROLE DEV_DW.SALESMKT_W TO ROLE SALESMKT_ENGINEER;\n\nGRANT USAGE, OPERATE ON WAREHOUSE ENGINEERING_WH TO ROLE CUSTSERV_ENGINEER;\nGRANT USAGE, OPERATE ON WAREHOUSE DEV_XFORM_WH TO ROLE CUSTSERV_ENGINEER;\n\nGRANT DATABASE ROLE DEV_LZ.DB_R TO ROLE CUSTSERV_ENGINEER;\nGRANT DATABASE ROLE DEV_DV.CUSTSERV_W TO ROLE CUSTSERV_ENGINEER;\nGRANT DATABASE ROLE DEV_DV.DB_R TO ROLE CUSTSERV_ENGINEER;\nGRANT DATABASE ROLE DEV_DW.CUSTSERV_W TO ROLE CUSTSERV_ENGINEER;\n\nGRANT DATABASE ROLE DEV_DW.DB_R TO ROLE QA_ANALYST;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ENow that everything has been created successfully, let's check it out! We can use the SQL below, or on the left select the Database Explorer icon. Remember, your selected role will influence what you can see after clicking the refresh button.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Explore as a Platform Admin -------------------------------------------------\nUSE ROLE PLT_ADMIN;\nUSE SECONDARY ROLES NONE;\nUSE WAREHOUSE ADMIN_WH;\n\nSHOW DATABASES ->> SELECT "name", "comment" FROM $1 WHERE "kind" = 'STANDARD';\n\nSHOW SCHEMAS IN DATABASE DEV_LZ ->> SELECT "database_name", "name", "comment" FROM $1;\n\n-- Explore as a QA Analyst -----------------------------------------------------\nUSE ROLE QA_ANALYST;\nUSE WAREHOUSE ENGINEERING_WH;\n\nSHOW DATABASES ->> SELECT "name", "comment" FROM $1 WHERE "kind" = 'STANDARD';\n\nSHOW SCHEMAS IN DATABASE DEV_DW ->> SELECT "database_name", "name", "comment" FROM $1;\n\nSHOW TAGS IN DATABASE PLT;\n\nSELECT * FROM TABLE(DEV_DW.INFORMATION_SCHEMA.TAG_REFERENCES('DEV_DW.CUSTSERV', 'SCHEMA'));\n\nSELECT * FROM TABLE(DEV_DW.INFORMATION_SCHEMA.TAG_REFERENCES('DEV_DW.SALESMKT', 'SCHEMA'));\n\nSHOW SCHEMAS IN DATABASE DEV_DV; -- This will error, as the role doesn't have access\n\nSHOW SCHEMAS IN DATABASE DEV_LZ; -- This will error, as the role doesn't have access\n\n-- Explore as Dev LZ Ingest ----------------------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\n\nSHOW DATABASES ->> SELECT "name", "comment" FROM $1 WHERE "kind" = 'STANDARD';\n\nSHOW SCHEMAS IN DATABASE DEV_LZ ->> SELECT "database_name", "name", "comment" FROM $1;\n\nSHOW SCHEMAS IN DATABASE DEV_DV; -- This will error, as the role doesn't have access\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe covered some of the basics to get started. As an architect, you may be considering creating test and main production environments; or additional roles, both platform oriented and domain oriented; or additional tags, access policies and restricted information roles; or data metric functions, with a data quality / error mart; or additional common functions and stored procedures; or even Presentation Zone databases for custom customer-facing data shares, Streamlit apps and dashboards, or Agents for use with Snowflake CoWork. We hope we've whet your appetite for more.\u003C/p\u003E\n","\u003Cp\u003EYou are now ready to advance to the next guide, \u003Ca href=\"https://www.snowflake.com/en/developers/guides/vhol-data-vault/\"\u003EBuilding a Real-Time Data Vault in Snowflake\u003C/a\u003E! Data Vault 2.1 consists of 3 pillars -- methodology, architecture, and model -- and while this guide focuses on architecture, the next guide focuses on modeling. We've updated that guide to leverage the structure defined here.\u003C/p\u003E\n","\u003Cp\u003EIf you want to learn more about Data Vault 2.1, check out the latest content from Data Vault Alliance: the \u003Ca href=\"https://datavaultalliance.com/\"\u003Eblog, training and certification resources\u003C/a\u003E, the \u003Ca href=\"https://www.dvaunited.com/\"\u003EDVA United\u003C/a\u003E community, and free content on \u003Ca href=\"https://www.youtube.com/@DataVaultAlliance/videos\"\u003EYouTube\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to translate Data Vault's logical architecture zones into Snowflake databases\u003C/li\u003E\u003Cli\u003EHow to get started with a Domain Taxonomy and Ontologies\u003C/li\u003E\u003Cli\u003EHow to create Snowflake schemas for either source systems or domains\u003C/li\u003E\u003Cli\u003EHow to create and leverage a common platform database for shared resources\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat's Next\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/vhol-data-vault/\"\u003EBuilding a Real-Time Data Vault in Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Defensible Analytics\n\nToday, AI tools interact with data on a daily basis, and enterprises are increasingly recognizing the need for a mature **system of information management** that provides **defensible analytics** -- analytics based upon auditable, trustworthy enterprise memory, with unambiguous business context.\n\nInstead of an adhoc approach, making it up as we go along, enterprises require proven patterns for a [system of information management](https://datavaultalliance.com/strategy-operating/system-information-management/). We need to rely upon a system that not only transforms data, but provides continuous trustworthy **information**, aligned to business needs, accompanied by business context, using business vocabulary, with auditable lineage to the originating source. The system must generate the evidence needed to make confident decisions, defend those decisions, and enable consistent answers to questions given to AI agents. The system must be responsive to change, which comes at the speed of business, compatible with an agile approach to implementation, maximizing reuse and avoiding duplication of effort, yet never destroying the auditability and reliability of what has already been delivered.\n\nData are assets, relevant to our decision-making processes, and can reduce the effort of decision-making, while also increasing the quality, speed, and execution of our decisions. Improving decision effectiveness improves the performance of our enterprise for all stakeholders. Knowing that, how do we manage information appropriately?\n\n### What is Data Vault?\n\nIn 2018, at the World-Wide Data Vault Consortium (WWDVC), [Bill Inmon](https://en.wikipedia.org/wiki/Bill_Inmon) updated his classic definition of the Data Warehouse.\n\n![A data warehouse is a subject-oriented, integrated (by business key), time-variant and non-volatile collection of data in support of management’s decision-making process, and/or in support of auditability as a system-of-record - William Inmon 2018](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/inmondw2018.png?v=7a7bb18b)\n\n This was only a minor variation, and this definition remains highly relevant today. He followed this update with his recommendation of the Data Vault system to build it. His definition does not contain the words schema-on-write or structured. His phrase \"collection of data\" includes [unstructured](https://docs.snowflake.com/en/user-guide/unstructured-intro) and [semi-structured](https://docs.snowflake.com/en/user-guide/semistructured-intro) data types. Whether we call that a data warehouse, data lake, or data lakehouse, nothing beats the Snowflake AI Data Cloud when it comes to handling those diverse types of data.\n\nData Vault, as invented by [Dan Linstedt](https://datavaultalliance.com/#about), is not just a collection of data modeling standards, but a complete system of information management. That system has key pillars of methodology, architecture, and model, always supporting informed decision-making and delivering business outcomes. This guide cannot possibly detail the entire Data Vault system. To gain a full understanding of Data Vault, we recommend working with experts & partners from [Data Vault Alliance](https://datavaultalliance.com/).\n\nIn Data Vault 2.1, Linstedt has clearly articulated a logical architecture consisting of gated [zones](https://www.youtube.com/watch?v=OkI1LWsz9Nc). At the core are the Landing Zone, the Enterprise Memory Zone, and the Information Delivery Zone. However, before a functional implementation, the logical architecture must progress to physical. This guide is an introduction to how that may be accomplished in the Snowflake AI Data Cloud.\n\n### Prerequisites\n- Familiarity with [Snowflake key concepts and architecture](https://docs.snowflake.com/en/user-guide/intro-key-concepts)\n- Familiarity with [Data Vault methodology and architecture](https://datavaultalliance.com/#resources)\n\n### What You’ll Learn\n- How to structure a Snowflake account for Data Vault\n- How to enable domain-oriented development and governance in your Data Vault\n\n### What You’ll Need \n- A Snowflake account -- we recommend starting with a fresh Enterprise Edition [trial](https://trial.snowflake.com/) account for this guide\n\n### What You’ll Build \n- A Data Vault environment using Snowflake\n\n\n\u003C!-- ------------------------ --\u003E\n## Reference Architecture\n\nLet’s start with the target architecture. \n\n![Multi-Tier Data Vault Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/multitierdatavaultarchitecture.png?v=7a7bb18b)\n\nOn the very left of figure above we have a list of **data sources** that typically include a mix of operational databases, files, streaming event sources, SaaS apps, and more. The [Snowflake Marketplace](https://www.snowflake.com/en/product/features/marketplace/) allows us to tap into 3rd party data to augment our own.\n\nOn the very right we have our ultimate **data consumers**: business users, AI agents, data scientists, IT systems or even other companies you decide to share your data with.\n\nArchitecturally, we will consider the following Data Vault 2.1 zones:\n- **Transient Zone**: used to transport ephemeral data from source systems and make it accessible for ingestion into Snowflake. We won't dive deep into this zone in this guide, but be sure to check out [Snowflake Openflow](https://docs.snowflake.com/en/user-guide/data-integration/openflow/about) and the [Snowflake Ecosystem](https://docs.snowflake.com/en/user-guide/ecosystem) as key enablers for this zone.\n- **Landing Zone**: a managed persistent staging area (PSA) where data is ingested and kept as close as possible to its original state, as established by the source systems it came from. For this Snowflake has [multiple options](https://docs.snowflake.com/en/guides-overview-loading-data), including [bulk loading of files](https://docs.snowflake.com/en/user-guide/data-load-local-file-system), continuous loading of micro-batches of files through [Snowpipe](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro), or continuous rows of data through [Snowpipe Streaming](https://docs.snowflake.com/en/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview). Snowflake allows you to load and store structured, unstructured, and semi-structured data in the original format whilst automatically optimizing the physical structure for efficient query access. But this zone isn't just a data dump. Per Data Vault 2.1, in this zone the data is immutable, stored as it was received from source, with no changes to the content. Here, data are [governed as assets](https://docs.snowflake.com/en/guides-overview-govern). Metadata may be documented, data may be tagged, profiled, and encrypted. Snowflake's [storage lifecycle policies](https://docs.snowflake.com/en/user-guide/storage-management/storage-lifecycle-policies) may be used to automatically move older data to more cost-effective cool and cold archival tiers, keeping expenses down.\n- **Enterprise Memory Zone**: where data become subject-oriented, integrated by business key, time-variant and non-volatile. This is where the data vault modeling patterns -- such as hubs, links, and satellites -- begin to be applied. Data enters the raw vault, sparsely built, where only hard business rules are applied, loading all records received from source.\n- **Information Delivery Zone**: a collection of consumer-oriented models, designed to inform decision-making processes. This can be implemented as a set (or multiple sets) of views. It is common to see the use of dimensional models (facts and dimensions, star or snowflake) or denormalized flat tables (for data science or sharing) but it could be any other modeling style that fits best for your data consumer. The Business Vault contains data vault objects with soft business rules applied, augmenting the intelligence of the system, and potentially enhancing the performance of the consumer-facing views. Soft business rules may include the calculation of metrics, commonly used aggregations, master data records, PIT and Bridge tables helping to simplify access to information with highly performant views of facts and dimensions. Snowflake’s scalability will support the required speed of access at any point of this data lifecycle. You should consider materialization of Business Vault and other Information Delivery objects as optional.\n\nWith a brand new Snowflake account, objects are not automatically created to represent these logical concepts. We must create them, keeping in mind Snowflake's object hierarchy.\n\n![Snowflake Object Hierarchy: Organization - Account - Database  - Schema - Object](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/objecthierarchy.png?v=7a7bb18b)\n\n\n\u003C!-- ------------------------ --\u003E\n## From Logical Architecture Zones to Implemented Snowflake Objects\n\nIf you're familiar with Snowflake's [Data Cloud Deployment Framework (DCDF)](https://www.snowflake.com/en/developers/guides/dcdf-incremental-processing/), you might recognize parallels between the Data Vault zones and the DCDF databases: Raw, Integration, and Presentation. This is because databases are a great place to start for the physical implementation of the logical zones. Snowflake Databases are not simply containers, but a key aspect of the physical architecture with significant governance implications.\n\nIn addition to the databases related to our Data Vault zones, a common platform database can serve to hold objects not specific to any single zone. We'll create this, as well.\n\n\u003E For the sake of simplicity, this guide will not delve into utilizing multiple Snowflake accounts. However, adopting a multi-account strategy, taking advantage of Snowflake's remarkable [Secure Data Sharing](https://docs.snowflake.com/en/user-guide/data-sharing-intro), could unlock significant value for your organization. A multi-account strategy is one where a single [Organization](https://docs.snowflake.com/en/user-guide/organizations) has multiple Accounts, each serving a specific purpose. This provides you with the flexibility to distribute databases across multiple accounts, where [shared read-only access to data](https://docs.snowflake.com/en/user-guide/data-sharing-intro) with zero copying, and thus enabling the use of different [Editions](https://docs.snowflake.com/en/user-guide/intro-editions) for different Accounts, which enable different feature sets and have different compute pricing. Databases may also be [replicated for business continuity and disaster recovery purposes](https://docs.snowflake.com/en/user-guide/account-replication-config) to other accounts.\n\n### Step 0: Login to your Snowflake Account, Create a Workspace File\n\nLogin to your Snowflake trial account. You will need to use the same user and password that you used to login to your Snowflake account the first time.\n\nTo get started on the implementation, click on Projects -\u003E Workspaces to open the Workspaces interface.\n\n![An Empty SQL File](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/menuworkspaces.png?v=7a7bb18b)\n\nMake a mental note of the content on the Home tab. Click the + Add new button, then SQL File, and name this new file DVArchitecture.sql.\n\n![An Empty SQL File](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/workspaceshome.png?v=7a7bb18b)\n\nThis is an intuitive SQL workbench. It has a section for the code we'll copy from the guide and paste into the SQL file. It also has a 'run' button to execute the code, the result panel at the bottom, and [Snowflake CoCo](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code#cortex-code-in-sf-web-interface) to the right to provide natural language assistance.\n\n![An Empty SQL File](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/emptysqlfile.png?v=7a7bb18b)\n\nAt the end of this guide, you will explore what you've created through the eyes of different roles. However, with a new trial account, your user will default to using all secondary roles. So, we'll start by changing that to using none. This ensures secondary roles are not automatically activated at login, so you can activate them explicitly when needed:\n\n```sql\n-- Defaulting Secondary Roles to None ------------------------------------------\nUSE ROLE ACCOUNTADMIN;\n\nALTER USER SET DEFAULT_SECONDARY_ROLES = ();\n```\n\nThis setting is checked at sign in, so click your user icon at the bottom left corner, Sign Out, and then sign back in. Once signed back in, open your DVArchitecture.sql SQL File.\n\n### Step 1: Platform Role, Warehouse, and Database\n\nAssuming you are using a new trial account, we'll start with some basics. We'll create a Platform Administrator role, as well as a virtual warehouse to use for basic administrative tasks, and a common platform database for common objects. This is meant to serve only as an example. Your role-based access control (RBAC) strategy and design may differ, but we'll use this example later in the guide. We'll grant the ability for any user to see the Platform database using the PUBLIC role.\n\n```sql\n-- Platform Administration Role ------------------------------------------------\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS PLT_ADMIN\n  COMMENT = 'Platform administrator role for managing shared platform objects';\n\nGRANT ROLE PLT_ADMIN TO ROLE SYSADMIN;\n\n-- Administration Warehouse ----------------------------------------------------\nUSE ROLE SYSADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS ADMIN_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE\n  COMMENT = 'Administration warehouse for platform admin tasks';\n\nGRANT ALL PRIVILEGES ON WAREHOUSE ADMIN_WH TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\n-- Platform Database -----------------------------------------------------------\nCREATE DATABASE IF NOT EXISTS PLT\n  COMMENT = 'Common centralized platform database for shared objects and utilities';\n\nDROP SCHEMA IF EXISTS PLT.PUBLIC; -- Removing automatically generated schema\n\nGRANT ALL PRIVILEGES ON DATABASE PLT TO ROLE PLT_ADMIN WITH GRANT OPTION;\n```\n\n### Step 2: Platform Governance Schema\n\nLet's create a schema in the platform database for governance, containing common objects that we'll provide as the platform administrator, or as a central enablement team.\n\n```sql\n-- Platform Database: Governance -----------------------------------------------\nUSE ROLE PLT_ADMIN;\nUSE WAREHOUSE ADMIN_WH;\n\nCREATE SCHEMA IF NOT EXISTS PLT.GOVERNANCE\n  WITH MANAGED ACCESS\n  COMMENT = 'Platform governance objects, such as classification tags for annotating data assets';\n\nCREATE DATABASE ROLE IF NOT EXISTS PLT.GOVERNANCE_R\n  COMMENT = 'Read access to platform governance objects and tag definitions';\nCREATE DATABASE ROLE IF NOT EXISTS PLT.GOVERNANCE_A\n  COMMENT = 'Apply and invoke access to platform governance objects';\nCREATE DATABASE ROLE IF NOT EXISTS PLT.GOVERNANCE_W\n  COMMENT = 'Create and manage platform governance objects';\n\nGRANT DATABASE ROLE PLT.GOVERNANCE_R TO DATABASE ROLE PLT.GOVERNANCE_A;\nGRANT DATABASE ROLE PLT.GOVERNANCE_A TO DATABASE ROLE PLT.GOVERNANCE_W;\n\nGRANT DATABASE ROLE PLT.GOVERNANCE_R TO ROLE PUBLIC;\nGRANT DATABASE ROLE PLT.GOVERNANCE_W TO ROLE PLT_ADMIN;\n\n-- GOVERNANCE_R: read access to governance schema and tag values\nGRANT USAGE ON DATABASE PLT TO DATABASE ROLE PLT.GOVERNANCE_R;\nGRANT USAGE ON SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_R;\n\n-- GOVERNANCE_A: apply platform governance objects\nGRANT MONITOR ON SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_A;\nGRANT USAGE ON FUTURE DATA METRIC FUNCTIONS IN SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_A;\n-- Note that the APPLY privilege may not be granted to future tags, but granted after tags are created\n\n-- GOVERNANCE_W: create and manage platform governance objects\nGRANT CREATE DATA METRIC FUNCTION ON SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_W;\nGRANT CREATE TAG ON SCHEMA PLT.GOVERNANCE TO DATABASE ROLE PLT.GOVERNANCE_W;\n\nCREATE TAG IF NOT EXISTS PLT.GOVERNANCE.INFO_CLASSIFICATION\n  ALLOWED_VALUES 'Public', 'Internal', 'Restricted'\n  COMMENT = 'Information classification level of the tagged data object or column';\nGRANT READ ON TAG PLT.GOVERNANCE.INFO_CLASSIFICATION TO DATABASE ROLE PLT.GOVERNANCE_R;\nGRANT APPLY ON TAG PLT.GOVERNANCE.INFO_CLASSIFICATION TO DATABASE ROLE PLT.GOVERNANCE_A;\n\nCREATE TAG IF NOT EXISTS PLT.GOVERNANCE.DOMAIN\n  COMMENT = 'Domain association of the tagged data object or column';\nGRANT READ ON TAG PLT.GOVERNANCE.DOMAIN TO DATABASE ROLE PLT.GOVERNANCE_R;\nGRANT APPLY ON TAG PLT.GOVERNANCE.DOMAIN TO DATABASE ROLE PLT.GOVERNANCE_A;\n```\n\n\u003E This guide could not possibly cover the vast array of what is possible using the powerful governance features available. [Snowflake CoCo includes built-in data governance skills](https://docs.snowflake.com/en/user-guide/governance-skills) designed to help you understand, protect, and monitor the data in your Snowflake account.\n\u003E\n\u003E Snowflake provides a set of built-in DMFs — such as `SNOWFLAKE.CORE.NULL_COUNT`, `SNOWFLAKE.CORE.DUPLICATE_COUNT`, and `SNOWFLAKE.CORE.FRESHNESS` — that cover generic quality checks out of the box. Custom DMFs in `PLT.GOVERNANCE` should encode Data Vault-specific quality standards that have no system equivalent, such as LDTS staleness, future LDTS values, hash key and business key duplicates, and ghost records.\n\u003E\n\u003E Snowflake provides a set of built-in Tags — such as `SNOWFLAKE.CORE.SEMANTIC_CATEGORY` and `SNOWFLAKE.CORE.PRIVACY_CATEGORY` — that cover generic sensitive data classification out of the box. Custom Tags stored in `PLT.GOVERNANCE` should encode your organization's specific or Data Vault-specific tags that have no system equivalent. The `INFO_CLASSIFICATION` tag is an example. You can map user-defined tags to system-defined classification tags. For example, you can set up a tag map so that every time the system tag SNOWFLAKE.CORE.SEMANTIC_CATEGORY = 'NAME' is applied to a column, the custom tag INFO_CLASSIFICATION = 'Restricted' is also applied. See [Sensitive Data Classification](https://docs.snowflake.com/en/user-guide/classify-intro).\n\u003E\n\u003E Snowflake's [Data Protection Policies](https://docs.snowflake.com/en/user-guide/tag-based-masking-policies) — masking, row access, aggregation, projection — enforce the information classification at the column or row level. Restricted data is typically restricted to a list of named persons, where that list is typically given a domain-specific role and governed in domain-specific manner. Thus, these policies will be created later in the domain-oriented schemas, not in `PLT.GOVERNANCE`.\n\u003E\n\u003E Snowflake does not support future grants for tags — `GRANT APPLY ON TAG` is issued per object immediately after each `CREATE TAG`.\n\n\n### Step 3: Platform Admin Tools Schema\n\nLet's create a schema in the platform database for administration tools, containing helper procedures that we'll later use as the platform administrator, reducing repetition and helping achieve consistency later in our deployment. This is a long block of code, with content that might make more sense later, so don't feel the need to understand every detail.\n\n```sql\n-- Platform Database: Admin Tools ----------------------------------------------\nUSE ROLE PLT_ADMIN;\nUSE WAREHOUSE ADMIN_WH;\n\nCREATE SCHEMA IF NOT EXISTS PLT.ADMIN_TOOLS\n  WITH MANAGED ACCESS\n  COMMENT = 'Common platform administration tools and utilities';\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    SCHEMA_SUBJECT  VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Base helper: creates a managed-access schema, paired _R/_W database roles, and standard privilege grants. Called by zone-specific wrappers.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db        VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema    VARCHAR DEFAULT UPPER(SCHEMA_NAME);\n    v_subject   VARCHAR DEFAULT SCHEMA_SUBJECT;\nBEGIN\n\n    -- Ensure database-wide roles exist\n    EXECUTE IMMEDIATE 'CREATE DATABASE ROLE IF NOT EXISTS ' || v_db || '.DB_R'\n        || ' COMMENT = ''Database-wide read access''';\n    EXECUTE IMMEDIATE 'CREATE DATABASE ROLE IF NOT EXISTS ' || v_db || '.DB_W'\n        || ' COMMENT = ''Database-wide write and create access''';\n\n    -- Create the managed access schema\n    EXECUTE IMMEDIATE\n        'CREATE SCHEMA IF NOT EXISTS ' || v_db || '.' || v_schema\n        || ' WITH MANAGED ACCESS'\n        || ' COMMENT = ''' || REPLACE(v_subject, '''', '''''') || '''';\n\n    -- Create schema-specific database roles\n    EXECUTE IMMEDIATE\n        'CREATE DATABASE ROLE IF NOT EXISTS ' || v_db || '.' || v_schema || '_R'\n        || ' COMMENT = ''Read access to ' || REPLACE(v_subject, '''', '''''') || ' (' || v_schema || ')''';\n    EXECUTE IMMEDIATE\n        'CREATE DATABASE ROLE IF NOT EXISTS ' || v_db || '.' || v_schema || '_W'\n        || ' COMMENT = ''Write and create access to ' || REPLACE(v_subject, '''', '''''') || ' (' || v_schema || ')''';\n\n    -- _R granted to DB_R and to _W (so _W inherits _R)\n    EXECUTE IMMEDIATE 'GRANT DATABASE ROLE ' || v_db || '.' || v_schema || '_R TO DATABASE ROLE ' || v_db || '.DB_R';\n    EXECUTE IMMEDIATE 'GRANT DATABASE ROLE ' || v_db || '.' || v_schema || '_R TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    -- _W granted to DB_W\n    EXECUTE IMMEDIATE 'GRANT DATABASE ROLE ' || v_db || '.' || v_schema || '_W TO DATABASE ROLE ' || v_db || '.DB_W';\n\n    -- Grant USAGE and MONITOR on database and schema to _R\n    EXECUTE IMMEDIATE 'GRANT USAGE, MONITOR ON DATABASE ' || v_db\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT USAGE, MONITOR ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n\n    RETURN 'SUCCESS: Created schema ' || v_db || '.' || v_schema\n        || ' with roles ' || v_schema || '_R, ' || v_schema || '_W.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_LZ_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    SOURCE_SYSTEM   VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Creates a Landing Zone schema and roles for a source system, adding insert-only write and stage/pipe future grants to enforce immutability after initial load.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db     VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema VARCHAR DEFAULT UPPER(SCHEMA_NAME);\nBEGIN\n\n    CALL PLT.ADMIN_TOOLS.CREATE_SCHEMA_AND_ROLES(:v_db, :v_schema, :SOURCE_SYSTEM);\n\n    -- _R: read-only access to landing zone objects\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n\n    -- _W: insert-only write access (LZ data is immutable after initial load)\n    EXECUTE IMMEDIATE 'GRANT INSERT ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT MONITOR, OPERATE ON FUTURE PIPES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT USAGE, READ, WRITE ON FUTURE STAGES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    \n    EXECUTE IMMEDIATE 'GRANT CREATE TABLE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE PIPE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE STAGE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    RETURN 'SUCCESS: Created schema ' || v_db || '.' || v_schema\n        || ' for source system ' || SOURCE_SYSTEM || ' with future grants applied.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_DOMAIN_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    DOMAIN          VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Creates a domain-oriented schema and roles with full analytical object future grants (tables, views, dynamic tables, functions, procedures, streams, tasks, DMFs, and policies). Base procedure for DV and DW zone wrappers.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db     VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema VARCHAR DEFAULT UPPER(SCHEMA_NAME);\nBEGIN\n    \n    CALL PLT.ADMIN_TOOLS.CREATE_SCHEMA_AND_ROLES(:v_db, :v_schema, :DOMAIN);\n\n    EXECUTE IMMEDIATE 'ALTER SCHEMA ' || v_db || '.' || v_schema\n        || ' SET TAG PLT.GOVERNANCE.DOMAIN = ''' || REPLACE(DOMAIN, '''', '''''') || '''';\n\n    -- _R: read access to domain schema objects\n    EXECUTE IMMEDIATE 'GRANT SELECT, REFERENCES ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE DYNAMIC TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE VIEWS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT USAGE ON FUTURE FUNCTIONS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT USAGE ON FUTURE PROCEDURES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n\n    -- _W: write access and pipeline management\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE STREAMS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT MONITOR, OPERATE ON FUTURE TASKS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT MONITOR, OPERATE ON FUTURE DYNAMIC TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    -- _W: schema-level CREATE privileges\n    EXECUTE IMMEDIATE 'GRANT CREATE TABLE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE DYNAMIC TABLE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE VIEW ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE FUNCTION ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE PROCEDURE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE STREAM ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE TASK ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE STAGE ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE DATA METRIC FUNCTION ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE MASKING POLICY ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE ROW ACCESS POLICY ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n    EXECUTE IMMEDIATE 'GRANT CREATE TAG ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    RETURN 'SUCCESS: Created domain schema ' || v_db || '.' || v_schema\n        || ' for ' || DOMAIN || '.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_DV_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    DOMAIN          VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Creates a Data Vault schema and roles for a domain, enforcing insert-only writes to preserve Data Vault immutability.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db     VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema VARCHAR DEFAULT UPPER(SCHEMA_NAME);\nBEGIN\n    \n    CALL PLT.ADMIN_TOOLS.CREATE_DOMAIN_SCHEMA_AND_ROLES(:v_db, :v_schema, :DOMAIN);\n\n    -- _W: insert-only (DV tables are immutable)\n    EXECUTE IMMEDIATE 'GRANT INSERT ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    RETURN 'SUCCESS: Created Data Vault schema ' || v_db || '.' || v_schema\n        || ' for ' || DOMAIN || ' with future grants applied.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE PLT.ADMIN_TOOLS.CREATE_DW_SCHEMA_AND_ROLES(\n    DATABASE_NAME   VARCHAR,\n    SCHEMA_NAME     VARCHAR,\n    DOMAIN          VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nCOMMENT = 'Creates a Data Warehouse schema and roles for a domain, granting full DML write access and semantic view creation for AI-agent consumption.'\nEXECUTE AS CALLER\nAS\n$$\nDECLARE\n    v_db     VARCHAR DEFAULT UPPER(DATABASE_NAME);\n    v_schema VARCHAR DEFAULT UPPER(SCHEMA_NAME);\nBEGIN\n\n    CALL PLT.ADMIN_TOOLS.CREATE_DOMAIN_SCHEMA_AND_ROLES(:v_db, :v_schema, :DOMAIN);\n\n    -- _W: full write access to DW tables\n    EXECUTE IMMEDIATE 'GRANT INSERT, UPDATE, DELETE, TRUNCATE ON FUTURE TABLES IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    -- _R/_W: semantic view access (DW schemas are AI-agent consumable)\n    EXECUTE IMMEDIATE 'GRANT SELECT ON FUTURE SEMANTIC VIEWS IN SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_R';\n    EXECUTE IMMEDIATE 'GRANT CREATE SEMANTIC VIEW ON SCHEMA ' || v_db || '.' || v_schema\n        || ' TO DATABASE ROLE ' || v_db || '.' || v_schema || '_W';\n\n    RETURN 'SUCCESS: Created Data Warehouse schema ' || v_db || '.' || v_schema\n        || ' for ' || DOMAIN || ' with future grants applied.';\nEND;\n$$;\n```\n\n### Step 4: Landing Zone Database\n\nLet's create a database that will serve as our Landing Zone, as well as a role and warehouse designed for ingesting data.\n\n```sql\n-- Development LZ Ingestion Role -----------------------------------------------\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS DEV_LZ_INGEST\n  COMMENT = 'Ingestion role for the development landing zone';\n\nGRANT ROLE DEV_LZ_INGEST TO ROLE SYSADMIN;\n\n-- Development LZ Ingestion Warehouse ------------------------------------------\nUSE ROLE SYSADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS DEV_INGEST_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE\n  COMMENT = 'Warehouse for automated data ingestion workloads';\n\nGRANT ALL PRIVILEGES ON WAREHOUSE DEV_INGEST_WH TO ROLE PLT_ADMIN WITH GRANT OPTION;\nGRANT USAGE, OPERATE ON WAREHOUSE DEV_INGEST_WH TO ROLE DEV_LZ_INGEST;\n\n-- Development LZ Database -----------------------------------------------------\nCREATE DATABASE IF NOT EXISTS DEV_LZ\n  COMMENT = 'Development landing zone for raw data ingestion';\n\nDROP SCHEMA IF EXISTS DEV_LZ.PUBLIC; -- Removing automatically generated schema\n\nGRANT ALL PRIVILEGES ON DATABASE DEV_LZ TO ROLE PLT_ADMIN WITH GRANT OPTION;\n```\n\n\u003E **Development Environment**\n\u003E\n\u003E You may note that this serves as a development environment example. Objects developed here might later be promoted to a TST_LZ for testing, and then a main LZ, for production use. We're leaving those out here for the sake of brevity. However, in those test and production environments, ingestion of data is typically performed by an automated Service User. In the event multiple Service Users will be used to ingest data using multiple solutions, you may wish to create multiple ingestion roles.\n\n### Step 5: Enterprise Memory and Information Delivery Zone Databases\n\nNow, let's create two more databases. The first will serve as our Data Vault, holding staging, raw vault, and business vault objects. The second will serve as our analyst-facing interface to the Information Delivery Zone. We'll also create a QA analyst role and a warehouse for engineering (development and testing) use, and a warehouse for automated data transformation.\n\n```sql\n-- QA (Dev/Test) Analyst Role --------------------------------------------------\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS QA_ANALYST\n  COMMENT = 'General analyst role with read-only access to the information delivery zone';\n\nGRANT ROLE QA_ANALYST TO ROLE PLT_ADMIN;\n\n\n-- Data Transformation and Engineering Warehouses ------------------------------\nUSE ROLE SYSADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS DEV_XFORM_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE\n  COMMENT = 'Warehouse for automated data transformation workloads';\n\nGRANT ALL PRIVILEGES ON WAREHOUSE DEV_XFORM_WH TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\nCREATE WAREHOUSE IF NOT EXISTS ENGINEERING_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE\n  COMMENT = 'Warehouse for general development and testing use';\n\nGRANT ALL PRIVILEGES ON WAREHOUSE ENGINEERING_WH TO ROLE PLT_ADMIN WITH GRANT OPTION;\nGRANT USAGE, OPERATE ON WAREHOUSE ENGINEERING_WH TO ROLE QA_ANALYST;\n\n-- Data Vault Database ---------------------------------------------------------\nCREATE DATABASE IF NOT EXISTS DEV_DV\n  COMMENT = 'Enterprise memory of domain-oriented data vault models';\n\nDROP SCHEMA IF EXISTS DEV_DV.PUBLIC; -- Removing automatically generated schema\n\nGRANT ALL PRIVILEGES ON DATABASE DEV_DV TO ROLE PLT_ADMIN WITH GRANT OPTION;\n\n-- Data Warehouse Database -----------------------------------------------------\nCREATE DATABASE IF NOT EXISTS DEV_DW\n  COMMENT = 'Information delivery of domain-oriented models';\n\nDROP SCHEMA IF EXISTS DEV_DW.PUBLIC; -- Removing automatically generated schema\n\nGRANT ALL PRIVILEGES ON DATABASE DEV_DW TO ROLE PLT_ADMIN WITH GRANT OPTION;\n```\n\n\u003E **Transformation Role and Ingestion Privileges**\n\u003E\n\u003E You may be wondering, why did we not grant privileges to the ingestion role, nor ever create a role for transforming data in the data vault? We will do this later, after the schemas in these databases are in place.\n\n\n\u003C!-- ------------------------ --\u003E\n## Business Architecture, Domains and Ontologies\n\nIt is critical that our Business Architecture inform our Information Architecture. Let's take a moment to think about how Business Architecture might inform these next examples.\n\n### Domains\n\nBusiness architecture can be organized conceptually into a **hierarchy of domains**. Eric Evans, in the book Domain Driven Design, defines a **domain** as, \"a sphere of knowledge, influence, or activity.\" It is important to recognize that, assuming your organization has been performing business activities regularly, these domains already exist. Typically, they can be recognized and diagramed in a hierarchy, where domains can be broken into sub-domains. The **activities** in these domains **originate data** as valuable assets, not waste byproducts.\n\nAlso, a domain's business activities can rarely function without using data originating from other domains as input. For example, the **Finance** activity of accounting for revenue recognition in the prior quarter depends on invoicing information associated with fulfilled orders, that invoice data originating from activities in **Customer Service**. Before being invoiced, those orders were fulfilled by activities in **Manufacturing & Delivery**. Those orders originated with activities in **Sales & Marketing**. Placed but unfulfilled orders, or possibly just a Sales & Marketing expectation of future orders, might inform a Finance domain's Revenue Forecast. The people performing all those activities are all hired, tracked and managed through activities in the **Workforce** domain. And those workers were likely first given an email address through activities the **IT Delivery** domain. The complex web of connections between domains is often difficult, if not impossible, to diagram or comprehend in total. Within the perspective of only a single domain, understanding is more easily achieved.\n\n\u003E Note: The domain examples above are inspired by the [TBM Taxonomy's](https://www.tbmcouncil.org/learn-tbm/resource-center/the-tbm-taxonomy/) Business Layer Solutions Hierarchy.\n\n### Domain Taxonomy and Ontologies\n\nThe business processes, as performed in an organization, along with business keys (how the people performing those activities identify things), units of work that connect business keys, descriptive information produced by activities, what technology systems are used, and input/output connections to other domains, can all be formally described in **ontologies**. These ontologies can be captured in simple text documents or aided by knowledge graph. And because each domain has a web of connections to other domains, not just hierarchical relationships, the domain hierarchy becomes a **domain taxonomy**. When getting started, don't attempt to \"boil the ocean,\" but rather limit scope to a prioritized set of specific business objectives. However, that which is formally documented should be captured accurately, utilizing the domain expertise of those performing these business processes, reviewing with and gaining the formal approval of those who have dominion over the given domain(s). You don't want a documented ontology of the Finance domain that the CFO would not recognize or approve of. Every domain, when formally documented, should have an obvious authority figure, already having dominion over the activities, processes, and business vocabulary used within that domain.\n\nAs an example, we'll consider the following simple domain hierarchy, starting with just two domains, Sales & Marketing and Customer Service.\n\n![Example Domain Hierarchy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/defensible-analytics-using-data-vault-and-snowflake/exampledomains.png?v=7a7bb18b)\n\nWith Snowflake, schema objects -- which include tables, views, stages, file formats, pipes, streams, UDFs, stored procedures, and more -- always exist within a schema. While access control is possible at the schema object level, assigning privileges on each individual object, doing so at that grain quickly becomes tedious and costly to maintain. When objects with common access control objectives are grouped together into schemas, maintaining access controls becomes much easier. Real-world governance is almost always domain-oriented. Thus, we'll use domain-oriented schemas to organize the objects in our Enterprise Memory Zone and beyond, promoting domain-oriented governance.\n\n\n\u003C!-- ------------------------ --\u003E\n## From Logical Business Architecture to Implementation\n\n### Step 6: Sample Source System Schema\n\nIn a real world scenario, because the data in the Landing Zone is source-system-oriented, a schema found in a Landing Zone database should be associated with a source system. For the sake of simplicity, let's create a single schema designed to land ingested sample data from the TPC-H decision support benchmark.\n\n```sql\n-- Landing Zone Schemas --------------------------------------------------------\nUSE ROLE PLT_ADMIN;\nUSE WAREHOUSE ADMIN_WH;\n\nCALL PLT.ADMIN_TOOLS.CREATE_LZ_SCHEMA_AND_ROLES('DEV_LZ', 'TPCH_REF', 'Static Reference Data');\nCALL PLT.ADMIN_TOOLS.CREATE_LZ_SCHEMA_AND_ROLES('DEV_LZ', 'TPCH_CUSTOMER_SYS', 'Customer System');\nCALL PLT.ADMIN_TOOLS.CREATE_LZ_SCHEMA_AND_ROLES('DEV_LZ', 'TPCH_ORDERS_SYS', 'Orders System');\n\nGRANT DATABASE ROLE DEV_LZ.DB_W TO ROLE DEV_LZ_INGEST;\n```\n\n\u003E Note the simplicity of creating the source system schemas, granting write access to the functional role DEV_LZ_INGEST, by utilizing a common stored procedure that creates the managed access schema, access roles, and grants privileges to those roles. When multiple service users are able to write to the landing zone, be sure use specialized functional roles, assigning those the schema-specific write access roles.\n\n### Step 7: Domain-Oriented Schemas\n\nNow that we know our domains, let's create domain-oriented schemas in the DV and DW databases, representing our Enterprise Memory and Information Delivery Zones.\n\n```sql\n-- Enterprise Memory and Information Delivery Schemas --------------------------\nUSE ROLE PLT_ADMIN;\nUSE WAREHOUSE ADMIN_WH;\n\nCALL PLT.ADMIN_TOOLS.CREATE_DV_SCHEMA_AND_ROLES('DEV_DV', 'SALESMKT', 'Sales & Marketing');\nCALL PLT.ADMIN_TOOLS.CREATE_DW_SCHEMA_AND_ROLES('DEV_DW', 'SALESMKT', 'Sales & Marketing');\nCALL PLT.ADMIN_TOOLS.CREATE_DV_SCHEMA_AND_ROLES('DEV_DV', 'CUSTSERV', 'Customer Service');\nCALL PLT.ADMIN_TOOLS.CREATE_DW_SCHEMA_AND_ROLES('DEV_DW', 'CUSTSERV', 'Customer Service');\n```\n\u003E Note that while the stored procedure creates the schemas and access roles, we don't yet have domain-oriented functional roles to which we may grant the access roles.\n\n### Step 8: Domain-Oriented Roles and Privileges\n\nNow, let's create domain-oriented functional roles, and grant access allowing for reading from the landing zone and other domain's data vault objects, but writing only to the domain-specific schemas in the Enterprise Memory and Information Delivery Zones. We grant read access to the entire Landing Zone, because source systems are often not domain specific. A domain-specific Landing Zone database could be used for a set of source systems restricted to just one domain. We grant cross-domain read access to the data vault models to avoid duplication of efforts and promote effective governance, while allowing one domain to leverage curated information from another. We restrict writing to the domain-specific roles, promoting accountability and effective governance over what is built.\n\n```sql\n-- Domain-Oriented Data Engineer Roles -----------------------------------------\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS SALESMKT_ENGINEER\n  COMMENT = 'Data engineering role for the development of Sales & Marketing objects and transformations';\nGRANT ROLE SALESMKT_ENGINEER TO ROLE PLT_ADMIN;\n\nCREATE ROLE IF NOT EXISTS CUSTSERV_ENGINEER\n  COMMENT = 'Data engineering role for the development of Customer Service objects and transformations';\nGRANT ROLE CUSTSERV_ENGINEER TO ROLE PLT_ADMIN;\n\nUSE ROLE ACCOUNTADMIN;\n\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE SALESMKT_ENGINEER;\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE CUSTSERV_ENGINEER;\n\n-- Domain-Oriented Data Engineer Privileges ------------------------------------\nUSE ROLE PLT_ADMIN;\n\nGRANT USAGE, OPERATE ON WAREHOUSE ENGINEERING_WH TO ROLE SALESMKT_ENGINEER;\nGRANT USAGE, OPERATE ON WAREHOUSE DEV_XFORM_WH TO ROLE SALESMKT_ENGINEER;\n\nGRANT DATABASE ROLE DEV_LZ.DB_R TO ROLE SALESMKT_ENGINEER;\nGRANT DATABASE ROLE DEV_DV.SALESMKT_W TO ROLE SALESMKT_ENGINEER;\nGRANT DATABASE ROLE DEV_DV.DB_R TO ROLE SALESMKT_ENGINEER;\nGRANT DATABASE ROLE DEV_DW.SALESMKT_W TO ROLE SALESMKT_ENGINEER;\n\nGRANT USAGE, OPERATE ON WAREHOUSE ENGINEERING_WH TO ROLE CUSTSERV_ENGINEER;\nGRANT USAGE, OPERATE ON WAREHOUSE DEV_XFORM_WH TO ROLE CUSTSERV_ENGINEER;\n\nGRANT DATABASE ROLE DEV_LZ.DB_R TO ROLE CUSTSERV_ENGINEER;\nGRANT DATABASE ROLE DEV_DV.CUSTSERV_W TO ROLE CUSTSERV_ENGINEER;\nGRANT DATABASE ROLE DEV_DV.DB_R TO ROLE CUSTSERV_ENGINEER;\nGRANT DATABASE ROLE DEV_DW.CUSTSERV_W TO ROLE CUSTSERV_ENGINEER;\n\nGRANT DATABASE ROLE DEV_DW.DB_R TO ROLE QA_ANALYST;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nNow that everything has been created successfully, let's check it out! We can use the SQL below, or on the left select the Database Explorer icon. Remember, your selected role will influence what you can see after clicking the refresh button.\n\n```sql\n-- Explore as a Platform Admin -------------------------------------------------\nUSE ROLE PLT_ADMIN;\nUSE SECONDARY ROLES NONE;\nUSE WAREHOUSE ADMIN_WH;\n\nSHOW DATABASES -\u003E\u003E SELECT \"name\", \"comment\" FROM $1 WHERE \"kind\" = 'STANDARD';\n\nSHOW SCHEMAS IN DATABASE DEV_LZ -\u003E\u003E SELECT \"database_name\", \"name\", \"comment\" FROM $1;\n\n-- Explore as a QA Analyst -----------------------------------------------------\nUSE ROLE QA_ANALYST;\nUSE WAREHOUSE ENGINEERING_WH;\n\nSHOW DATABASES -\u003E\u003E SELECT \"name\", \"comment\" FROM $1 WHERE \"kind\" = 'STANDARD';\n\nSHOW SCHEMAS IN DATABASE DEV_DW -\u003E\u003E SELECT \"database_name\", \"name\", \"comment\" FROM $1;\n\nSHOW TAGS IN DATABASE PLT;\n\nSELECT * FROM TABLE(DEV_DW.INFORMATION_SCHEMA.TAG_REFERENCES('DEV_DW.CUSTSERV', 'SCHEMA'));\n\nSELECT * FROM TABLE(DEV_DW.INFORMATION_SCHEMA.TAG_REFERENCES('DEV_DW.SALESMKT', 'SCHEMA'));\n\nSHOW SCHEMAS IN DATABASE DEV_DV; -- This will error, as the role doesn't have access\n\nSHOW SCHEMAS IN DATABASE DEV_LZ; -- This will error, as the role doesn't have access\n\n-- Explore as Dev LZ Ingest ----------------------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\n\nSHOW DATABASES -\u003E\u003E SELECT \"name\", \"comment\" FROM $1 WHERE \"kind\" = 'STANDARD';\n\nSHOW SCHEMAS IN DATABASE DEV_LZ -\u003E\u003E SELECT \"database_name\", \"name\", \"comment\" FROM $1;\n\nSHOW SCHEMAS IN DATABASE DEV_DV; -- This will error, as the role doesn't have access\n```\n\nWe covered some of the basics to get started. As an architect, you may be considering creating test and main production environments; or additional roles, both platform oriented and domain oriented; or additional tags, access policies and restricted information roles; or data metric functions, with a data quality / error mart; or additional common functions and stored procedures; or even Presentation Zone databases for custom customer-facing data shares, Streamlit apps and dashboards, or Agents for use with Snowflake CoWork. We hope we've whet your appetite for more.\n\nYou are now ready to advance to the next guide, [Building a Real-Time Data Vault in Snowflake](https://www.snowflake.com/en/developers/guides/vhol-data-vault/)! Data Vault 2.1 consists of 3 pillars -- methodology, architecture, and model -- and while this guide focuses on architecture, the next guide focuses on modeling. We've updated that guide to leverage the structure defined here.\n\nIf you want to learn more about Data Vault 2.1, check out the latest content from Data Vault Alliance: the [blog, training and certification resources](https://datavaultalliance.com/), the [DVA United](https://www.dvaunited.com/) community, and free content on [YouTube](https://www.youtube.com/@DataVaultAlliance/videos).\n\n### What You Learned\n- How to translate Data Vault's logical architecture zones into Snowflake databases\n- How to get started with a Domain Taxonomy and Ontologies\n- How to create Snowflake schemas for either source systems or domains\n- How to create and leverage a common platform database for shared resources\n\n### What's Next\n- [Building a Real-Time Data Vault in Snowflake](https://www.snowflake.com/en/developers/guides/vhol-data-vault/)\n","title":"Quickstart Article Body",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-da6ad9890b","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":{"id":"container-2dab7c72c9","layout":"SIMPLE",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0985ed5a09","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-07",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-e8b6e47f62","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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