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On the TPC-H sample data that ships with every Snowflake account, five perfectly reasonable definitions give results between \u003Cstrong\u003E$109B and $230B\u003C/strong\u003E:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDefinition\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERevenue (TPC-H SF1)\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGross: \u003Ccode\u003ESUM(L_EXTENDEDPRICE)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$229.6B\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrder header: \u003Ccode\u003ESUM(O_TOTALPRICE)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$226.8B\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENet of discount\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$218.1B\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENet of discount, excluding returns\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$164.4B\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENet of discount, shipped lines only\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$108.9B\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ENobody made an error. Each dashboard, notebook and AI assistant simply encoded its own definition. This is \u003Cstrong\u003Emetric drift\u003C/strong\u003E, and it is one symptom of semantic fragmentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMetric drift\u003C/strong\u003E: inconsistent KPIs across dashboards.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EManual translation\u003C/strong\u003E: the same logic re-typed in every tool, then reconciled.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHallucinations\u003C/strong\u003E: an LLM grounded on conflicting or missing definitions guesses.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIntegration debt\u003C/strong\u003E: N-to-N custom integrations between proprietary semantic layers.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOpen Semantic Interchange (OSI)\u003C/strong\u003E is a vendor-neutral YAML specification for semantic models: datasets, keys, relationships, fields and metrics. The specification and its converters are developed in the open as \u003Ca href=\"https://github.com/apache/ossie\"\u003EApache Ossie\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EBe clear about what OSI is today. It is \u003Cstrong\u003Enot\u003C/strong\u003E a format that every tool reads natively. Snowflake can turn an OSI document directly into a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\"\u003Esemantic view\u003C/a\u003E. Most other tools are reached through open-source converters that translate OSI into their own proprietary format, and those converters are young. The value of OSI right now is architectural: one open definition that you own, version and test, from which every tool-specific format is \u003Cstrong\u003Egenerated\u003C/strong\u003E rather than hand-maintained.\u003C/p\u003E\n","\u003Cp\u003EIn this guide you:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EWrite the revenue definition once, in OSI, and compile it into a Snowflake semantic view that Cortex Analyst can use.\u003C/li\u003E\u003Cli\u003EProve the semantic view returns the right numbers.\u003C/li\u003E\u003Cli\u003EGenerate other tools' formats from the same file, and weigh how the model should bind to the data.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe tools used along the way are examples; the architectural pattern is what matters.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE:\nThe OSI stored procedures used in this guide are in preview. Behaviour and supported spec versions may change. Everything in this guide was tested against OSI spec version \u003Ccode\u003E0.1.1\u003C/code\u003E, the only version the Snowflake procedure accepted at the time of writing.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBasic SQL knowledge\u003C/li\u003E\u003Cli\u003EFamiliarity with YAML\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhy an open semantic standard matters architecturally, and where it stands today\u003C/li\u003E\u003Cli\u003EThe building blocks of an OSI semantic model and why each one matters\u003C/li\u003E\u003Cli\u003EWhich parts of an OSI model are portable and which are Snowflake-specific\u003C/li\u003E\u003Cli\u003EHow to create a semantic view from OSI YAML with \u003Ccode\u003ESYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to test a semantic view against reference SQL\u003C/li\u003E\u003Cli\u003EHow to generate proprietary formats from the model, and how to detect what a conversion loses\u003C/li\u003E\u003Cli\u003EWhen an interface layer of views between tables and model is worth considering, and what it costs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with access to the OSI preview, and a role that can create a database\u003C/li\u003E\u003Cli\u003EPython 3.11+ and \u003Ccode\u003Egit\u003C/code\u003E for the open-source converters\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn OSI model \u003Ccode\u003Etpch-revenue.osi.yaml\u003C/code\u003E with one governed definition of revenue\u003C/li\u003E\u003Cli\u003EA Snowflake semantic view compiled from it, with parity tests\u003C/li\u003E\u003Cli\u003ETwo proprietary formats generated from the same file, and a measured report of what each conversion keeps and loses\u003C/li\u003E\u003Cli\u003EOptionally, an interface layer of views, to compare against binding the model to the tables directly\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWhy an Open Standard\u003C/h2\u003E\n","\u003Cp\u003EBefore writing any YAML, it helps to see the problem the way an enterprise architect does.\u003C/p\u003E\n","\u003Ch3\u003EFrom N-to-N to hub and spoke\u003C/h3\u003E\n","\u003Cp\u003EA typical enterprise already has three to five semantic layers: one in the BI tool, one in the transformation framework, one in the notebook platform, and now one for every AI agent. Without a shared format, each pair of tools that must agree needs its own translation, maintained by hand. Three tools that define metrics and three that consume them already mean nine such translations, and every new tool adds a full row:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-apache-osi/n-to-n-integration.png?v=be5f4e5c\" alt=\"Without a standard, every pair of tools needs its own hand-maintained translation\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWith OSI as the hub, there is one reviewed and tested source. Snowflake compiles it natively into a semantic view, which AI agents and tools that read semantic views use directly. Other tools get their own format generated by a converter, never edited by hand:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-apache-osi/osi-hub-and-spoke.png?v=be5f4e5c\" alt=\"With OSI as the hub, one source of truth compiles to Snowflake and generates every other format\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWith a hub, adding a tool means adding one converter, not N new integrations. Proprietary formats stay, but they become \u003Cstrong\u003Ebuild artifacts\u003C/strong\u003E, like compiled binaries, rather than places where business logic is authored.\u003C/p\u003E\n","\u003Ch3\u003EWhat this buys an enterprise today\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBenefit\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat it means in practice\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EOne point of governance\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERevenue is defined in one reviewed file. A change is a pull request with an owner, a reviewer and an audit trail, not an edit buried in a dashboard.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EVendor optionality\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe definition is not locked inside any one product. \u003Ccode\u003ESYSTEM$READ_OSSIE_YAML_FROM_SEMANTIC_VIEW\u003C/code\u003E exports a Snowflake semantic view back to OSI, so choosing Snowflake does not trap your semantics.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ECheaper migration and onboarding\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExisting models in other semantic layers can be converted to OSI and compiled into Snowflake as a starting point, instead of being rewritten from scratch. Expect to repair what a converter cannot carry.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EConsistent AI grounding\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe same descriptions feed Cortex Analyst, agents and BI tools, so an LLM and a dashboard work from the same definition. How much of the model each AI tool actually uses varies; see the Cortex Analyst section.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ELoss you can measure\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConverters report what a target cannot represent, and parity tests prove the numbers. Drift between tools becomes a CI failure instead of a surprise in a board meeting.\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EHow much of this works today depends on the target; the section \u003Cem\u003EPortable versus Snowflake-specific\u003C/em\u003E covers it once you know the building blocks of a model.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Snowflake\u003C/h2\u003E\n","\u003Cp\u003ECreate a database for the semantic view and make sure the TPC-H sample data is available. Some accounts do not have \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA\u003C/code\u003E mounted; the second statement fixes that.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS OSI_GUIDE;\nCREATE SCHEMA IF NOT EXISTS OSI_GUIDE.TPCH;\n\nCREATE DATABASE IF NOT EXISTS SNOWFLAKE_SAMPLE_DATA FROM SHARE SFC_SAMPLES.SAMPLE_DATA;\n\nSELECT COUNT(*) FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM;  -- 6,001,215\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReproduce the drift for yourself:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  (SELECT SUM(O_TOTALPRICE) FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS) AS order_total_price,\n  SUM(L_EXTENDEDPRICE)                                                   AS gross,\n  SUM(L_EXTENDEDPRICE * (1 - L_DISCOUNT))                                AS net,\n  SUM(IFF(L_RETURNFLAG <> 'R', L_EXTENDEDPRICE * (1 - L_DISCOUNT), 0))   AS net_excl_returns,\n  SUM(IFF(L_LINESTATUS = 'F',  L_EXTENDEDPRICE * (1 - L_DISCOUNT), 0))   AS net_shipped_only\nFROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice that the gap between "net" and "net, shipped only" comes from a \u003Cstrong\u003Efilter\u003C/strong\u003E, not a formula. A metric definition is the expression \u003Cem\u003Eand\u003C/em\u003E its filters \u003Cem\u003Eand\u003C/em\u003E its grain. That is exactly what we will pin down in OSI.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAnatomy of a Model\u003C/h2\u003E\n","\u003Cp\u003EAn OSI document describes one semantic model. Its building blocks map closely to how analysts already think:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBlock\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat it says\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy it matters for drift\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edatasets\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA logical table and its physical \u003Ccode\u003Esource\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEveryone starts from the same tables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eprimary_key\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhat makes a row unique, i.e. the grain\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELets engines aggregate at the right level\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erelationships\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMany-to-one joins between datasets\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPrevents fan-out double counting\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Efields\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERow-level columns or expressions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EShared building blocks such as \u003Ccode\u003Enet_amount_kept\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Emetrics\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAggregate expressions across datasets\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe single definition of \u003Ccode\u003Enet_revenue\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edescription\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHuman-readable meaning\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBecomes native comments in Snowflake, which Cortex Analyst reads\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eai_context\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExtra context intended for AI tools\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPortable hints for other tools; see the Cortex Analyst section for how Snowflake treats it today\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustom_extensions\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVendor-specific metadata\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EKeeps tool-specific settings out of the core\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EWhy keys and relationships are not optional\u003C/h3\u003E\n","\u003Cp\u003EJoin order lines to orders and sum the order header amount, a mistake that is easy to make in a notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SUM(o.O_TOTALPRICE) AS naive_joined\nFROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS o\nJOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM l ON l.L_ORDERKEY = o.O_ORDERKEY;\n-- 1,134 billion: five times the real 226.8 billion\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEvery order is counted once per line. Because the OSI model declares \u003Ccode\u003Eprimary_key\u003C/code\u003E on each dataset and the direction of each relationship (\u003Ccode\u003Efrom\u003C/code\u003E the many side \u003Ccode\u003Eto\u003C/code\u003E the one side), a semantic engine knows \u003Ccode\u003Eorders\u003C/code\u003E is at a coarser grain than \u003Ccode\u003Elineitem\u003C/code\u003E and aggregates each metric at its own grain.\u003C/p\u003E\n","\u003Ch3\u003EDecide the definition, then write it down\u003C/h3\u003E\n","\u003Cp\u003EFor this guide, the business agrees that \u003Cstrong\u003Erevenue is extended price after discount, excluding returned lines, before tax\u003C/strong\u003E. We encode it as a field plus a metric:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003E    - name: net_amount_kept\n      datatype: Decimal\n      expression: {dialects: [{dialect: ANSI_SQL, expression: "CASE WHEN L_RETURNFLAG <> 'R' THEN L_EXTENDEDPRICE * (1 - L_DISCOUNT) ELSE 0 END"}]}\n      description: >\n        Revenue per order line, the company definition: amount after discount,\n        before tax, zero for returned lines. Also called net sales. Sum it for revenue.\n...\n  metrics:\n  - name: net_revenue\n    datatype: Decimal\n    expression: {dialects: [{dialect: ANSI_SQL, expression: "SUM(lineitem.net_amount_kept)"}]}\n    description: >\n      Revenue, the company definition: extended price after discount, excluding\n      returned lines, before tax. Also called sales or net sales. Use this metric\n      whenever a question asks for revenue or sales.\n    ai_context: "Synonyms: revenue, sales, net sales"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe filter lives in the field expression, so every consumer inherits it. \u003Ccode\u003EO_TOTALPRICE\u003C/code\u003E stays in the model as \u003Ccode\u003Etotal_price\u003C/code\u003E, but its description says explicitly: "Not revenue: never use it for revenue or sales questions."\u003C/p\u003E\n","\u003Cp\u003ENotice that the business meaning, including synonyms such as \u003Cem\u003Enet sales\u003C/em\u003E, is written into \u003Ccode\u003Edescription\u003C/code\u003E, on the field as well as the metric. The same synonyms also appear in \u003Ccode\u003Eai_context\u003C/code\u003E, a field intended for AI tools. The section on Cortex Analyst explains why the model states things twice: in the current preview, Snowflake only acts on the \u003Ccode\u003Edescription\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EPortable versus Snowflake-specific\u003C/h3\u003E\n","\u003Cp\u003ETwo questions decide how portable a model is: which parts of the document are standard, and which tools can consume it. Within the document:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPart of the model\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPortable?\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENotes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatasets, keys, relationships, fields, metrics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECore OSI specification\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExpressions in \u003Ccode\u003EANSI_SQL\u003C/code\u003E dialect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUse where possible; the converters tested here read them\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExpressions in \u003Ccode\u003ESNOWFLAKE\u003C/code\u003E dialect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake only\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUse for Snowflake functions; add an \u003Ccode\u003EANSI_SQL\u003C/code\u003E variant for other tools\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esource: DB.SCHEMA.TABLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPartly\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAny tool that connects to the same Snowflake tables can reuse it\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustom_extensions\u003C/code\u003E with \u003Ccode\u003Evendor_name: SNOWFLAKE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake only\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIgnored by other tools\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EAnd on the consuming side, at the time of writing (verified while building this guide):\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETarget\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EHow it consumes the model\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStatus\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake semantic views\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML\u003C/code\u003E, natively\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPreview; OSI \u003Ccode\u003E0.1.1\u003C/code\u003E only\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake export\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESYSTEM$READ_OSSIE_YAML_FROM_SEMANTIC_VIEW\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPreview; \u003Ccode\u003Edatatype\u003C/code\u003E values not exported\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOther tools' semantic formats\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpen-source converters in the Apache Ossie repository (Hex, dbt MetricFlow, Cube, Sigma, Power BI, GoodData, Honeydew and others)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConverters, not native import; two are tested in this guide\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EMost third-party tools do not import OSI directly today. They read their own format, and some read Snowflake semantic views natively. The converters target the in-development spec \u003Ccode\u003E0.2.0.dev0\u003C/code\u003E and were not yet on PyPI. Treat them as early tooling: useful, inspectable, and worth testing before you trust them.\u003C/p\u003E\n","\u003Cp\u003ETwo practical rules that we learned while testing this guide:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EGive every field a \u003Ccode\u003Edatatype\u003C/code\u003E.\u003C/strong\u003E Snowflake can infer types from the source table, but a converter generating another tool's format cannot. Without it, one of the converters tested later typed prices as \u003Ccode\u003Estring\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWrite \u003Ccode\u003Eai_context\u003C/code\u003E as a string.\u003C/strong\u003E The spec also allows an object (\u003Ccode\u003Einstructions\u003C/code\u003E, \u003Ccode\u003Esynonyms\u003C/code\u003E, \u003Ccode\u003Eexamples\u003C/code\u003E), but the Snowflake procedure currently rejects the object form.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy the Semantic View\u003C/h2\u003E\n","\u003Cp\u003EThe complete model is in \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-apache-osi/assets/tpch-revenue.osi.yaml\"\u003E\u003Ccode\u003Eassets/tpch-revenue.osi.yaml\u003C/code\u003E\u003C/a\u003E. Pass it to the procedure, together with the schema that will hold the semantic view. The semantic view gets the model's \u003Ccode\u003Ename\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML('OSI_GUIDE.TPCH', $$\n-- paste the full contents of assets/tpch-revenue.osi.yaml here\n$$);\n-- Semantic view was successfully created from OSI YAML.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E$$\u003C/code\u003E delimiters let you paste the YAML as-is, without escaping quotes. Most of the file repeats the patterns from the previous section. The parts worth reading closely are the keys and relationships, which give the semantic view its grain and join paths:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003E  - name: lineitem\n    source: SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM\n    primary_key: [l_orderkey, l_linenumber]\n...\n  relationships:\n  - {name: lineitem_to_orders, from: lineitem, to: orders, from_columns: [l_orderkey], to_columns: [o_orderkey]}\n  - {name: orders_to_customer, from: orders, to: customer, from_columns: [o_custkey], to_columns: [c_custkey]}\n  - {name: customer_to_nation, from: customer, to: nation, from_columns: [c_nationkey], to_columns: [n_nationkey]}\n  - {name: nation_to_region, from: nation, to: region, from_columns: [n_regionkey], to_columns: [r_regionkey]}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that \u003Ccode\u003Esource\u003C/code\u003E points straight at the shared sample database; the tables do not have to live in the same schema as the semantic view.\u003C/p\u003E\n","\u003Cp\u003EQuery it like any semantic view:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT *\nFROM SEMANTIC_VIEW(\n  OSI_GUIDE.TPCH.TPCH_REVENUE\n  METRICS net_revenue, order_count, average_order_value\n  DIMENSIONS region.region_name\n)\nORDER BY net_revenue DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Eregion\u003C/code\u003E dimension sits three relationships away from \u003Ccode\u003Elineitem\u003C/code\u003E (lineitem → orders → customer → nation → region). The semantic view resolves those joins from the model; nobody writes them again.\u003C/p\u003E\n","\u003Ch3\u003ERead the model back\u003C/h3\u003E\n","\u003Cp\u003EThe conversion also works in the other direction. This returns OSI YAML for any semantic view, including ones you built without OSI:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$READ_OSSIE_YAML_FROM_SEMANTIC_VIEW('OSI_GUIDE.TPCH.TPCH_REVENUE');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe output uses upper-case names and adds \u003Ccode\u003ESNOWFLAKE\u003C/code\u003E vendor extensions such as \u003Ccode\u003Eaccess_modifier\u003C/code\u003E. Keys, relationships and descriptions are exported from native semantic view properties. \u003Ccode\u003Eai_context\u003C/code\u003E comes back too, but only because it was stored verbatim in the \u003Ccode\u003EOSI\u003C/code\u003E extension; the OSI \u003Ccode\u003Edatatype\u003C/code\u003E values are not exported. This export is what keeps Snowflake from becoming a lock-in point: any semantic view, including ones built without OSI, can leave as an open document. Keep the hand-written YAML in Git as the source of truth and treat the read-back as an export.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EProve Zero Drift\u003C/h2\u003E\n","\u003Cp\u003EA shared definition is only useful if it is \u003Cem\u003Ecorrect\u003C/em\u003E. Write each business question twice, once against the semantic view and once as independent reference SQL over the raw tables, and require zero differing rows.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH\nsv_total   AS (SELECT ROUND(net_revenue, 2) v\n               FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_REVENUE METRICS net_revenue)),\nref_total  AS (SELECT ROUND(SUM(IFF(L_RETURNFLAG <> 'R', L_EXTENDEDPRICE * (1 - L_DISCOUNT), 0)), 2) v\n               FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM),\n\nsv_region  AS (SELECT region_name k, ROUND(net_revenue, 2) v\n               FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_REVENUE METRICS net_revenue DIMENSIONS region.region_name)),\nref_region AS (SELECT r.R_NAME k, ROUND(SUM(IFF(l.L_RETURNFLAG <> 'R', l.L_EXTENDEDPRICE * (1 - l.L_DISCOUNT), 0)), 2) v\n               FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM l\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS   o ON o.O_ORDERKEY  = l.L_ORDERKEY\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER c ON c.C_CUSTKEY   = o.O_CUSTKEY\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.NATION   n ON n.N_NATIONKEY = c.C_NATIONKEY\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.REGION   r ON r.R_REGIONKEY = n.N_REGIONKEY\n               GROUP BY 1),\n\nsv_aov     AS (SELECT market_segment k, ROUND(average_order_value, 4) v\n               FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_REVENUE METRICS average_order_value DIMENSIONS customer.market_segment)),\nref_aov    AS (SELECT c.C_MKTSEGMENT k,\n                      ROUND(SUM(IFF(l.L_RETURNFLAG <> 'R', l.L_EXTENDEDPRICE * (1 - l.L_DISCOUNT), 0))\n                            / COUNT(DISTINCT l.L_ORDERKEY), 4) v\n               FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM l\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS   o ON o.O_ORDERKEY = l.L_ORDERKEY\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER c ON c.C_CUSTKEY  = o.O_CUSTKEY\n               GROUP BY 1),\n\nsv_year    AS (SELECT order_year k, order_count v\n               FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_REVENUE METRICS order_count DIMENSIONS orders.order_year)),\nref_year   AS (SELECT YEAR(O_ORDERDATE) k, COUNT(*) v\n               FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS GROUP BY 1)\n\nSELECT 'total' AS test,\n       (SELECT COUNT(*) FROM (SELECT * FROM sv_total EXCEPT SELECT * FROM ref_total)) AS diffs\nUNION ALL SELECT 'by_region',\n       (SELECT COUNT(*) FROM ((SELECT * FROM sv_region EXCEPT SELECT * FROM ref_region)\n                    UNION ALL (SELECT * FROM ref_region EXCEPT SELECT * FROM sv_region)))\nUNION ALL SELECT 'aov_by_segment',\n       (SELECT COUNT(*) FROM ((SELECT * FROM sv_aov EXCEPT SELECT * FROM ref_aov)\n                    UNION ALL (SELECT * FROM ref_aov EXCEPT SELECT * FROM sv_aov)))\nUNION ALL SELECT 'orders_by_year',\n       (SELECT COUNT(*) FROM ((SELECT * FROM sv_year EXCEPT SELECT * FROM ref_year)\n                    UNION ALL (SELECT * FROM ref_year EXCEPT SELECT * FROM sv_year)));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll four tests should return \u003Ccode\u003Ediffs = 0\u003C/code\u003E. The \u003Ccode\u003Eorders_by_year\u003C/code\u003E test is the fan-out check: \u003Ccode\u003Eorder_count\u003C/code\u003E is defined on \u003Ccode\u003Eorders\u003C/code\u003E, and stays correct even though revenue is measured on \u003Ccode\u003Elineitem\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ERun these tests every time the model changes, for example in a CI pipeline, and a definition can no longer drift silently.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGround Cortex Analyst\u003C/h2\u003E\n","\u003Cp\u003EThe OSI procedure turns \u003Ccode\u003Edescription\u003C/code\u003E into native \u003Ccode\u003ECOMMENT\u003C/code\u003Es on the semantic view, its facts, dimensions and metrics, and Cortex Analyst reads those. Ask a plain-language question in Snowsight (\u003Cstrong\u003EAI & ML » Cortex Analyst\u003C/strong\u003E, select \u003Ccode\u003EOSI_GUIDE.TPCH.TPCH_REVENUE\u003C/code\u003E) or through a Cortex Agent that uses the semantic view as a tool:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EWhat was total revenue by region in 1997?\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECortex Analyst generates:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT *\nFROM SEMANTIC_VIEW(\n    OSI_GUIDE.TPCH.TPCH_REVENUE\n    DIMENSIONS region.region_name\n    METRICS NET_REVENUE\n    WHERE orders.order_year = 1997\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIt picked \u003Ccode\u003Enet_revenue\u003C/code\u003E, not the tempting \u003Ccode\u003EO_TOTALPRICE\u003C/code\u003E column. The AI assistant now reports the same number as the finance dashboard. The names and descriptions in the model did that work, not prompt engineering: the metric is called \u003Ccode\u003Enet_revenue\u003C/code\u003E, its basis \u003Ccode\u003Enet_amount_kept\u003C/code\u003E is described as the company definition of revenue, and \u003Ccode\u003Etotal_price\u003C/code\u003E is described as "Not revenue".\u003C/p\u003E\n","\u003Cp\u003EThe synonyms written into the descriptions work too:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestion\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECortex Analyst chose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EShow net sales per country by booking year\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ENET_REVENUE\u003C/code\u003E by \u003Ccode\u003Enation.nation_name\u003C/code\u003E and \u003Ccode\u003Eorders.order_year\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhat is the AOV per customer segment?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EAVERAGE_ORDER_VALUE\u003C/code\u003E by \u003Ccode\u003Ecustomer.market_segment\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EIn the current preview, only \u003Ccode\u003Edescription\u003C/code\u003E grounds Cortex Analyst. It becomes a native \u003Ccode\u003ECOMMENT\u003C/code\u003E, and it works best on the field Analyst has to choose between. \u003Ccode\u003Eai_context\u003C/code\u003E is kept only in the \u003Ccode\u003EOSI\u003C/code\u003E extension of the semantic view for export, and Cortex Analyst ignores it. Put anything an AI tool must respect in \u003Ccode\u003Edescription\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE:\nThis is the behaviour of the OSI procedure and Cortex Analyst as tested during the preview, with OSI spec version \u003Ccode\u003E0.1.1\u003C/code\u003E. How \u003Ccode\u003Eai_context\u003C/code\u003E is mapped into semantic views, for example into synonyms or custom instructions, may change in future versions. Re-test before you rely on it.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate Target Formats\u003C/h2\u003E\n","\u003Cp\u003EThe Snowflake semantic view is one target compiled from the OSI file. Other tools have their own proprietary semantic formats. In the hub-and-spoke architecture you never author those formats by hand: you \u003Cstrong\u003Egenerate\u003C/strong\u003E them from the OSI source, the way a compiler produces binaries for different platforms.\u003C/p\u003E\n","\u003Cp\u003EThis guide uses two of the open-source converters in the Apache Ossie repository as examples: Hex semantic project YAML and dbt MetricFlow. They represent two common kinds of target, a BI tool's semantic layer and a transformation framework's metric layer. The pattern is the same for any other converter. How each tool then loads its generated files is that tool's own concern and outside the scope of this guide.\u003C/p\u003E\n","\u003Ch3\u003EInstall the converters\u003C/h3\u003E\n","\u003Cp\u003EAt the time of writing, the converters and their core library are not on PyPI. Install them from the repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone --depth 1 https://github.com/apache/ossie.git\npython3 -m venv .venv && source .venv/bin/activate\npip install ./ossie/python ./ossie/converters/hex ./ossie/converters/dbt\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBridge the spec version\u003C/h3\u003E\n","\u003Cp\u003EThe converters expect the in-development spec \u003Ccode\u003E0.2.0.dev0\u003C/code\u003E, which drops the \u003Ccode\u003Esemantic_model:\u003C/code\u003E list and puts the model at the document root. The Snowflake procedure only accepts \u003Ccode\u003E0.1.1\u003C/code\u003E. The content is identical, so generate the newer shape from your single source file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython - <<'EOF'\nimport yaml\nmodel = yaml.safe_load(open("tpch-revenue.osi.yaml"))["semantic_model"][0]\nyaml.safe_dump({"version": "0.2.0.dev0", **model},\n               open("tpch-revenue.osi.v02.yaml", "w"), sort_keys=False)\nEOF\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is itself an example of the pattern: one hand-edited file, other shapes generated in the build. When Snowflake supports the newer version, the step disappears.\u003C/p\u003E\n","\u003Ch3\u003EGenerate\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eossie-hex export     -i tpch-revenue.osi.v02.yaml -o hex_project -d ansi_sql -v\nossie-dbt ossie-to-msi -i tpch-revenue.osi.v02.yaml -o semantic_manifest.json\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E-d ansi_sql\u003C/code\u003E flag selects the expression dialect, which is why the model uses \u003Ccode\u003EANSI_SQL\u003C/code\u003E: portable expressions are what let one source feed many targets. Where a conversion succeeds, the business logic carries over unchanged. In the generated Hex YAML, for example:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Emeasures:\n- id: net_revenue\n  func_sql: SUM(${net_amount_kept})\nrelations:\n- id: lineitem_to_orders\n  target: orders\n  type: many_to_one\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESame table, same filter, same metric, same join direction. Whether a conversion fully succeeds is the subject of the next section.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMeasure What Conversion Loses\u003C/h2\u003E\n","\u003Cp\u003EAn open standard does not make tools identical. Each proprietary format can express some things and not others. The architectural value of OSI is that those gaps become \u003Cstrong\u003Eexplicit and testable\u003C/strong\u003E instead of silent. This section measures them for the two example targets.\u003C/p\u003E\n","\u003Ch3\u003EA BI semantic layer: warnings, not silent drops\u003C/h3\u003E\n","\u003Cp\u003EThe Hex export above reports:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EEncountered 10 problems: 10 warnings.\n   7× `ai_context` — AI context is not preserved in Hex and was dropped.\n   1× `Dataset.primary_key` — Composite primary keys are not supported in Hex and were dropped.\n   1× `Field.is_time` — Temporal role markers are not supported in Hex and were dropped.\n   1× `description` — Project descriptions are not supported in Hex and were dropped.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe metrics and joins survive; the AI context and the composite key on \u003Ccode\u003Elineitem\u003C/code\u003E do not. An earlier draft of this model had no \u003Ccode\u003Edatatype\u003C/code\u003E on most fields and produced 23 extra warnings, with prices typed as \u003Ccode\u003Estring\u003C/code\u003E in the target. Adding types fixed that.\u003C/p\u003E\n","\u003Ch3\u003EA metric layer: a round trip\u003C/h3\u003E\n","\u003Cp\u003EMany enterprises already have metrics defined in another tool, and the most valuable use of a standard is to bring them over instead of rewriting them. dbt MetricFlow is a good test case. Converting to its format:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eossie-dbt ossie-to-msi -i tpch-revenue.osi.v02.yaml -o semantic_manifest.json\n# ValueError: Dataset 'lineitem' has composite primary key ['l_orderkey', 'l_linenumber'];\n# MetricFlow entities cannot represent composite keys losslessly\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe converter refuses rather than producing a subtly wrong model. That is the right behaviour: the key defines the grain of \u003Ccode\u003Elineitem\u003C/code\u003E, and the grain is what protects against fan-out. Removing the key would make the error go away by throwing that protection away. The proper fix is a single-column key, which is what the interface layer in the next section provides. Only to continue this experiment, a throwaway copy with the \u003Ccode\u003Eprimary_key\u003C/code\u003E removed converts successfully. The converter even turns \u003Ccode\u003Eaverage_order_value\u003C/code\u003E into a proper MetricFlow ratio metric. Do not do this in a real model.\u003C/p\u003E\n","\u003Cp\u003ENow simulate the migration direction: take that manifest as if it were an existing project, convert it back to OSI and compile it into Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eossie-dbt msi-to-ossie -i semantic_manifest.json -o from_dbt.yaml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWrap \u003Ccode\u003Efrom_dbt.yaml\u003C/code\u003E in the \u003Ccode\u003E0.1.1\u003C/code\u003E shape, name the model \u003Ccode\u003Etpch_from_dbt\u003C/code\u003E and deploy it with \u003Ccode\u003ESYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML\u003C/code\u003E. Then run two queries:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT net_revenue FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_FROM_DBT METRICS net_revenue);\n-- 164,360,931,200.40: identical to the original\n\nSELECT * FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_FROM_DBT\n  METRICS net_revenue DIMENSIONS region.region_name);\n-- SQL compilation error: The entities 'REGION' and 'LINEITEM' are not related.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe metric definition and its descriptions came through intact. All four relationships, the \u003Ccode\u003Edatatype\u003C/code\u003E values and the \u003Ccode\u003Eai_context\u003C/code\u003E were lost. MetricFlow expresses joins through shared entity names, and the converter names each entity after its own key column (\u003Ccode\u003El_orderkey\u003C/code\u003E on one side, \u003Ccode\u003Eo_orderkey\u003C/code\u003E on the other), so nothing matches. Snowflake then refuses the cross-dataset query instead of returning a wrong number.\u003C/p\u003E\n","\u003Ch3\u003EWhat survived where\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EModel element\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESnowflake semantic view (and its OSI read-back)\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBI semantic layer (\u003Ccode\u003Eossie-hex\u003C/code\u003E)\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMetric layer round trip (\u003Ccode\u003Eossie-dbt\u003C/code\u003E)\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMetrics and expressions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EField filters (\u003Ccode\u003Enet_amount_kept\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDescriptions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMostly\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERelationships\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENo\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EComposite primary key\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDropped, with warning\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERefused\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edatatype\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUsed, but not in the read-back export\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENo\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eai_context\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStored in the \u003Ccode\u003EOSI\u003C/code\u003E extension only; not used by Cortex Analyst in our test\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDropped, with warning\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENo\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EThe architectural lesson\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EKeep OSI as the source and never edit generated formats.\u003C/strong\u003E Losses then happen in one place, in one direction, and can be fixed by improving a converter or the model, not by patching every tool.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETest every target, not just the source.\u003C/strong\u003E The same parity queries that validated \u003Ccode\u003ETPCH_REVENUE\u003C/code\u003E would have failed on \u003Ccode\u003ETPCH_FROM_DBT\u003C/code\u003E immediately.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EModel for portability.\u003C/strong\u003E Put filters in field expressions, give every field a \u003Ccode\u003Edatatype\u003C/code\u003E, write expressions in \u003Ccode\u003EANSI_SQL\u003C/code\u003E, prefer single-column keys where the grain allows it, and put meaning in \u003Ccode\u003Edescription\u003C/code\u003E rather than only in \u003Ccode\u003Eai_context\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPrefer native paths where they exist.\u003C/strong\u003E Compiling OSI into a Snowflake semantic view and letting tools read that view natively avoids a converter altogether. Several BI tools integrate with Snowflake semantic views directly.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EMany of the losses above trace back to how the model is bound to the data, not to the standard itself. The model points straight at physical tables, with their composite keys and inconsistent column names. That leaves an uncomfortable choice: keep the composite key and lose the conversion, or drop the key and lose the grain. The next section explores a design pattern that removes the dilemma.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConsider an Interface Layer\u003C/h2\u003E\n","\u003Cp\u003ESo far the OSI model has read directly from the data layer: \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA.TPCH_SF1\u003C/code\u003E. That is a perfectly valid way to work, and Snowflake semantic views accept tables and views alike. This section explores an alternative worth considering: a thin layer of views between the physical tables and the semantic model, so that the model binds to a stable interface instead of directly to storage.\u003C/p\u003E\n","\u003Cp\u003EThis is a general architectural design pattern, not a Snowflake recommendation. Whether it is worth it depends on your situation. The case for it on this data set is concrete, though. The composite key on \u003Ccode\u003ELINEITEM\u003C/code\u003E forced a choice between correctness and portability. A view that exposes a single-column key gives you both: the grain stays declared, and the conversion succeeds. The section shows what it changes, then weighs the benefits against the downsides.\u003C/p\u003E\n","\u003Ch3\u003EWhere the pattern comes from\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELogical data independence.\u003C/strong\u003E The ANSI/SPARC three-schema architecture separates the physical schema from the external schemas that consumers see. Consumers survive physical changes, such as renamed columns, split tables or a new source system, because only the mapping changes. A semantic model is such a consumer.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Edbt layering.\u003C/strong\u003E dbt's recommended project structure goes from sources to staging to marts, and MetricFlow semantic models are defined on models, not on raw sources.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDimensional modelling.\u003C/strong\u003E Kimball's presentation layer gives BI tools a curated star schema rather than operational tables.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EBuild the views\u003C/h3\u003E\n","\u003Cp\u003ERun \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-apache-osi/assets/interface-views.sql\"\u003E\u003Ccode\u003Eassets/interface-views.sql\u003C/code\u003E\u003C/a\u003E. It creates five views in \u003Ccode\u003EOSI_GUIDE.TPCH\u003C/code\u003E, next to the semantic views, which keeps grants for the domain in one schema. The views only rename, key and type the data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE VIEW OSI_GUIDE.TPCH.ORDER_LINE AS\nSELECT L_ORDERKEY || '-' || L_LINENUMBER AS order_line_key,   -- single-column surrogate key\n       L_ORDERKEY      AS order_key,\n       L_RETURNFLAG    AS return_flag,\n       L_EXTENDEDPRICE AS extended_price,\n       L_DISCOUNT      AS discount,\n       ...\nFROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM;\n\nCREATE OR REPLACE VIEW OSI_GUIDE.TPCH.SALES_ORDER AS\nSELECT O_ORDERKEY AS order_key,       -- same name as in ORDER_LINE\n       O_CUSTKEY  AS customer_key,\n       ...\nFROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETwo design rules matter:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EA key has the same name everywhere it appears.\u003C/strong\u003E \u003Ccode\u003Eorder_key\u003C/code\u003E in \u003Ccode\u003EORDER_LINE\u003C/code\u003E and \u003Ccode\u003ESALES_ORDER\u003C/code\u003E, \u003Ccode\u003Ecustomer_key\u003C/code\u003E in \u003Ccode\u003ESALES_ORDER\u003C/code\u003E and \u003Ccode\u003ECUSTOMER\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEvery dataset has a single-column primary key.\u003C/strong\u003E \u003Ccode\u003Eorder_line_key\u003C/code\u003E replaces the composite \u003Ccode\u003E(L_ORDERKEY, L_LINENUMBER)\u003C/code\u003E. The script checks that it is unique across all 6,001,215 rows.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe business rule, excluding returned lines, deliberately stays in the OSI model and not in the view. Views express how data is stored; the model expresses what the business means. Hiding a definition in SQL would make it invisible to every other tool.\u003C/p\u003E\n","\u003Ch3\u003EPoint the model at the views\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-apache-osi/assets/tpch-revenue-if.osi.yaml\"\u003E\u003Ccode\u003Eassets/tpch-revenue-if.osi.yaml\u003C/code\u003E\u003C/a\u003E is the same model bound to the views. Only the \u003Ccode\u003Esource\u003C/code\u003E lines, column names and keys change:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003E  - name: order_line\n    source: OSI_GUIDE.TPCH.ORDER_LINE\n    primary_key: [order_line_key]\n...\n  relationships:\n  - {name: order_line_to_sales_order, from: order_line, to: sales_order, from_columns: [order_key], to_columns: [order_key]}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDeploy it as \u003Ccode\u003ETPCH_REVENUE_IF\u003C/code\u003E with \u003Ccode\u003ESYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML\u003C/code\u003E, then run the parity tests against \u003Ccode\u003EOSI_GUIDE.TPCH.TPCH_REVENUE_IF\u003C/code\u003E, with the reference SQL still on the raw tables. All four return \u003Ccode\u003Ediffs = 0\u003C/code\u003E, so the views add no drift.\u003C/p\u003E\n","\u003Ch3\u003ERerun the conversions\u003C/h3\u003E\n","\u003Cp\u003EGenerate the \u003Ccode\u003E0.2.0.dev0\u003C/code\u003E copy and run both converters again:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eossie-hex export -i tpch-revenue-if.osi.v02.yaml -o hex_project_if -d ansi_sql -v\n# Encountered 9 problems: 9 warnings.   (composite key warning is gone)\n\nossie-dbt ossie-to-msi -i tpch-revenue-if.osi.v02.yaml -o semantic_manifest.json\n# Written to semantic_manifest.json      (no manual edit needed)\nossie-dbt msi-to-ossie -i semantic_manifest.json -o from_dbt.yaml --model-name tpch_if_from_dbt\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the MetricFlow manifest, the entities now line up: \u003Ccode\u003Eorder_key\u003C/code\u003E is a foreign entity on \u003Ccode\u003Eorder_line\u003C/code\u003E and the primary entity on \u003Ccode\u003Esales_order\u003C/code\u003E, so MetricFlow can join them. Converted back to OSI, the model has all four relationships and five primary keys. Deploy it as \u003Ccode\u003ETPCH_IF_FROM_DBT\u003C/code\u003E and run the parity tests: \u003Cstrong\u003Eall four pass\u003C/strong\u003E, including revenue by region, which failed before.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EResult\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDirect on tables\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EThrough interface views\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBI semantic layer export warnings\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMetric layer export\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERefused: composite key\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESucceeds unchanged\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERelationships after round trip\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0 of 4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4 of 4\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EParity tests after round trip\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECross-dataset queries fail\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4 of 4 pass\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edatatype\u003C/code\u003E and \u003Ccode\u003Eai_context\u003C/code\u003E after round trip\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELost\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStill lost\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ERead the table as the answer to the dilemma from the previous section. Nothing in the OSI model was weakened to get there. The grain is still declared and the business rule is unchanged. Only the binding to the data changed, and the generated model went from unusable for cross-dataset questions to passing every parity test.\u003C/p\u003E\n","\u003Cp\u003EThe last row is the honest limit. Views fix structural problems such as keys, names and grain. They cannot make a target format carry metadata it has no slot for. That is a converter or target limitation, so keep catching it in the pipeline.\u003C/p\u003E\n","\u003Ch3\u003EBenefits and downsides\u003C/h3\u003E\n","\u003Cp\u003EBenefits:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECorrectness and portability together.\u003C/strong\u003E A single-column key keeps the grain declared and is something every target format can represent. Without the views, this data set forces you to give up one or the other.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPortability.\u003C/strong\u003E Consistent key names are what let the relationships survive the round trip. Other tools can bind to the same view names.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInsulation from physical change.\u003C/strong\u003E A renamed column, a split table or a move to SF10 becomes a change to the views, not to the OSI file, its tests or any generated target.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReuse across sources.\u003C/strong\u003E Two systems holding the same concepts can each get their own views exposing the same names, and share one model.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA clear split of concerns.\u003C/strong\u003E Views describe how data is stored; the OSI model describes what the business means.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDownsides:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMore objects to own.\u003C/strong\u003E The views need the same versioning, review and deployment as the model. Without that discipline they become one more place where logic drifts.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnother place to hide logic.\u003C/strong\u003E It is tempting to put business rules in the view SQL, where other tools cannot see them. Keep the views to renaming, keys and types.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGrants.\u003C/strong\u003E Readers need \u003Ccode\u003ESELECT\u003C/code\u003E on the views. Snowflake's documentation states that Cortex Agents need \u003Ccode\u003ESELECT\u003C/code\u003E on both the semantic view and its underlying tables. Grant the agent's role \u003Ccode\u003ESELECT\u003C/code\u003E on the views, and test whether your setup also needs it on the base tables behind them.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPerformance.\u003C/strong\u003E Simple renaming views like these are normally merged into the query by the optimizer, so they typically add little or no cost. A layer that does heavy work, such as deduplicating change-data-capture tables, may need to be materialized, for example as a dynamic table, which brings its own refresh and cost considerations.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELineage gets one hop longer.\u003C/strong\u003E Tracing a metric back to its source column now goes through the view.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhen to skip it\u003C/h3\u003E\n","\u003Cp\u003EBinding directly to tables is the simpler choice when the tables are already a clean, well-named presentation layer, when the model only ever compiles to Snowflake, or when one team owns both the tables and the model. The interface layer pays off as the number of source systems, consuming tools or teams grows.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAutomate the Pipeline\u003C/h2\u003E\n","\u003Cp\u003ESuppose finance decides that revenue should also exclude lines that have not shipped yet. With OSI that is one edit, in one file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003E    - name: net_amount_kept\n      expression: {dialects: [{dialect: ANSI_SQL, expression: "CASE WHEN L_RETURNFLAG <> 'R' AND L_LINESTATUS = 'F' THEN L_EXTENDEDPRICE * (1 - L_DISCOUNT) ELSE 0 END"}]}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn an enterprise setup, the pull request triggers a pipeline:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EReview\u003C/strong\u003E: the finance data owner approves the change to the OSI file.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECompile to Snowflake\u003C/strong\u003E: deploy the interface views if you use them, then \u003Ccode\u003ESYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML\u003C/code\u003E, into a test schema.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETest\u003C/strong\u003E: run the parity queries, with the reference SQL updated to the new definition. Fail the build on any difference.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGenerate targets\u003C/strong\u003E: run every converter you depend on. Fail the build on new converter warnings or errors.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPublish\u003C/strong\u003E: deploy the semantic view to production and hand each generated artifact to its tool's own deployment process. Tools that read Snowflake semantic views natively pick up the change without a separate artifact.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENo tool keeps a hand-edited copy of the logic, so no tool drifts.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP DATABASE IF EXISTS OSI_GUIDE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou treated an open OSI model as the source of truth for revenue and compiled it into a Snowflake semantic view, with Cortex Analyst grounded on the same definition. You generated two proprietary formats from the same file and pushed the model through one of them and back. Bound directly to the tables, the conversions lost structure. With an optional interface layer of views, the same round trip kept every relationship and passed every parity test, at the cost of more objects to own. The lesson is architectural, not tool-specific. Own your semantics in one open file, generate and test every proprietary format from it, and decide deliberately how the model binds to the data. Native OSI support across vendors is still maturing, but that architecture is something you can adopt now.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhy an open semantic standard turns N-to-N integrations into one source with generated targets\u003C/li\u003E\u003Cli\u003EHow metric drift arises from formulas, filters and grain, measured on TPC-H\u003C/li\u003E\u003Cli\u003EThe OSI building blocks, why keys, relationships and \u003Ccode\u003Edatatype\u003C/code\u003E matter, and why \u003Ccode\u003Edescription\u003C/code\u003E is what grounds Cortex Analyst today\u003C/li\u003E\u003Cli\u003EWhich parts of a model are portable (\u003Ccode\u003EANSI_SQL\u003C/code\u003E, core spec) and which are Snowflake-specific\u003C/li\u003E\u003Cli\u003EHow to deploy OSI YAML as a semantic view and export it back\u003C/li\u003E\u003Cli\u003EHow to test a semantic view against reference SQL\u003C/li\u003E\u003Cli\u003EWhat conversions to proprietary formats keep and lose, and how to catch it in a pipeline\u003C/li\u003E\u003Cli\u003EWhen an interface layer of views is worth considering, and its benefits and downsides\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/apache/ossie\"\u003EApache Ossie on GitHub: specification and converters\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/apache/ossie/blob/main/core-spec/spec.md\"\u003EOSI core specification\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\"\u003ESnowflake semantic views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/best-practices-dev\"\u003EBest practices for developing and deploying semantic views\u003C/a\u003E, including BI tools that integrate with semantic views\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Getting Started with Apache OSI: An Open Source of Truth for Metrics",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nAsk three teams for \"total revenue\" and you will often get three numbers. On the TPC-H sample data that ships with every Snowflake account, five perfectly reasonable definitions give results between **$109B and $230B**:\n\n| Definition | Revenue (TPC-H SF1) |\n|---|---|\n| Gross: `SUM(L_EXTENDEDPRICE)` | $229.6B |\n| Order header: `SUM(O_TOTALPRICE)` | $226.8B |\n| Net of discount | $218.1B |\n| Net of discount, excluding returns | $164.4B |\n| Net of discount, shipped lines only | $108.9B |\n\nNobody made an error. Each dashboard, notebook and AI assistant simply encoded its own definition. This is **metric drift**, and it is one symptom of semantic fragmentation:\n\n- **Metric drift**: inconsistent KPIs across dashboards.\n- **Manual translation**: the same logic re-typed in every tool, then reconciled.\n- **Hallucinations**: an LLM grounded on conflicting or missing definitions guesses.\n- **Integration debt**: N-to-N custom integrations between proprietary semantic layers.\n\n**Open Semantic Interchange (OSI)** is a vendor-neutral YAML specification for semantic models: datasets, keys, relationships, fields and metrics. The specification and its converters are developed in the open as [Apache Ossie](https://github.com/apache/ossie).\n\nBe clear about what OSI is today. It is **not** a format that every tool reads natively. Snowflake can turn an OSI document directly into a [semantic view](https://docs.snowflake.com/en/user-guide/views-semantic/overview). Most other tools are reached through open-source converters that translate OSI into their own proprietary format, and those converters are young. The value of OSI right now is architectural: one open definition that you own, version and test, from which every tool-specific format is **generated** rather than hand-maintained.\n\nIn this guide you:\n\n1. Write the revenue definition once, in OSI, and compile it into a Snowflake semantic view that Cortex Analyst can use.\n2. Prove the semantic view returns the right numbers.\n3. Generate other tools' formats from the same file, and weigh how the model should bind to the data.\n\nThe tools used along the way are examples; the architectural pattern is what matters.\n\n\u003E NOTE:\n\u003E The OSI stored procedures used in this guide are in preview. Behaviour and supported spec versions may change. Everything in this guide was tested against OSI spec version `0.1.1`, the only version the Snowflake procedure accepted at the time of writing.\n\n### Prerequisites\n- Basic SQL knowledge\n- Familiarity with YAML\n\n### What You'll Learn\n- Why an open semantic standard matters architecturally, and where it stands today\n- The building blocks of an OSI semantic model and why each one matters\n- Which parts of an OSI model are portable and which are Snowflake-specific\n- How to create a semantic view from OSI YAML with `SYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML`\n- How to test a semantic view against reference SQL\n- How to generate proprietary formats from the model, and how to detect what a conversion loses\n- When an interface layer of views between tables and model is worth considering, and what it costs\n\n### What You'll Need\n- A Snowflake account with access to the OSI preview, and a role that can create a database\n- Python 3.11+ and `git` for the open-source converters\n\n### What You'll Build\n- An OSI model `tpch-revenue.osi.yaml` with one governed definition of revenue\n- A Snowflake semantic view compiled from it, with parity tests\n- Two proprietary formats generated from the same file, and a measured report of what each conversion keeps and loses\n- Optionally, an interface layer of views, to compare against binding the model to the tables directly\n\n\u003C!-- ------------------------ --\u003E\n## Why an Open Standard\n\nBefore writing any YAML, it helps to see the problem the way an enterprise architect does.\n\n### From N-to-N to hub and spoke\n\nA typical enterprise already has three to five semantic layers: one in the BI tool, one in the transformation framework, one in the notebook platform, and now one for every AI agent. Without a shared format, each pair of tools that must agree needs its own translation, maintained by hand. Three tools that define metrics and three that consume them already mean nine such translations, and every new tool adds a full row:\n\n![Without a standard, every pair of tools needs its own hand-maintained translation](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-apache-osi/n-to-n-integration.png?v=be5f4e5c)\n\nWith OSI as the hub, there is one reviewed and tested source. Snowflake compiles it natively into a semantic view, which AI agents and tools that read semantic views use directly. Other tools get their own format generated by a converter, never edited by hand:\n\n![With OSI as the hub, one source of truth compiles to Snowflake and generates every other format](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-apache-osi/osi-hub-and-spoke.png?v=be5f4e5c)\n\nWith a hub, adding a tool means adding one converter, not N new integrations. Proprietary formats stay, but they become **build artifacts**, like compiled binaries, rather than places where business logic is authored.\n\n### What this buys an enterprise today\n\n| Benefit | What it means in practice |\n|---|---|\n| **One point of governance** | Revenue is defined in one reviewed file. A change is a pull request with an owner, a reviewer and an audit trail, not an edit buried in a dashboard. |\n| **Vendor optionality** | The definition is not locked inside any one product. `SYSTEM$READ_OSSIE_YAML_FROM_SEMANTIC_VIEW` exports a Snowflake semantic view back to OSI, so choosing Snowflake does not trap your semantics. |\n| **Cheaper migration and onboarding** | Existing models in other semantic layers can be converted to OSI and compiled into Snowflake as a starting point, instead of being rewritten from scratch. Expect to repair what a converter cannot carry. |\n| **Consistent AI grounding** | The same descriptions feed Cortex Analyst, agents and BI tools, so an LLM and a dashboard work from the same definition. How much of the model each AI tool actually uses varies; see the Cortex Analyst section. |\n| **Loss you can measure** | Converters report what a target cannot represent, and parity tests prove the numbers. Drift between tools becomes a CI failure instead of a surprise in a board meeting. |\n\nHow much of this works today depends on the target; the section *Portable versus Snowflake-specific* covers it once you know the building blocks of a model.\n\n\u003C!-- ------------------------ --\u003E\n## Set Up Snowflake\n\nCreate a database for the semantic view and make sure the TPC-H sample data is available. Some accounts do not have `SNOWFLAKE_SAMPLE_DATA` mounted; the second statement fixes that.\n\n```sql\nCREATE DATABASE IF NOT EXISTS OSI_GUIDE;\nCREATE SCHEMA IF NOT EXISTS OSI_GUIDE.TPCH;\n\nCREATE DATABASE IF NOT EXISTS SNOWFLAKE_SAMPLE_DATA FROM SHARE SFC_SAMPLES.SAMPLE_DATA;\n\nSELECT COUNT(*) FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM;  -- 6,001,215\n```\n\nReproduce the drift for yourself:\n\n```sql\nSELECT\n  (SELECT SUM(O_TOTALPRICE) FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS) AS order_total_price,\n  SUM(L_EXTENDEDPRICE)                                                   AS gross,\n  SUM(L_EXTENDEDPRICE * (1 - L_DISCOUNT))                                AS net,\n  SUM(IFF(L_RETURNFLAG \u003C\u003E 'R', L_EXTENDEDPRICE * (1 - L_DISCOUNT), 0))   AS net_excl_returns,\n  SUM(IFF(L_LINESTATUS = 'F',  L_EXTENDEDPRICE * (1 - L_DISCOUNT), 0))   AS net_shipped_only\nFROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM;\n```\n\nNotice that the gap between \"net\" and \"net, shipped only\" comes from a **filter**, not a formula. A metric definition is the expression *and* its filters *and* its grain. That is exactly what we will pin down in OSI.\n\n\u003C!-- ------------------------ --\u003E\n## Anatomy of a Model\n\nAn OSI document describes one semantic model. Its building blocks map closely to how analysts already think:\n\n| Block | What it says | Why it matters for drift |\n|---|---|---|\n| `datasets` | A logical table and its physical `source` | Everyone starts from the same tables |\n| `primary_key` | What makes a row unique, i.e. the grain | Lets engines aggregate at the right level |\n| `relationships` | Many-to-one joins between datasets | Prevents fan-out double counting |\n| `fields` | Row-level columns or expressions | Shared building blocks such as `net_amount_kept` |\n| `metrics` | Aggregate expressions across datasets | The single definition of `net_revenue` |\n| `description` | Human-readable meaning | Becomes native comments in Snowflake, which Cortex Analyst reads |\n| `ai_context` | Extra context intended for AI tools | Portable hints for other tools; see the Cortex Analyst section for how Snowflake treats it today |\n| `custom_extensions` | Vendor-specific metadata | Keeps tool-specific settings out of the core |\n\n### Why keys and relationships are not optional\n\nJoin order lines to orders and sum the order header amount, a mistake that is easy to make in a notebook:\n\n```sql\nSELECT SUM(o.O_TOTALPRICE) AS naive_joined\nFROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS o\nJOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM l ON l.L_ORDERKEY = o.O_ORDERKEY;\n-- 1,134 billion: five times the real 226.8 billion\n```\n\nEvery order is counted once per line. Because the OSI model declares `primary_key` on each dataset and the direction of each relationship (`from` the many side `to` the one side), a semantic engine knows `orders` is at a coarser grain than `lineitem` and aggregates each metric at its own grain.\n\n### Decide the definition, then write it down\n\nFor this guide, the business agrees that **revenue is extended price after discount, excluding returned lines, before tax**. We encode it as a field plus a metric:\n\n```yaml\n    - name: net_amount_kept\n      datatype: Decimal\n      expression: {dialects: [{dialect: ANSI_SQL, expression: \"CASE WHEN L_RETURNFLAG \u003C\u003E 'R' THEN L_EXTENDEDPRICE * (1 - L_DISCOUNT) ELSE 0 END\"}]}\n      description: \u003E\n        Revenue per order line, the company definition: amount after discount,\n        before tax, zero for returned lines. Also called net sales. Sum it for revenue.\n...\n  metrics:\n  - name: net_revenue\n    datatype: Decimal\n    expression: {dialects: [{dialect: ANSI_SQL, expression: \"SUM(lineitem.net_amount_kept)\"}]}\n    description: \u003E\n      Revenue, the company definition: extended price after discount, excluding\n      returned lines, before tax. Also called sales or net sales. Use this metric\n      whenever a question asks for revenue or sales.\n    ai_context: \"Synonyms: revenue, sales, net sales\"\n```\n\nThe filter lives in the field expression, so every consumer inherits it. `O_TOTALPRICE` stays in the model as `total_price`, but its description says explicitly: \"Not revenue: never use it for revenue or sales questions.\"\n\nNotice that the business meaning, including synonyms such as *net sales*, is written into `description`, on the field as well as the metric. The same synonyms also appear in `ai_context`, a field intended for AI tools. The section on Cortex Analyst explains why the model states things twice: in the current preview, Snowflake only acts on the `description`.\n\n### Portable versus Snowflake-specific\n\nTwo questions decide how portable a model is: which parts of the document are standard, and which tools can consume it. Within the document:\n\n| Part of the model | Portable? | Notes |\n|---|---|---|\n| Datasets, keys, relationships, fields, metrics | Yes | Core OSI specification |\n| Expressions in `ANSI_SQL` dialect | Yes | Use where possible; the converters tested here read them |\n| Expressions in `SNOWFLAKE` dialect | Snowflake only | Use for Snowflake functions; add an `ANSI_SQL` variant for other tools |\n| `source: DB.SCHEMA.TABLE` | Partly | Any tool that connects to the same Snowflake tables can reuse it |\n| `custom_extensions` with `vendor_name: SNOWFLAKE` | Snowflake only | Ignored by other tools |\n\nAnd on the consuming side, at the time of writing (verified while building this guide):\n\n| Target | How it consumes the model | Status |\n|---|---|---|\n| Snowflake semantic views | `SYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML`, natively | Preview; OSI `0.1.1` only |\n| Snowflake export | `SYSTEM$READ_OSSIE_YAML_FROM_SEMANTIC_VIEW` | Preview; `datatype` values not exported |\n| Other tools' semantic formats | Open-source converters in the Apache Ossie repository (Hex, dbt MetricFlow, Cube, Sigma, Power BI, GoodData, Honeydew and others) | Converters, not native import; two are tested in this guide |\n\nMost third-party tools do not import OSI directly today. They read their own format, and some read Snowflake semantic views natively. The converters target the in-development spec `0.2.0.dev0` and were not yet on PyPI. Treat them as early tooling: useful, inspectable, and worth testing before you trust them.\n\nTwo practical rules that we learned while testing this guide:\n\n- **Give every field a `datatype`.** Snowflake can infer types from the source table, but a converter generating another tool's format cannot. Without it, one of the converters tested later typed prices as `string`.\n- **Write `ai_context` as a string.** The spec also allows an object (`instructions`, `synonyms`, `examples`), but the Snowflake procedure currently rejects the object form.\n\n\u003C!-- ------------------------ --\u003E\n## Deploy the Semantic View\n\nThe complete model is in [`assets/tpch-revenue.osi.yaml`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-apache-osi/assets/tpch-revenue.osi.yaml). Pass it to the procedure, together with the schema that will hold the semantic view. The semantic view gets the model's `name`.\n\n```sql\nCALL SYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML('OSI_GUIDE.TPCH', $$\n-- paste the full contents of assets/tpch-revenue.osi.yaml here\n$$);\n-- Semantic view was successfully created from OSI YAML.\n```\n\nThe `$$` delimiters let you paste the YAML as-is, without escaping quotes. Most of the file repeats the patterns from the previous section. The parts worth reading closely are the keys and relationships, which give the semantic view its grain and join paths:\n\n```yaml\n  - name: lineitem\n    source: SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM\n    primary_key: [l_orderkey, l_linenumber]\n...\n  relationships:\n  - {name: lineitem_to_orders, from: lineitem, to: orders, from_columns: [l_orderkey], to_columns: [o_orderkey]}\n  - {name: orders_to_customer, from: orders, to: customer, from_columns: [o_custkey], to_columns: [c_custkey]}\n  - {name: customer_to_nation, from: customer, to: nation, from_columns: [c_nationkey], to_columns: [n_nationkey]}\n  - {name: nation_to_region, from: nation, to: region, from_columns: [n_regionkey], to_columns: [r_regionkey]}\n```\n\nNote that `source` points straight at the shared sample database; the tables do not have to live in the same schema as the semantic view.\n\nQuery it like any semantic view:\n\n```sql\nSELECT *\nFROM SEMANTIC_VIEW(\n  OSI_GUIDE.TPCH.TPCH_REVENUE\n  METRICS net_revenue, order_count, average_order_value\n  DIMENSIONS region.region_name\n)\nORDER BY net_revenue DESC;\n```\n\nThe `region` dimension sits three relationships away from `lineitem` (lineitem → orders → customer → nation → region). The semantic view resolves those joins from the model; nobody writes them again.\n\n### Read the model back\n\nThe conversion also works in the other direction. This returns OSI YAML for any semantic view, including ones you built without OSI:\n\n```sql\nSELECT SYSTEM$READ_OSSIE_YAML_FROM_SEMANTIC_VIEW('OSI_GUIDE.TPCH.TPCH_REVENUE');\n```\n\nThe output uses upper-case names and adds `SNOWFLAKE` vendor extensions such as `access_modifier`. Keys, relationships and descriptions are exported from native semantic view properties. `ai_context` comes back too, but only because it was stored verbatim in the `OSI` extension; the OSI `datatype` values are not exported. This export is what keeps Snowflake from becoming a lock-in point: any semantic view, including ones built without OSI, can leave as an open document. Keep the hand-written YAML in Git as the source of truth and treat the read-back as an export.\n\n\u003C!-- ------------------------ --\u003E\n## Prove Zero Drift\n\nA shared definition is only useful if it is *correct*. Write each business question twice, once against the semantic view and once as independent reference SQL over the raw tables, and require zero differing rows.\n\n```sql\nWITH\nsv_total   AS (SELECT ROUND(net_revenue, 2) v\n               FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_REVENUE METRICS net_revenue)),\nref_total  AS (SELECT ROUND(SUM(IFF(L_RETURNFLAG \u003C\u003E 'R', L_EXTENDEDPRICE * (1 - L_DISCOUNT), 0)), 2) v\n               FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM),\n\nsv_region  AS (SELECT region_name k, ROUND(net_revenue, 2) v\n               FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_REVENUE METRICS net_revenue DIMENSIONS region.region_name)),\nref_region AS (SELECT r.R_NAME k, ROUND(SUM(IFF(l.L_RETURNFLAG \u003C\u003E 'R', l.L_EXTENDEDPRICE * (1 - l.L_DISCOUNT), 0)), 2) v\n               FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM l\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS   o ON o.O_ORDERKEY  = l.L_ORDERKEY\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER c ON c.C_CUSTKEY   = o.O_CUSTKEY\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.NATION   n ON n.N_NATIONKEY = c.C_NATIONKEY\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.REGION   r ON r.R_REGIONKEY = n.N_REGIONKEY\n               GROUP BY 1),\n\nsv_aov     AS (SELECT market_segment k, ROUND(average_order_value, 4) v\n               FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_REVENUE METRICS average_order_value DIMENSIONS customer.market_segment)),\nref_aov    AS (SELECT c.C_MKTSEGMENT k,\n                      ROUND(SUM(IFF(l.L_RETURNFLAG \u003C\u003E 'R', l.L_EXTENDEDPRICE * (1 - l.L_DISCOUNT), 0))\n                            / COUNT(DISTINCT l.L_ORDERKEY), 4) v\n               FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM l\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS   o ON o.O_ORDERKEY = l.L_ORDERKEY\n               JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER c ON c.C_CUSTKEY  = o.O_CUSTKEY\n               GROUP BY 1),\n\nsv_year    AS (SELECT order_year k, order_count v\n               FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_REVENUE METRICS order_count DIMENSIONS orders.order_year)),\nref_year   AS (SELECT YEAR(O_ORDERDATE) k, COUNT(*) v\n               FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS GROUP BY 1)\n\nSELECT 'total' AS test,\n       (SELECT COUNT(*) FROM (SELECT * FROM sv_total EXCEPT SELECT * FROM ref_total)) AS diffs\nUNION ALL SELECT 'by_region',\n       (SELECT COUNT(*) FROM ((SELECT * FROM sv_region EXCEPT SELECT * FROM ref_region)\n                    UNION ALL (SELECT * FROM ref_region EXCEPT SELECT * FROM sv_region)))\nUNION ALL SELECT 'aov_by_segment',\n       (SELECT COUNT(*) FROM ((SELECT * FROM sv_aov EXCEPT SELECT * FROM ref_aov)\n                    UNION ALL (SELECT * FROM ref_aov EXCEPT SELECT * FROM sv_aov)))\nUNION ALL SELECT 'orders_by_year',\n       (SELECT COUNT(*) FROM ((SELECT * FROM sv_year EXCEPT SELECT * FROM ref_year)\n                    UNION ALL (SELECT * FROM ref_year EXCEPT SELECT * FROM sv_year)));\n```\n\nAll four tests should return `diffs = 0`. The `orders_by_year` test is the fan-out check: `order_count` is defined on `orders`, and stays correct even though revenue is measured on `lineitem`.\n\nRun these tests every time the model changes, for example in a CI pipeline, and a definition can no longer drift silently.\n\n\u003C!-- ------------------------ --\u003E\n## Ground Cortex Analyst\n\nThe OSI procedure turns `description` into native `COMMENT`s on the semantic view, its facts, dimensions and metrics, and Cortex Analyst reads those. Ask a plain-language question in Snowsight (**AI & ML » Cortex Analyst**, select `OSI_GUIDE.TPCH.TPCH_REVENUE`) or through a Cortex Agent that uses the semantic view as a tool:\n\n\u003E What was total revenue by region in 1997?\n\nCortex Analyst generates:\n\n```sql\nSELECT *\nFROM SEMANTIC_VIEW(\n    OSI_GUIDE.TPCH.TPCH_REVENUE\n    DIMENSIONS region.region_name\n    METRICS NET_REVENUE\n    WHERE orders.order_year = 1997\n);\n```\n\nIt picked `net_revenue`, not the tempting `O_TOTALPRICE` column. The AI assistant now reports the same number as the finance dashboard. The names and descriptions in the model did that work, not prompt engineering: the metric is called `net_revenue`, its basis `net_amount_kept` is described as the company definition of revenue, and `total_price` is described as \"Not revenue\".\n\nThe synonyms written into the descriptions work too:\n\n| Question | Cortex Analyst chose |\n|---|---|\n| Show net sales per country by booking year | `NET_REVENUE` by `nation.nation_name` and `orders.order_year` |\n| What is the AOV per customer segment? | `AVERAGE_ORDER_VALUE` by `customer.market_segment` |\n\nIn the current preview, only `description` grounds Cortex Analyst. It becomes a native `COMMENT`, and it works best on the field Analyst has to choose between. `ai_context` is kept only in the `OSI` extension of the semantic view for export, and Cortex Analyst ignores it. Put anything an AI tool must respect in `description`.\n\n\u003E NOTE:\n\u003E This is the behaviour of the OSI procedure and Cortex Analyst as tested during the preview, with OSI spec version `0.1.1`. How `ai_context` is mapped into semantic views, for example into synonyms or custom instructions, may change in future versions. Re-test before you rely on it.\n\n\u003C!-- ------------------------ --\u003E\n## Generate Target Formats\n\nThe Snowflake semantic view is one target compiled from the OSI file. Other tools have their own proprietary semantic formats. In the hub-and-spoke architecture you never author those formats by hand: you **generate** them from the OSI source, the way a compiler produces binaries for different platforms.\n\nThis guide uses two of the open-source converters in the Apache Ossie repository as examples: Hex semantic project YAML and dbt MetricFlow. They represent two common kinds of target, a BI tool's semantic layer and a transformation framework's metric layer. The pattern is the same for any other converter. How each tool then loads its generated files is that tool's own concern and outside the scope of this guide.\n\n### Install the converters\n\nAt the time of writing, the converters and their core library are not on PyPI. Install them from the repository:\n\n```bash\ngit clone --depth 1 https://github.com/apache/ossie.git\npython3 -m venv .venv && source .venv/bin/activate\npip install ./ossie/python ./ossie/converters/hex ./ossie/converters/dbt\n```\n\n### Bridge the spec version\n\nThe converters expect the in-development spec `0.2.0.dev0`, which drops the `semantic_model:` list and puts the model at the document root. The Snowflake procedure only accepts `0.1.1`. The content is identical, so generate the newer shape from your single source file:\n\n```bash\npython - \u003C\u003C'EOF'\nimport yaml\nmodel = yaml.safe_load(open(\"tpch-revenue.osi.yaml\"))[\"semantic_model\"][0]\nyaml.safe_dump({\"version\": \"0.2.0.dev0\", **model},\n               open(\"tpch-revenue.osi.v02.yaml\", \"w\"), sort_keys=False)\nEOF\n```\n\nThis is itself an example of the pattern: one hand-edited file, other shapes generated in the build. When Snowflake supports the newer version, the step disappears.\n\n### Generate\n\n```bash\nossie-hex export     -i tpch-revenue.osi.v02.yaml -o hex_project -d ansi_sql -v\nossie-dbt ossie-to-msi -i tpch-revenue.osi.v02.yaml -o semantic_manifest.json\n```\n\nThe `-d ansi_sql` flag selects the expression dialect, which is why the model uses `ANSI_SQL`: portable expressions are what let one source feed many targets. Where a conversion succeeds, the business logic carries over unchanged. In the generated Hex YAML, for example:\n\n```yaml\nmeasures:\n- id: net_revenue\n  func_sql: SUM(${net_amount_kept})\nrelations:\n- id: lineitem_to_orders\n  target: orders\n  type: many_to_one\n```\n\nSame table, same filter, same metric, same join direction. Whether a conversion fully succeeds is the subject of the next section.\n\n\u003C!-- ------------------------ --\u003E\n## Measure What Conversion Loses\n\nAn open standard does not make tools identical. Each proprietary format can express some things and not others. The architectural value of OSI is that those gaps become **explicit and testable** instead of silent. This section measures them for the two example targets.\n\n### A BI semantic layer: warnings, not silent drops\n\nThe Hex export above reports:\n\n```\nEncountered 10 problems: 10 warnings.\n   7× `ai_context` — AI context is not preserved in Hex and was dropped.\n   1× `Dataset.primary_key` — Composite primary keys are not supported in Hex and were dropped.\n   1× `Field.is_time` — Temporal role markers are not supported in Hex and were dropped.\n   1× `description` — Project descriptions are not supported in Hex and were dropped.\n```\n\nThe metrics and joins survive; the AI context and the composite key on `lineitem` do not. An earlier draft of this model had no `datatype` on most fields and produced 23 extra warnings, with prices typed as `string` in the target. Adding types fixed that.\n\n### A metric layer: a round trip\n\nMany enterprises already have metrics defined in another tool, and the most valuable use of a standard is to bring them over instead of rewriting them. dbt MetricFlow is a good test case. Converting to its format:\n\n```bash\nossie-dbt ossie-to-msi -i tpch-revenue.osi.v02.yaml -o semantic_manifest.json\n# ValueError: Dataset 'lineitem' has composite primary key ['l_orderkey', 'l_linenumber'];\n# MetricFlow entities cannot represent composite keys losslessly\n```\n\nThe converter refuses rather than producing a subtly wrong model. That is the right behaviour: the key defines the grain of `lineitem`, and the grain is what protects against fan-out. Removing the key would make the error go away by throwing that protection away. The proper fix is a single-column key, which is what the interface layer in the next section provides. Only to continue this experiment, a throwaway copy with the `primary_key` removed converts successfully. The converter even turns `average_order_value` into a proper MetricFlow ratio metric. Do not do this in a real model.\n\nNow simulate the migration direction: take that manifest as if it were an existing project, convert it back to OSI and compile it into Snowflake:\n\n```bash\nossie-dbt msi-to-ossie -i semantic_manifest.json -o from_dbt.yaml\n```\n\nWrap `from_dbt.yaml` in the `0.1.1` shape, name the model `tpch_from_dbt` and deploy it with `SYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML`. Then run two queries:\n\n```sql\nSELECT net_revenue FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_FROM_DBT METRICS net_revenue);\n-- 164,360,931,200.40: identical to the original\n\nSELECT * FROM SEMANTIC_VIEW(OSI_GUIDE.TPCH.TPCH_FROM_DBT\n  METRICS net_revenue DIMENSIONS region.region_name);\n-- SQL compilation error: The entities 'REGION' and 'LINEITEM' are not related.\n```\n\nThe metric definition and its descriptions came through intact. All four relationships, the `datatype` values and the `ai_context` were lost. MetricFlow expresses joins through shared entity names, and the converter names each entity after its own key column (`l_orderkey` on one side, `o_orderkey` on the other), so nothing matches. Snowflake then refuses the cross-dataset query instead of returning a wrong number.\n\n### What survived where\n\n| Model element | Snowflake semantic view (and its OSI read-back) | BI semantic layer (`ossie-hex`) | Metric layer round trip (`ossie-dbt`) |\n|---|---|---|---|\n| Metrics and expressions | Yes | Yes | Yes |\n| Field filters (`net_amount_kept`) | Yes | Yes | Yes |\n| Descriptions | Yes | Mostly | Yes |\n| Relationships | Yes | Yes | **No** |\n| Composite primary key | Yes | Dropped, with warning | Refused |\n| `datatype` | Used, but not in the read-back export | Yes | **No** |\n| `ai_context` | Stored in the `OSI` extension only; not used by Cortex Analyst in our test | Dropped, with warning | **No** |\n\n### The architectural lesson\n\n- **Keep OSI as the source and never edit generated formats.** Losses then happen in one place, in one direction, and can be fixed by improving a converter or the model, not by patching every tool.\n- **Test every target, not just the source.** The same parity queries that validated `TPCH_REVENUE` would have failed on `TPCH_FROM_DBT` immediately.\n- **Model for portability.** Put filters in field expressions, give every field a `datatype`, write expressions in `ANSI_SQL`, prefer single-column keys where the grain allows it, and put meaning in `description` rather than only in `ai_context`.\n- **Prefer native paths where they exist.** Compiling OSI into a Snowflake semantic view and letting tools read that view natively avoids a converter altogether. Several BI tools integrate with Snowflake semantic views directly.\n\nMany of the losses above trace back to how the model is bound to the data, not to the standard itself. The model points straight at physical tables, with their composite keys and inconsistent column names. That leaves an uncomfortable choice: keep the composite key and lose the conversion, or drop the key and lose the grain. The next section explores a design pattern that removes the dilemma.\n\n\u003C!-- ------------------------ --\u003E\n## Consider an Interface Layer\n\nSo far the OSI model has read directly from the data layer: `SNOWFLAKE_SAMPLE_DATA.TPCH_SF1`. That is a perfectly valid way to work, and Snowflake semantic views accept tables and views alike. This section explores an alternative worth considering: a thin layer of views between the physical tables and the semantic model, so that the model binds to a stable interface instead of directly to storage.\n\nThis is a general architectural design pattern, not a Snowflake recommendation. Whether it is worth it depends on your situation. The case for it on this data set is concrete, though. The composite key on `LINEITEM` forced a choice between correctness and portability. A view that exposes a single-column key gives you both: the grain stays declared, and the conversion succeeds. The section shows what it changes, then weighs the benefits against the downsides.\n\n### Where the pattern comes from\n\n- **Logical data independence.** The ANSI/SPARC three-schema architecture separates the physical schema from the external schemas that consumers see. Consumers survive physical changes, such as renamed columns, split tables or a new source system, because only the mapping changes. A semantic model is such a consumer.\n- **dbt layering.** dbt's recommended project structure goes from sources to staging to marts, and MetricFlow semantic models are defined on models, not on raw sources.\n- **Dimensional modelling.** Kimball's presentation layer gives BI tools a curated star schema rather than operational tables.\n\n\n### Build the views\n\nRun [`assets/interface-views.sql`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-apache-osi/assets/interface-views.sql). It creates five views in `OSI_GUIDE.TPCH`, next to the semantic views, which keeps grants for the domain in one schema. The views only rename, key and type the data:\n\n```sql\nCREATE OR REPLACE VIEW OSI_GUIDE.TPCH.ORDER_LINE AS\nSELECT L_ORDERKEY || '-' || L_LINENUMBER AS order_line_key,   -- single-column surrogate key\n       L_ORDERKEY      AS order_key,\n       L_RETURNFLAG    AS return_flag,\n       L_EXTENDEDPRICE AS extended_price,\n       L_DISCOUNT      AS discount,\n       ...\nFROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM;\n\nCREATE OR REPLACE VIEW OSI_GUIDE.TPCH.SALES_ORDER AS\nSELECT O_ORDERKEY AS order_key,       -- same name as in ORDER_LINE\n       O_CUSTKEY  AS customer_key,\n       ...\nFROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS;\n```\n\nTwo design rules matter:\n\n- **A key has the same name everywhere it appears.** `order_key` in `ORDER_LINE` and `SALES_ORDER`, `customer_key` in `SALES_ORDER` and `CUSTOMER`.\n- **Every dataset has a single-column primary key.** `order_line_key` replaces the composite `(L_ORDERKEY, L_LINENUMBER)`. The script checks that it is unique across all 6,001,215 rows.\n\nThe business rule, excluding returned lines, deliberately stays in the OSI model and not in the view. Views express how data is stored; the model expresses what the business means. Hiding a definition in SQL would make it invisible to every other tool.\n\n### Point the model at the views\n\n[`assets/tpch-revenue-if.osi.yaml`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-apache-osi/assets/tpch-revenue-if.osi.yaml) is the same model bound to the views. Only the `source` lines, column names and keys change:\n\n```yaml\n  - name: order_line\n    source: OSI_GUIDE.TPCH.ORDER_LINE\n    primary_key: [order_line_key]\n...\n  relationships:\n  - {name: order_line_to_sales_order, from: order_line, to: sales_order, from_columns: [order_key], to_columns: [order_key]}\n```\n\nDeploy it as `TPCH_REVENUE_IF` with `SYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML`, then run the parity tests against `OSI_GUIDE.TPCH.TPCH_REVENUE_IF`, with the reference SQL still on the raw tables. All four return `diffs = 0`, so the views add no drift.\n\n### Rerun the conversions\n\nGenerate the `0.2.0.dev0` copy and run both converters again:\n\n```bash\nossie-hex export -i tpch-revenue-if.osi.v02.yaml -o hex_project_if -d ansi_sql -v\n# Encountered 9 problems: 9 warnings.   (composite key warning is gone)\n\nossie-dbt ossie-to-msi -i tpch-revenue-if.osi.v02.yaml -o semantic_manifest.json\n# Written to semantic_manifest.json      (no manual edit needed)\nossie-dbt msi-to-ossie -i semantic_manifest.json -o from_dbt.yaml --model-name tpch_if_from_dbt\n```\n\nIn the MetricFlow manifest, the entities now line up: `order_key` is a foreign entity on `order_line` and the primary entity on `sales_order`, so MetricFlow can join them. Converted back to OSI, the model has all four relationships and five primary keys. Deploy it as `TPCH_IF_FROM_DBT` and run the parity tests: **all four pass**, including revenue by region, which failed before.\n\n| Result | Direct on tables | Through interface views |\n|---|---|---|\n| BI semantic layer export warnings | 10 | 9 |\n| Metric layer export | Refused: composite key | Succeeds unchanged |\n| Relationships after round trip | 0 of 4 | 4 of 4 |\n| Parity tests after round trip | Cross-dataset queries fail | 4 of 4 pass |\n| `datatype` and `ai_context` after round trip | Lost | Still lost |\n\nRead the table as the answer to the dilemma from the previous section. Nothing in the OSI model was weakened to get there. The grain is still declared and the business rule is unchanged. Only the binding to the data changed, and the generated model went from unusable for cross-dataset questions to passing every parity test.\n\nThe last row is the honest limit. Views fix structural problems such as keys, names and grain. They cannot make a target format carry metadata it has no slot for. That is a converter or target limitation, so keep catching it in the pipeline.\n\n### Benefits and downsides\n\nBenefits:\n\n- **Correctness and portability together.** A single-column key keeps the grain declared and is something every target format can represent. Without the views, this data set forces you to give up one or the other.\n- **Portability.** Consistent key names are what let the relationships survive the round trip. Other tools can bind to the same view names.\n- **Insulation from physical change.** A renamed column, a split table or a move to SF10 becomes a change to the views, not to the OSI file, its tests or any generated target.\n- **Reuse across sources.** Two systems holding the same concepts can each get their own views exposing the same names, and share one model.\n- **A clear split of concerns.** Views describe how data is stored; the OSI model describes what the business means.\n\nDownsides:\n\n- **More objects to own.** The views need the same versioning, review and deployment as the model. Without that discipline they become one more place where logic drifts.\n- **Another place to hide logic.** It is tempting to put business rules in the view SQL, where other tools cannot see them. Keep the views to renaming, keys and types.\n- **Grants.** Readers need `SELECT` on the views. Snowflake's documentation states that Cortex Agents need `SELECT` on both the semantic view and its underlying tables. Grant the agent's role `SELECT` on the views, and test whether your setup also needs it on the base tables behind them.\n- **Performance.** Simple renaming views like these are normally merged into the query by the optimizer, so they typically add little or no cost. A layer that does heavy work, such as deduplicating change-data-capture tables, may need to be materialized, for example as a dynamic table, which brings its own refresh and cost considerations.\n- **Lineage gets one hop longer.** Tracing a metric back to its source column now goes through the view.\n\n### When to skip it\n\nBinding directly to tables is the simpler choice when the tables are already a clean, well-named presentation layer, when the model only ever compiles to Snowflake, or when one team owns both the tables and the model. The interface layer pays off as the number of source systems, consuming tools or teams grows.\n\n\u003C!-- ------------------------ --\u003E\n## Automate the Pipeline\n\nSuppose finance decides that revenue should also exclude lines that have not shipped yet. With OSI that is one edit, in one file:\n\n```yaml\n    - name: net_amount_kept\n      expression: {dialects: [{dialect: ANSI_SQL, expression: \"CASE WHEN L_RETURNFLAG \u003C\u003E 'R' AND L_LINESTATUS = 'F' THEN L_EXTENDEDPRICE * (1 - L_DISCOUNT) ELSE 0 END\"}]}\n```\n\nIn an enterprise setup, the pull request triggers a pipeline:\n\n1. **Review**: the finance data owner approves the change to the OSI file.\n2. **Compile to Snowflake**: deploy the interface views if you use them, then `SYSTEM$CREATE_SEMANTIC_VIEW_FROM_OSSIE_YAML`, into a test schema.\n3. **Test**: run the parity queries, with the reference SQL updated to the new definition. Fail the build on any difference.\n4. **Generate targets**: run every converter you depend on. Fail the build on new converter warnings or errors.\n5. **Publish**: deploy the semantic view to production and hand each generated artifact to its tool's own deployment process. Tools that read Snowflake semantic views natively pick up the change without a separate artifact.\n\nNo tool keeps a hand-edited copy of the logic, so no tool drifts.\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\n```sql\nDROP DATABASE IF EXISTS OSI_GUIDE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nYou treated an open OSI model as the source of truth for revenue and compiled it into a Snowflake semantic view, with Cortex Analyst grounded on the same definition. You generated two proprietary formats from the same file and pushed the model through one of them and back. Bound directly to the tables, the conversions lost structure. With an optional interface layer of views, the same round trip kept every relationship and passed every parity test, at the cost of more objects to own. The lesson is architectural, not tool-specific. Own your semantics in one open file, generate and test every proprietary format from it, and decide deliberately how the model binds to the data. Native OSI support across vendors is still maturing, but that architecture is something you can adopt now.\n\n### What You Learned\n- Why an open semantic standard turns N-to-N integrations into one source with generated targets\n- How metric drift arises from formulas, filters and grain, measured on TPC-H\n- The OSI building blocks, why keys, relationships and `datatype` matter, and why `description` is what grounds Cortex Analyst today\n- Which parts of a model are portable (`ANSI_SQL`, core spec) and which are Snowflake-specific\n- How to deploy OSI YAML as a semantic view and export it back\n- How to test a semantic view against reference SQL\n- What conversions to proprietary formats keep and lose, and how to catch it in a pipeline\n- When an interface layer of views is worth considering, and its benefits and downsides\n\n### Related Resources\n- [Apache Ossie on GitHub: specification and converters](https://github.com/apache/ossie)\n- [OSI core specification](https://github.com/apache/ossie/blob/main/core-spec/spec.md)\n- [Snowflake semantic views](https://docs.snowflake.com/en/user-guide/views-semantic/overview)\n- [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)\n- [Best practices for developing and deploying semantic views](https://docs.snowflake.com/en/user-guide/views-semantic/best-practices-dev), including BI tools that integrate with semantic views\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b646e9fdee","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-139d517dcd",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0d9f7d4fb3","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-14",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-cdd514e480","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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