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data. Businesses change at a rapid rate, becoming more competitive all the time. Those that can harness the value of their data faster to drive better decision making and better business outcomes will prevail.\u003C/p\u003E\n","\u003Cp\u003EOne of the benefits of using the Data Vault system is that it was designed from inception not only to accept data loaded using traditional batch mode (which was the prevailing mode in the early 2000s when \u003Ca href=\"https://datavaultalliance.com/#about\"\u003EDan Linstedt\u003C/a\u003E introduced Data Vault) but also to easily accept data loading continuously in real or near-realtime (NRT). In the early 2000s, that was a nice-to-have aspect of the approach and meant the methodology was effectively future-proofed from that perspective. Still, few relational database management systems had the ability to support that kind of requirement. Today, RT or at least NRT loading is almost becoming a mandatory requirement for modern data platforms. Granted, not all loads or use cases need to be NRT, but most forward-thinking organizations need to onboard data for analytics in an NRT manner.\u003C/p\u003E\n","\u003Cp\u003EThose who have been using the Data Vault approach don’t need to change much other than figure out how to engineer their data pipeline to serve up data to the Data Vault in NRT. The data models don’t need to change; the reporting views don’t need to change; even the loading patterns don’t need to change. For those that aren’t using Data Vault already, if they have real-time loading requirements, this architecture and method might be worth considering.\u003C/p\u003E\n","\u003Ch3\u003EData Vault on Snowflake\u003C/h3\u003E\n","\u003Cp\u003EThere have been numerous \u003Ca href=\"/blog/tips-for-optimizing-the-data-vault-architecture-on-snowflake/\"\u003Eblog posts\u003C/a\u003E, user groups, and webinars over the years, discussing the best practices and customer success stories of implementing Data Vaults on Snowflake.  But, how do we build a Data Vault on Snowflake that has real-time or near real-time continuous data streaming into it?\u003C/p\u003E\n","\u003Cp\u003ELuckily, streaming data is one of the \u003Ca href=\"/cloud-data-platform/\"\u003Euse-cases\u003C/a\u003E that Snowflake was built to support, so we have many features to help us achieve this goal. \u003Cstrong\u003EThis guide is an extended version of the \u003Ca href=\"https://datavaultalliance.com/news/building-a-real-time-data-vault-in-snowflake/\"\u003Earticle\u003C/a\u003E posted on the Data Vault Alliance website, with practical steps to build an example of a real-time Data Vault on Snowflake. Join us for simple-to-follow steps to see it in action.\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-key-concepts\"\u003ESnowflake key concepts and architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EFamiliarity with \u003Ca href=\"https://datavaultalliance.com/#resources\"\u003EData Vault methodology and architecture\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use Data Vault modeling on Snowflake\u003C/li\u003E\u003Cli\u003EHow to build basic objects and write ELT code for them\u003C/li\u003E\u003Cli\u003EHow to leverage \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro\"\u003ESnowpipe\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-pipelines\"\u003EContinuous Data Pipelines\u003C/a\u003E to automate data processing\u003C/li\u003E\u003Cli\u003EHow to apply data virtualization to accelerate data access\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account -- we recommend starting with a \u003Ca href=\"https://trial.snowflake.com/\"\u003Etrial\u003C/a\u003E account\u003C/li\u003E\u003Cli\u003EArchitectural objects created through the \u003Ca href=\"https://www.snowflake.com/en/developers/guides/defensible-analytics-using-data-vault-and-snowflake/\"\u003EDefensible Analytics using Data Vault and Snowflake\u003C/a\u003E guide. If you haven't used this guide to set up foundational architecture in your account, you'll need to alter the references to roles, databases, schemas, warehouses, and other Snowflake objects in order to work with your specific account design.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EData Vault models on Snowflake, based on sample dataset\u003C/li\u003E\u003Cli\u003EData pipelines, leveraging streams, tasks and Snowpipe\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EReference Architecture and Environment Setup\u003C/h2\u003E\n","\u003Cp\u003EThis is the target architecture.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/multitierdatavaultarchitecture.png?v=7a7bb18b\" alt=\"Multi-Tier Data Vault Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the \u003Ca href=\"https://www.snowflake.com/en/developers/guides/defensible-analytics-using-data-vault-and-snowflake/\"\u003EDefensible Analytics using Data Vault and Snowflake\u003C/a\u003E guide, we implement components of this architecture, which we'll use in this guide. If you haven't already executed the steps in that guide in your Snowflake account, you'll need to do that now, or change the examples given in this guide to accommodate your design.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Pipelines: Design\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/continuousdatapipelinearchitecture.png?v=7a7bb18b\" alt=\"Continuous Data Pipeline Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESnowflake supports multiple options for engineering data pipelines. In this guide, we'll demonstrate one of the most efficient ways to implement incremental continuous data integration with Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-pipelines\"\u003EContinuous Data Pipelines\u003C/a\u003E. Let's take a look at the architecture diagram above to understand how it works.\u003C/p\u003E\n","\u003Cp\u003EWe'll use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/streams\"\u003Estream\u003C/a\u003E objects to track data inserts into our Landing Zone tables. This process is automatic, and unlike a traditional RDBMS, this change-tracking will never impact the speed of data loading. The change data captured in a stream is automatically consumed once there is a successfully completed DML operation using the stream object as a source. After loading new data into a table, the changes are immediately reflected in the stream showing the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/streams#data-flow\"\u003Edelta\u003C/a\u003E that requires processing.\u003C/p\u003E\n","\u003Cp\u003EWe'll then use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003Etasks\u003C/a\u003E. Tasks are a powerful way to automate data processing, waking up on a defined schedule (here, every 1 minute), checking for new data in the associated stream, and if so, they execute SQL, pushing into the Raw Vault objects. Tasks can be arranged in a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro#simple-tree-of-tasks\"\u003Etree-like dependency graph\u003C/a\u003E, executing child tasks the moment the predecessor finished its part.\u003C/p\u003E\n","\u003Cp\u003EFollowing Data Vault best practices for continuous data integration -- loading data in parallel -- we'll use Snowflake’s \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/insert-multi-table\"\u003Emulti-table insert (MTI)\u003C/a\u003E capability in our tasks to populate multiple Raw Vault objects in our Enterprise Memory Zone using a single DML transaction. These tasks utilize scalable virtual warehouses, which means we always have enough \u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-overview#warehouse-size\"\u003Ecompute power\u003C/a\u003E (from XS to 6XL) to handle any workload, whilst the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-multicluster#multi-cluster-warehouses\"\u003Emulti-cluster virtual warehouse\u003C/a\u003E option will automatically scale-out and load balance all the tasks as more hubs, links and satellites are introduced.\u003C/p\u003E\n","\u003Cp\u003EFinally, as our Raw Vault is updated, streams will be used to propagate changes to Business Vault objects (such as derived Satellites, or PITs and Bridges, if needed for performance) in the Information Delivery Zone. This setup can be repeated, continuously transforming data through in small increments, quickly and efficiently. Virtualized facts and dimensions are delivered to data consumers, with a truly agile data pipeline backing them up.\u003C/p\u003E\n","\u003Cp\u003EFollowing this approach will result in a fully automated, reliable and defensible production data pipeline that powers decisions based on the latest data available.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild: Landing Zone, Sample Data and Staging for the Raw Vault\u003C/h2\u003E\n","\u003Cp\u003EEvery new Snowflake trial account includes access to \u003Ca href=\"https://docs.snowflake.com/en/user-guide/sample-data\"\u003Esample data sets\u003C/a\u003E, and we can find these in the SNOWFLAKE_SAMPLE_DATA database. For this guide, we'll use a subset of objects from \u003Ca href=\"https://docs.snowflake.com/en/user-guide/sample-data-tpch\"\u003ETPC-H\u003C/a\u003E set, representing \u003Cstrong\u003Ecustomers\u003C/strong\u003E and their \u003Cstrong\u003Eorders\u003C/strong\u003E. We'll also store reference data about \u003Cstrong\u003Enations\u003C/strong\u003E and \u003Cstrong\u003Eregions\u003C/strong\u003E.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDataset\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESystem\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESpecific Source\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELoad Scenario\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMechanism\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStatic Reference\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Esnowflake.sample_data.tpch_sf10.nation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eone-off CTAS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESQL\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERegion\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStatic Reference\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Esnowflake.sample_data.tpch_sf10.region\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eone-off CTAS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESQL\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer System\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Esnowflake.sample_data.tpch_sf10.customer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eincremental JSON files\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpipe\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrders\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrders System\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Esnowflake.sample_data.tpch_sf10.orders\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eincremental CSV files\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpipe\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIn the real world, we rarely have a singular source system with all our data. So, even though our sample dataset comes from a single Snowflake share, we'll act as if it comes from three different source systems, and simulate the customer detail comes in the form of a semi-structured raw JSON object.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1: LZ - Static Reference Data\u003C/h3\u003E\n","\u003Cp\u003ELet's start with the static reference data, loading that with simple one-off CTAS (CREATE TABLE ... AS SELECT ...) statements. If you've just completed the \u003Ca href=\"https://www.snowflake.com/en/developers/guides/defensible-analytics-using-data-vault-and-snowflake/\"\u003EDefensible Analytics using Data Vault and Snowflake\u003C/a\u003E guide, create a new SQL file in Workspaces named DVRealTime.sql.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Vault audit columns:\u003C/strong\u003E Every entity in this guide carries two mandatory audit columns. \u003Ccode\u003Eldts\u003C/code\u003E (\u003Cem\u003ELoad Date Time Stamp\u003C/em\u003E) is the immutable timestamp of when the row was loaded into the vault — set once on arrival and never changed, with meaning only to the data vault. This is not when the data originated, but when loaded in from the source. \u003Ccode\u003Ersrc\u003C/code\u003E (\u003Cem\u003ERecord Source\u003C/em\u003E) identifies the originating system or feed. Together they ensure every record is fully traceable: you can always answer \u003Cem\u003Ewhen\u003C/em\u003E did this arrive and \u003Cem\u003Ewhere\u003C/em\u003E did it come from, satisfying both auditability and compliance requirements.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LZ: Static Reference Data ---------------------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\nUSE DATABASE DEV_LZ;\nUSE SCHEMA TPCH_REF;\n\nCREATE OR REPLACE TABLE stg_nation \nAS \nSELECT src.*\n     , CURRENT_TIMESTAMP()    ldts \n     , 'Static Reference'     rsrc \n  FROM snowflake_sample_data.tpch_sf10.nation src\n;\n\nCREATE OR REPLACE TABLE stg_region\nAS \nSELECT src.*\n     , CURRENT_TIMESTAMP()    ldts \n     , 'Static Reference'     rsrc \n  FROM snowflake_sample_data.tpch_sf10.region src\n;\n-- Note that we're using OR REPLACE here in the guide for ease of correcting mistakes. In a production landing zone, data should be immutable.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: LZ - Raw Customer and Order Data\u003C/h3\u003E\n","\u003Cp\u003ENext, let's create staging tables for our future data pipeline. This syntax should be familiar to most. It is ANSI SQL compliant DDL, but with a key exception - for stg_customer we'll load the full payload of JSON into the raw_json column using the data type \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/data-types-semistructured.html\"\u003EVARIANT\u003C/a\u003E. We'll also enable change tracking, enabling our future use of streams.\u003C/p\u003E\n","\u003Cp\u003EAs we load data, we'll also add some technical metadata, like the row number in a file.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LZ: Customer System ---------------------------------------------------------\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nCREATE OR REPLACE TABLE stg_customer\n(\n  raw_json                VARIANT\n, filename                STRING    NOT NULL\n, file_row_seq            NUMBER    NOT NULL\n, ldts                    TIMESTAMP NOT NULL\n, rsrc                    STRING    NOT NULL\n)\nCHANGE_TRACKING = TRUE;\n\n-- LZ: Orders System -----------------------------------------------------------\nUSE SCHEMA TPCH_ORDERS_SYS;\n\nCREATE OR REPLACE TABLE stg_orders\n(\n  o_orderkey              NUMBER\n, o_custkey               NUMBER\n, o_orderstatus           STRING\n, o_totalprice            NUMBER\n, o_orderdate             DATE\n, o_orderpriority         STRING\n, o_clerk                 STRING\n, o_shippriority          NUMBER\n, o_comment               STRING\n, filename                STRING    NOT NULL\n, file_row_seq            NUMBER    NOT NULL\n, ldts                    TIMESTAMP NOT NULL\n, rsrc                    STRING    NOT NULL\n)\nCHANGE_TRACKING = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: DV - Tracking New Data to be loaded into the Raw Vault\u003C/h3\u003E\n","\u003Cp\u003EThe tables we created will be used by Snowpipe to drip-feed the data as it lands in the stage. In order to easily detect and incrementally process the new portion of data we'll create \u003Ca href=\"https://docs.snowflake.com/en/user-guide/streams.html\"\u003Estreams\u003C/a\u003E on these staging tables. These streams are part of the mechanism used to load data into the Enterprise Memory Zone's Raw Vault, not into the Landing Zone, so we place them accordingly.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--- DV: Tracking Customer Changes ----------------------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DV;\nUSE SCHEMA SALESMKT;\n\nCREATE OR REPLACE STREAM stg_customer_strm ON TABLE DEV_LZ.TPCH_CUSTOMER_SYS.stg_customer;\n\n-- DV: Tracking Orders Changes ------------------------------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA CUSTSERV;\n\nCREATE OR REPLACE STREAM stg_orders_strm ON TABLE DEV_LZ.TPCH_ORDERS_SYS.stg_orders;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote the use of two different roles here. We are effectively acting as a data engineer for two domains. Domains are not teams. A single person can have multiple functional roles, akin to "wearing different hats." In a real-world scenario, we're likely working with subject matter experts and getting approval from leaders specific to either the Sales & Marketing domain (perhaps the CRO, or VP of Sales), or the Customer Service domain (perhaps the CCO, or VP of Customer Success). The process is similar, but the specific people are often different.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 4: LZ - Simulating New Data into the Landing Zone\u003C/h3\u003E\n","\u003Cp\u003ENext we'll produce some sample data. And for the sake of simplicity we'll take a bit of a shortcut here, generating data by unloading subset of data from our TPCH sample dataset into files. Then we'll use Snowpipe to load it back into our Landing Zone, simulating the streaming feed.\u003C/p\u003E\n","\u003Cp\u003ELet's start by creating two stages for each data class type, orders and customer. In real-life scenarios these could be internal or external stages, or these feeds could be sourced via the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/kafka-connector/index\"\u003ESnowflake Connector for Kafka\u003C/a\u003E, using high-performance \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview\"\u003ESnowpipe Streaming\u003C/a\u003E. The world is your oyster.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LZ: Simulating Customer System Data -----------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\nUSE DATABASE DEV_LZ;\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nCREATE OR REPLACE STAGE customer_data FILE_FORMAT = (TYPE = JSON);\n\n-- LZ: Simulating Orders System Data -------------------------------------------\nUSE SCHEMA TPCH_ORDERS_SYS;\n\nCREATE OR REPLACE STAGE orders_data   FILE_FORMAT = (TYPE = CSV );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we'll generate and unload sample data. There are couple of things going on.\u003C/p\u003E\n","\u003Cp\u003EFirst, we are using \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/object_construct.html\"\u003Eobject_construct\u003C/a\u003E as a quick way to create a object/document from all columns and subset of rows for \u003Cstrong\u003Ecustomer\u003C/strong\u003E data and offload it into customer_data stage. \u003Cstrong\u003EOrders\u003C/strong\u003E data would be extracted into compressed CSV files. There are many additonal options in the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/copy-into-location.html\"\u003ECOPY INTO stage\u003C/a\u003E construct that would fit most requirements, but in this case we are using INCLUDE_QUERY_ID to make it easier to generate new incremental files, as we'll run these commands over and over again, without a need to deal with file overriding.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LZ: Simulating Customer System Data -----------------------------------------\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nCOPY INTO @customer_data \nFROM\n(SELECT object_construct(*)\n  FROM snowflake_sample_data.tpch_sf10.customer limit 10\n) \nINCLUDE_QUERY_ID=TRUE;\n\n-- LZ: Simulating Orders System Data -------------------------------------------\nUSE SCHEMA TPCH_ORDERS_SYS;\n\nCOPY INTO @orders_data \nFROM\n(SELECT *\n  FROM snowflake_sample_data.tpch_sf10.orders limit 1000\n) \nINCLUDE_QUERY_ID=TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can now run the following to validate that the JSON object data is now stored in files:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LZ: Inspecting Customer System Data -----------------------------------------\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nLIST @customer_data;\n\nSELECT METADATA$FILENAME,$1 FROM @customer_data; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/semistructuredcustomerdata.png?v=7a7bb18b\" alt=\"Semistructured JSON Customer Data\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, we will setup Snowpipe to load data from files in a stage into staging tables.\u003C/p\u003E\n","\u003Cp\u003EFor better transparency, we will trigger Snowpipe explicitly to scan for new files. However, in real projects, we would likely enable AUTO_INGEST, connecting it to cloud storage events (like AWS SNS) to process new files automatically.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LZ: Simulating Orders System Data -------------------------------------------\nUSE SCHEMA TPCH_ORDERS_SYS;\n\nCREATE OR REPLACE PIPE stg_orders_pp \nAS \nCOPY INTO stg_orders \nFROM\n(\nSELECT $1,$2,$3,$4,$5,$6,$7,$8,$9 \n     , metadata$filename\n     , metadata$file_row_number\n     , CURRENT_TIMESTAMP()\n     , 'Orders System'\n  FROM @orders_data\n);\n\nALTER PIPE DEV_LZ.TPCH_ORDERS_SYS.stg_orders_pp REFRESH;\n\n-- LZ: Simulating Customer System Data -----------------------------------------\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nCREATE OR REPLACE PIPE stg_customer_pp \n--AUTO_INGEST = TRUE\n--aws_sns_topic = 'arn:aws:sns:mybucketdetails'\nAS \nCOPY INTO stg_customer\nFROM \n(\nSELECT $1\n     , metadata$filename\n     , metadata$file_row_number\n     , CURRENT_TIMESTAMP()\n     , 'Customer System'\n  FROM @customer_data\n);\n\nALTER PIPE DEV_LZ.TPCH_CUSTOMER_SYS.stg_customer_pp REFRESH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce this done, we should see data is appearing in the target tables and the stream on these tables. As we'd expect, the number of rows in the streams are the same as in the tables. That's because we haven't yet consumed the content of the streams, and the tables had zero rows when we created the streams. Stay tuned!\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- DV: Inspecting Streams ------------------------------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\n\nSELECT 'DEV_LZ.TPCH_CUSTOMER_SYS.stg_customer', count(1) FROM DEV_LZ.TPCH_CUSTOMER_SYS.stg_customer\nUNION ALL\nSELECT 'DEV_DV.SALESMKT.stg_customer_strm', count(1) FROM DEV_DV.SALESMKT.stg_customer_strm\n;\n\nUSE ROLE CUSTSERV_ENGINEER;\n\nSELECT 'DEV_LZ.TPCH_ORDERS_SYS.stg_orders', count(1) FROM DEV_LZ.TPCH_ORDERS_SYS.stg_orders\nUNION ALL\nSELECT 'DEV_DV.CUSTSERV.stg_orders_strm', count(1) FROM DEV_DV.CUSTSERV.stg_orders_strm\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/matchingrowcounts.png?v=7a7bb18b\" alt=\"Matching Row Counts\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 5: DV - Preparing to Load the Raw Vault\u003C/h3\u003E\n","\u003Cp\u003EFinally, now that we established the basics and new data is logged in the stream, let's see how we can derive some of the business keys for the Data Vault entities we'll model. In this example, we'll model it as a view on top of the stream that should allow us to perform data parsing (raw_json -> columns) and business_key, hash_diff derivation on the fly.\u003C/p\u003E\n","\u003Cp\u003EAnother thing to notice here is the use of the SHA1_BINARY function in the hashing algorithm. There are many articles on choosing between MD5/SHA1(2)/other hash functions, so we won't focus on this. For this guide, we'll use fairly common SHA1 and its BINARY version from Snowflake arsenal of functions, using fewer bytes than a STRING. Note that our chosen delimiter is '^', '-1' represents our required null keys or missing descriptive data, '_hk' indicates a hash key, and '_hd' indicates a hash diff. While Data Vault 2.1 does not standardize a specific hashing algorithm or naming convention, the DV2.1 standards do state these must be defined and applied consistently across the implementation.\u003C/p\u003E\n","\u003Cp\u003EAfter creating the views, be sure to take a good look at the sample output.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- DV: Hard Business Rules and Hashing - Customer ------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DV;\nUSE SCHEMA SALESMKT;\n\nCREATE OR REPLACE VIEW stg_customer_strm_outbound AS \nSELECT src.*\n     , raw_json:C_CUSTKEY::NUMBER           c_custkey\n     , raw_json:C_NAME::STRING              c_name\n     , raw_json:C_ADDRESS::STRING           c_address\n     , raw_json:C_NATIONKEY::NUMBER         C_nationcode\n     , raw_json:C_PHONE::STRING             c_phone\n     , raw_json:C_ACCTBAL::NUMBER           c_acctbal\n     , raw_json:C_MKTSEGMENT::STRING        c_mktsegment\n     , raw_json:C_COMMENT::STRING           c_comment     \n--------------------------------------------------------------------\n-- derived hashes\n--------------------------------------------------------------------\n     , SHA1_BINARY(UPPER(TRIM(NVL(c_custkey,'-1'))))  AS  customer_hk\n     , SHA1_BINARY(UPPER(ARRAY_TO_STRING(ARRAY_CONSTRUCT( \n                                              NVL(TRIM(c_name)       ,'-1')\n                                            , NVL(TRIM(c_address)    ,'-1')              \n                                            , NVL(TRIM(c_nationcode) ,'-1')                 \n                                            , NVL(TRIM(c_phone)      ,'-1')            \n                                            , NVL(TRIM(c_acctbal)    ,'-1')               \n                                            , NVL(TRIM(c_mktsegment) ,'-1')                 \n                                            , NVL(TRIM(c_comment)    ,'-1')               \n                                            ), '^')))  AS customer_hd\n  FROM stg_customer_strm src\n;\n\nSELECT * FROM stg_customer_strm_outbound LIMIT 5;\n\n-- DV: Hard Business Rules and Hashing - Orders --------------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nCREATE OR REPLACE VIEW stg_order_strm_outbound AS \nSELECT src.*\n--------------------------------------------------------------------\n-- derived hashes\n--------------------------------------------------------------------\n     , SHA1_BINARY(UPPER(TRIM(o_orderkey)))                        AS order_hk\n     , SHA1_BINARY(UPPER(TRIM(o_custkey)))                         AS customer_hk\n     , SHA1_BINARY(UPPER(ARRAY_TO_STRING(ARRAY_CONSTRUCT( NVL(TRIM(o_orderkey)       ,'-1')\n                                                        , NVL(TRIM(o_custkey)        ,'-1')\n                                                        ), '^')))  AS customer_order_hk\n     , SHA1_BINARY(UPPER(ARRAY_TO_STRING(ARRAY_CONSTRUCT( NVL(TRIM(o_orderstatus)    , '-1')         \n                                                        , NVL(TRIM(o_totalprice)     , '-1')        \n                                                        , NVL(TRIM(o_orderdate)      , '-1')       \n                                                        , NVL(TRIM(o_orderpriority)  , '-1')           \n                                                        , NVL(TRIM(o_clerk)          , '-1')    \n                                                        , NVL(TRIM(o_shippriority)   , '-1')          \n                                                        , NVL(TRIM(o_comment)        , '-1')      \n                                                        ), '^')))  AS order_hd     \n  FROM DEV_DV.CUSTSERV.stg_orders_strm src\n;\n\nSELECT * FROM stg_order_strm_outbound LIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/customerstreamoutbound.png?v=7a7bb18b\" alt=\"Staged Customer Stream Outbound View\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDoes the output look good? Well done! We've built our staging/inbound pipeline, ready to accommodate streaming data with defined business keys, hash keys, and hash diffs that we'll use in our Raw Vault. Let's move on to the next step!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild: Data Vault - Raw Vault\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we will start building structures and pipelines for the \u003Cstrong\u003ERaw Vault\u003C/strong\u003E. This is the ERD of the objects we'll deploy:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/img16.png?v=7a7bb18b\" alt=\"Raw Vault ERD\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Raw Vault Tables\u003C/h3\u003E\n","\u003Cp\u003EWe'll start with DDL for the Hubs, Links and Satellites. This guide won't detail the Data Vault 2.1 model standards. For certified training in DV2.1, we highly recommend working with experts & partners from Data Vault Alliance. While primary and foreign keys are identified by constraints in the code below, it's important to remember that Snowflake does not enforce them.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- DV: Raw Vault - Sales & Marketing -------------------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.SALESMKT;\n\n-- Sales & Marketing: Region Reference\nCREATE OR REPLACE TABLE ref_region \n( \n  regioncode            NUMBER    NOT NULL\n, ldts                  TIMESTAMP NOT NULL\n, rsrc                  STRING    NOT NULL\n, r_name                STRING\n, r_comment             STRING\n, CONSTRAINT pk_ref_region        PRIMARY KEY (regioncode)                                                                             \n)\nAS \nSELECT r_regionkey\n     , ldts\n     , rsrc\n     , r_name\n     , r_comment\n  FROM DEV_LZ.TPCH_REF.stg_region;\n\n-- Sales & Marketing: Nation Reference\nCREATE OR REPLACE TABLE ref_nation \n( \n  nationcode            NUMBER    NOT NULL\n, regioncode            NUMBER    NOT NULL\n, ldts                  TIMESTAMP NOT NULL\n, rsrc                  STRING    NOT NULL\n, n_name                STRING\n, n_comment             STRING\n, CONSTRAINT pk_ref_nation        PRIMARY KEY (nationcode)                                                                             \n, CONSTRAINT fk_ref_region        FOREIGN KEY (regioncode) REFERENCES ref_region(regioncode)  \n)\nAS \nSELECT n_nationkey\n     , n_regionkey\n     , ldts\n     , rsrc\n     , n_name\n     , n_comment\n  FROM DEV_LZ.TPCH_REF.stg_nation;\n\n-- Sales & Marketing: Raw Vault Customer Hub\nCREATE OR REPLACE TABLE rv_hub_customer \n( \n  customer_hk             BINARY    NOT NULL   \n, customer_bk             NUMBER    NOT NULL                                                                                 \n, ldts                    TIMESTAMP NOT NULL\n, rsrc                    STRING    NOT NULL\n, CONSTRAINT pk_rv_hub_customer     PRIMARY KEY(customer_hk)\n);\n\n-- Sales & Marketing: Raw Vault Customer Satellite\nCREATE OR REPLACE TABLE rv_sat_customer \n( \n  customer_hk            BINARY    NOT NULL\n, ldts                   TIMESTAMP NOT NULL\n, rsrc                   STRING    NOT NULL  \n, customer_hd            BINARY    NOT NULL\n, c_name                 STRING\n, c_address              STRING\n, c_phone                STRING \n, c_acctbal              NUMBER\n, c_mktsegment           STRING    \n, c_comment              STRING\n, nationcode             NUMBER\n, CONSTRAINT pk_rv_sat_customer    PRIMARY KEY(customer_hk, ldts)\n, CONSTRAINT fk_rv_sat_customer    FOREIGN KEY(customer_hk) REFERENCES rv_hub_customer\n);\n\n-- DV: Raw Vault - Customer Service\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\n-- Customer Service: Raw Vault Order Hub\nCREATE OR REPLACE TABLE rv_hub_order \n( \n  order_hk                BINARY    NOT NULL   \n, order_bk                NUMBER    NOT NULL                                                                                 \n, ldts                    TIMESTAMP NOT NULL\n, rsrc                    STRING    NOT NULL\n, CONSTRAINT pk_rv_hub_order        PRIMARY KEY(order_hk)\n);\n\n-- Customer Service: Raw Vault Order Satellite\nCREATE OR REPLACE TABLE rv_sat_order \n( \n  order_hk               BINARY    NOT NULL   \n, ldts                   TIMESTAMP NOT NULL\n, rsrc                   STRING    NOT NULL   \n, order_hd               BINARY    NOT NULL\n, o_orderstatus          STRING   \n, o_totalprice           NUMBER\n, o_orderdate            DATE\n, o_orderpriority        STRING\n, o_clerk                STRING    \n, o_shippriority         NUMBER\n, o_comment              STRING\n, CONSTRAINT pk_rv_sat_order       PRIMARY KEY(order_hk, ldts)\n, CONSTRAINT fk_rv_sat_order       FOREIGN KEY(order_hk) REFERENCES rv_hub_order\n);\n\n-- Customer Service: Raw Vault Customer-Order Link\nCREATE OR REPLACE TABLE rv_lnk_customer_order\n(\n  customer_order_hk       BINARY       NOT NULL   \n, customer_hk             BINARY \n, order_hk                BINARY \n, ldts                    TIMESTAMP    NOT NULL\n, rsrc                    STRING       NOT NULL  \n, CONSTRAINT pk_rv_lnk_customer_order  PRIMARY KEY(customer_order_hk)\n, CONSTRAINT fk1_rv_lnk_customer_order FOREIGN KEY(customer_hk) REFERENCES DEV_DV.SALESMKT.rv_hub_customer\n, CONSTRAINT fk2_rv_lnk_customer_order FOREIGN KEY(order_hk)    REFERENCES rv_hub_order\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Continuous Loading into the Raw Vault Tables\u003C/h3\u003E\n","\u003Cp\u003ENow, we have source data waiting in our staging streams & views, and we have target Raw Vault tables ready.\u003C/p\u003E\n","\u003Cp\u003ELet's connect the dots. We'll create tasks, one task per stream, so when new records are available, that new data will be incrementally loaded to all dependent Raw Vault models in one operation. To achieve that, we'll use \u003Cstrong\u003Emulti-table insert\u003C/strong\u003E capability of Snowflake mentioned earlier. Tasks can be set up to run on a pre-defined frequency (every 1 minute in this guide) and use a dedicated virtual warehouse for compute. To minimize compute costs, before waking the warehouse, tasks will check for new data in the stream. We only pay for the compute we use.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Sales & Marketing: Raw Vault Customer System Data MTI -----------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE SCHEMA DEV_DV.SALESMKT;\n\nCREATE OR REPLACE TASK stg_customer_strm_tsk\n  WAREHOUSE = DEV_XFORM_WH\n  SCHEDULE = '1 minute'\nWHEN\n  SYSTEM$STREAM_HAS_DATA('DEV_DV.SALESMKT.STG_CUSTOMER_STRM')\nAS \nINSERT ALL\nWHEN (SELECT COUNT(1) FROM rv_hub_customer tgt WHERE tgt.customer_hk = src_customer_hk) = 0\nTHEN INTO rv_hub_customer  \n( customer_hk\n, customer_bk\n, ldts\n, rsrc\n)\nVALUES \n( src_customer_hk\n, src_customer_bk\n, src_ldts\n, src_rsrc\n)\nWHEN (SELECT COUNT(1) FROM rv_sat_customer tgt WHERE tgt.customer_hk = src_customer_hk AND tgt.customer_hd = src_customer_hd) = 0\nTHEN INTO rv_sat_customer  \n(\n  customer_hk  \n, ldts              \n, c_name            \n, c_address         \n, c_phone           \n, c_acctbal         \n, c_mktsegment      \n, c_comment         \n, nationcode        \n, customer_hd         \n, rsrc              \n)\nVALUES \n(\n  src_customer_hk\n, src_ldts              \n, src_c_name            \n, src_c_address         \n, src_c_phone           \n, src_c_acctbal         \n, src_c_mktsegment      \n, src_c_comment         \n, src_nationcode        \n, src_customer_hd         \n, src_rsrc              \n)\nSELECT customer_hk         src_customer_hk\n     , c_custkey           src_customer_bk\n     , c_name              src_c_name\n     , c_address           src_c_address\n     , c_nationcode        src_nationcode\n     , c_phone             src_c_phone\n     , c_acctbal           src_c_acctbal\n     , c_mktsegment        src_c_mktsegment\n     , c_comment           src_c_comment    \n     , customer_hd         src_customer_hd\n     , ldts                src_ldts\n     , rsrc                src_rsrc\n  FROM stg_customer_strm_outbound src\n;\n\n-- Customer Service: Raw Vault Orders System Data MTI --------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nCREATE OR REPLACE TASK stg_order_strm_tsk\n  WAREHOUSE = DEV_XFORM_WH\n  SCHEDULE = '1 minute'\nWHEN\n  SYSTEM$STREAM_HAS_DATA('DEV_DV.CUSTSERV.STG_ORDERS_STRM')\nAS \nINSERT ALL\nWHEN (SELECT COUNT(1) FROM rv_hub_order tgt WHERE tgt.order_hk = src_order_hk) = 0\nTHEN INTO rv_hub_order  \n( order_hk\n, order_bk\n, ldts\n, rsrc\n)  \nVALUES \n( src_order_hk\n, src_order_bk\n, src_ldts\n, src_rsrc\n)  \nWHEN (SELECT COUNT(1) FROM rv_sat_order tgt WHERE tgt.order_hk = src_order_hk AND tgt.order_hd = src_order_hd) = 0\nTHEN INTO rv_sat_order\n(\n  order_hk\n, ldts\n, o_orderstatus\n, o_totalprice\n, o_orderdate\n, o_orderpriority\n, o_clerk\n, o_shippriority\n, o_comment\n, order_hd\n, rsrc\n)  \nVALUES \n(\n  src_order_hk\n, src_ldts\n, src_o_orderstatus\n, src_o_totalprice\n, src_o_orderdate\n, src_o_orderpriority\n, src_o_clerk\n, src_o_shippriority\n, src_o_comment\n, src_order_hd\n, src_rsrc\n)\nWHEN (SELECT COUNT(1) FROM rv_lnk_customer_order tgt WHERE tgt.customer_order_hk = src_customer_order_hk) = 0\nTHEN INTO rv_lnk_customer_order  \n(\n  customer_order_hk\n, customer_hk\n, order_hk\n, ldts\n, rsrc\n)  \nVALUES \n(\n  src_customer_order_hk\n, src_customer_hk\n, src_order_hk\n, src_ldts\n, src_rsrc\n)\nSELECT order_hk                src_order_hk\n     , customer_order_hk       src_customer_order_hk\n     , customer_hk             src_customer_hk\n     , o_orderkey              src_order_bk\n     , o_orderstatus           src_o_orderstatus  \n     , o_totalprice            src_o_totalprice   \n     , o_orderdate             src_o_orderdate    \n     , o_orderpriority         src_o_orderpriority\n     , o_clerk                 src_o_clerk        \n     , o_shippriority          src_o_shippriority \n     , o_comment               src_o_comment      \n     , order_hd                src_order_hd\n     , ldts                    src_ldts\n     , rsrc                    src_rsrc\n  FROM stg_order_strm_outbound src\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Continuous Loading into the Raw Vault Tables\u003C/h3\u003E\n","\u003Cp\u003EWhen tasks are created, they are initially suspended; Resuming the tasks lets them automatically run on schedule. After resuming, we can look at the task execution history to see the process.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Customer Service: Resuming the Raw Vault MTI Loading Task -------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nALTER TASK stg_order_strm_tsk RESUME;\n\nSELECT * FROM table(information_schema.task_history()) ORDER BY scheduled_time DESC;\n\n-- Repeat the SELECT above over a few minutes to see the results over time.\n--   The row representing the next scheduled run will have a STATE of SCHEDULED.\n--   A STATE of FAILED means the task failed, and the ERROR_MESSAGE explains why.\n--   Repeated failures will result in a STATE of FAILED_AND_SUSPENDED.\n--   A STATE of SUCCEEDED means the task succeeded.\n--   A STATE of SKIPPED means the task didn't find any new data.\n\n-- Sales & Marketing: Resuming the Raw Vault MTI Loading Task ------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE SCHEMA DEV_DV.SALESMKT;\n\nALTER TASK stg_customer_strm_tsk RESUME;\n\nSELECT * FROM table(information_schema.task_history()) ORDER BY scheduled_time DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf we see SUCCEEDED, the task ran successfully!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/taskhistory.png?v=7a7bb18b\" alt=\"Task Details\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAll tasks can be monitored in one location by clicking on Transformation -> Tasks. Opening Task Details from there, or directly through the Horizon Catalog, shows us key details like the state of the task, its schedule, the condition in which it runs, and how many failures are attempted before auto-suspending. The Task Graph and Run History are just a tab-click away.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/taskdetails.png?v=7a7bb18b\" alt=\"Task Details\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 4: Verifying the Successful Loads\u003C/h3\u003E\n","\u003Cp\u003EWe can use the SQL below to check the row counts in our Raw Vault tables, and our two outbound streams.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Verifying the Loads ---------------------------------------------------------\nSELECT 'rv_hub_customer', count(1) FROM DEV_DV.SALESMKT.rv_hub_customer\nUNION ALL\nSELECT 'rv_hub_order', count(1) FROM DEV_DV.CUSTSERV.rv_hub_order\nUNION ALL\nSELECT 'rv_sat_customer', count(1) FROM DEV_DV.SALESMKT.rv_sat_customer\nUNION ALL\nSELECT 'rv_sat_order', count(1) FROM DEV_DV.CUSTSERV.rv_sat_order\nUNION ALL\nSELECT 'rv_lnk_customer_order', count(1) FROM DEV_DV.CUSTSERV.rv_lnk_customer_order\nUNION ALL\nSELECT 'stg_customer_strm_outbound', count(1) FROM DEV_DV.SALESMKT.stg_customer_strm_outbound\nUNION ALL\nSELECT 'stg_order_strm_outbound', count(1) FROM DEV_DV.CUSTSERV.stg_order_strm_outbound\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe views on streams in our staging area no longer return any rows, and as a result, the tasks simply wait to be triggered on the stream having data again. We never deleted rows or truncated a table. The prior querying of the views did not consume the contents of the stream. The task paired with the stream automatically consumed the rows of the stream. The multi-table insert allowed us to load \u003Cstrong\u003Efive\u003C/strong\u003E target tables with just \u003Cstrong\u003Etwo\u003C/strong\u003E stream / task combinations. Do we need to spend any more time implementing incremental detection and processing logic on the application side? No.\u003C/p\u003E\n","\u003Cp\u003ENotice that we didn't use two different roles when selecting rows from the two domains. While only the specific domain-oriented role is able to perform \u003Cstrong\u003Ecreate and write\u003C/strong\u003E actions, \u003Cstrong\u003Ecross-domain reading and referencing\u003C/strong\u003E can be performed. We intentionally designed this in the \u003Ca href=\"https://www.snowflake.com/en/developers/guides/defensible-analytics-using-data-vault-and-snowflake/\"\u003EDefensible Analytics using Data Vault and Snowflake\u003C/a\u003E guide. Those with dominion over Sales & Marketing govern the data, while those operating in other domains are able to use that data, eliminating duplication of effort and establishing clear data lineage and accountability.\u003C/p\u003E\n","\u003Cp\u003EWe now have data in our \u003Cstrong\u003ERaw Vault\u003C/strong\u003E. Let's move on and consider virtualization in our continuous Data Vault solution.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EViews for Agile Reporting\u003C/h2\u003E\n","\u003Cp\u003EOne of the great benefits of Snowflake is that it is now possible to default our Business Vault and Information Mart objects as views. Numerous customers use this approach in production today. No longer is there a need to argue that there are “too many joins” or that the response "won’t be fast enough." The elasticity of the Snowflake virtual warehouses combined with our dynamic optimization engine have solved that problem. For more detail, see this \u003Ca href=\"/blog/tips-for-optimizing-the-data-vault-architecture-on-snowflake-part-3/\"\u003Epost\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EWhen we want to deliver data to the business users and data scientists in near real time, using views is the best option. Once we have the streaming loads built to feed our Data Vault, the fastest way to make that data visible downstream will be views. Views allow us to deliver the data faster by eliminating any latency that would be incurred by ELT processes between the Data Vault and the data consumers downstream.\u003C/p\u003E\n","\u003Cp\u003EAll the business logic, alignment, and formatting of the data can be coded into the view. That means fewer moving parts to debug, no sequential downstream latency, and minimized storage costs.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/datavaultvirtualization.png?v=7a7bb18b\" alt=\"Data Vault Virtualization Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELooking at the diagram above, we see an example of how virtualization fits in the architecture. Here, solid lines represent physical tables and dotted lines represent views. We incrementally ingest data into the \u003Cstrong\u003ERaw Vault\u003C/strong\u003E, and downstream transformations are applied in views. From a data consumer's perspective when working with a virtualized information mart, the query has access to the full, up-to-date enterprise memory, at the point in time the query is submitted.\u003C/p\u003E\n","\u003Cp\u003EWith Snowflake, we have the ability to provide as much compute as required, on-demand, without a risk of performance impact on any surrounding processes, paying only for what we use. This makes materialization of transformations in the \u003Cstrong\u003EBusiness Vault\u003C/strong\u003E or other \u003Cstrong\u003EInformation Delivery\u003C/strong\u003E objects optional, rather than required. Instead of “optimizing upfront,” we can now make this decision based on observed usage pattern characteristics, such as frequency of use, type of queries, latency requirements, readiness of the requirements, etc. As always, the Business Vault is sparsely built, maximizing the amount of work not done, allowing us to focus on decision effectiveness and delivering business outcomes.\u003C/p\u003E\n","\u003Cp\u003EMany modern data engineering automation tools already support virtualization of logic. Several tools offer a low-code or configuration-like ability to switch between materializing an object as a view or a physical table, automatically generating all required DDL & DML. This could be applied to specific objects, zones, or be environment specific. So even if we start with a view, we can easily refactor to using a table, as requirements evolve.\u003C/p\u003E\n","\u003Cp\u003EVirtualization is not only a way to improve time-to-value and provide near real time access to the data. Given the scalability and workload isolation of Snowflake, virtualization is a design technique that could make your Data Vault excel: minimizing cost-of-change, accelerating the time-to-delivery and becoming an extremely agile, future proof solution for ever changing business needs.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild: Data Vault - Business Vault\u003C/h2\u003E\n","\u003Cp\u003ENext, we'll demonstrate enrichment of descriptive data in the Business Vault.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Expanding Customer Detail\u003C/h3\u003E\n","\u003Cp\u003ELet's create a view that will perform additional derivations on the fly. Assuming non-functional capabilities are satisfying our requirements, deploying transformations (and re-deploying new versions) in this way is super easy.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Business Vault: Expanding Customer with Nation and Region Name\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.SALESMKT;\n\nCREATE OR REPLACE VIEW bv_sat_customer\nAS\nSELECT rsc.customer_hk\n     , rsc.ldts                   \n     , rsc.c_name                 \n     , rsc.c_address              \n     , rsc.c_phone                 \n     , rsc.c_acctbal              \n     , rsc.c_mktsegment               \n     , rsc.c_comment              \n     , rsc.nationcode             \n     , rsc.rsrc \n     -- derived \n     , rrn.n_name        nation_name\n     , rrr.r_name        region_name\n  FROM rv_sat_customer rsc\n  LEFT OUTER JOIN ref_nation rrn\n    ON (rsc.nationcode = rrn.nationcode)\n  LEFT OUTER JOIN ref_region rrr\n    ON (rrn.regioncode = rrr.regioncode)\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Expanding Order Detail\u003C/h3\u003E\n","\u003Cp\u003ENow, let's imagine we have a heavier transformation to perform on our order data. It could be more data volume, could be more complex logic, PITs, bridges or even an object that will be used frequently and by many users.\u003C/p\u003E\n","\u003Cp\u003ESnowflake provides \u003Ca href=\"https://docs.snowflake.com/en/user-guide/overview-view-mview-dts\"\u003Emultiple options\u003C/a\u003E in these situations:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA new bv_sat_order Table, loaded through the use of another Stream / Task combination, where the new Task is triggered to run immediately after stg_order_strm_tsk. This option is available on all Snowflake Editions.\u003C/li\u003E\u003Cli\u003EA new bv_sat_order \u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-about\"\u003EDynamic Table\u003C/a\u003E. This simplifies the implementation significantly as compared to using a Stream and Task, using a definition similar to a CTAS statement, with a warehouse specified and a target lag. This option is also available on all Snowflake Editions.\u003C/li\u003E\u003Cli\u003EA new bv_sat_order \u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-materialized\"\u003EMaterialized View\u003C/a\u003E. This also simplifies the implementation dramatically, being defined just as you would a view. However, this option requires Enterprise Edition or above, and is limited to a query of a single table.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor this example, let's consider a soft business rule where orders with an Urgent or High priority, and having a total price of 200000 or more, are considered Tier-1. Urgent, High, or Medium and between 150000 and 200000 are Tier-2, and the rest Tier-3. We first try building this into a view, but find that performance is poor, due to regular querying with a filter set to just Tier-1 orders. We decide for performance reasons to materialize this, and we want it updated immediately, with no delay.\u003C/p\u003E\n","\u003Cp\u003EIn this particular case, a Materialized View could be used, because the view would query a single table, and assuming we're using Enterprise Edition. Given the underlying table is insert-only, auto-refresh will be incremental (and thus cheap). And because there is only a simple CASE derivation, with no joins, aggregates, or window functions, a Materialized View could be an ideal choice. A Dynamic Table would be much less limited, but would introduce at least some target lag between the raw vault and business vault structures (and hey, this is a near-real-time data vault we're building). So, we'll stick with the Streams & Tasks method. Feel free to experiment with the other options, if desired.\u003C/p\u003E\n","\u003Cp\u003ELet's first build a new business vault satellite, using our existing data. We'll first suspend our raw vault order loading, so we don't miss any data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Business Vault: Expanding Order with Order Priority Bucket (soft business rule)\nUSE ROLE CUSTSERV_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\n-- Suspend loading rv_sat_order (and verify it's not currently running)\nALTER TASK stg_order_strm_tsk SUSPEND;\n\nSELECT * FROM table(information_schema.task_history()) ORDER BY scheduled_time DESC;\n\n-- Initialize the new bv_sat_order table\nCREATE OR REPLACE TABLE bv_sat_order\n( \n  order_hk               BINARY    NOT NULL   \n, ldts                   TIMESTAMP NOT NULL\n, o_orderstatus          STRING   \n, o_totalprice           NUMBER\n, o_orderdate            DATE\n, o_orderpriority        STRING\n, o_clerk                STRING    \n, o_shippriority         NUMBER\n, o_comment              STRING  \n, order_hd               BINARY    NOT NULL\n, rsrc                   STRING    NOT NULL   \n-- additional attributes\n, order_priority_bucket  STRING\n, CONSTRAINT pk_bv_sat_order PRIMARY KEY(order_hk, ldts)\n, CONSTRAINT fk_bv_sat_order FOREIGN KEY(order_hk) REFERENCES rv_hub_order\n)\nAS \nSELECT order_hk\n     , ldts\n     , o_orderstatus\n     , o_totalprice\n     , o_orderdate\n     , o_orderpriority\n     , o_clerk\n     , o_shippriority\n     , o_comment\n     , order_hd\n     , rsrc\n     -- derived additional attributes\n     , CASE WHEN o_orderpriority IN ('2-HIGH', '1-URGENT')             AND o_totalprice >= 200000 THEN 'Tier-1'\n            WHEN o_orderpriority IN ('3-MEDIUM', '2-HIGH', '1-URGENT') AND o_totalprice BETWEEN 150000 AND 200000 THEN 'Tier-2'  \n            ELSE 'Tier-3'\n       END order_priority_bucket\nFROM rv_sat_order;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Loading the Expanded Order Detail\u003C/h3\u003E\n","\u003Cp\u003EFrom a processing and orchestration perspective, we will extend our order processing pipeline so that when the task populates \u003Cstrong\u003Erv_sat_order\u003C/strong\u003E, this will generate a new stream of changes, and those changes will be propagated by a dependent task to \u003Cstrong\u003Ebv_sat_order\u003C/strong\u003E. Tasks in Snowflake can be not only schedule-based but also start automatically once a parent task completes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Business Vault: Loading Order with Order Priority Bucket (soft business rule)\nUSE ROLE CUSTSERV_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\n-- Create a new stream\nCREATE OR REPLACE STREAM rv_sat_order_strm ON TABLE rv_sat_order;\n\n-- Create the new task to consume the stream\nCREATE OR REPLACE TASK rv_sat_order_strm_tsk\n  WAREHOUSE = DEV_XFORM_WH\n  AFTER stg_order_strm_tsk\nAS\nINSERT INTO bv_sat_order\nSELECT\n  order_hk\n, ldts\n, o_orderstatus\n, o_totalprice\n, o_orderdate\n, o_orderpriority\n, o_clerk\n, o_shippriority\n, o_comment\n, order_hd\n, rsrc\n-- derived additional attributes\n, CASE WHEN o_orderpriority IN ('2-HIGH', '1-URGENT')             AND o_totalprice >= 200000 THEN 'Tier-1'\n       WHEN o_orderpriority IN ('3-MEDIUM', '2-HIGH', '1-URGENT') AND o_totalprice BETWEEN 150000 AND 200000 THEN 'Tier-2'\n       ELSE 'Tier-3'\n  END order_priority_bucket\nFROM rv_sat_order_strm;\n\n-- Resume the new and old task\nALTER TASK rv_sat_order_strm_tsk RESUME;\nALTER TASK stg_order_strm_tsk RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4: Testing the Expanded Order Detail Load\u003C/h3\u003E\n","\u003Cp\u003ENow, let's go back to our staging area to process another slice of data to test the task.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LZ: Simulating More Orders System Data --------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\nUSE SCHEMA DEV_LZ.TPCH_ORDERS_SYS;\n\nCOPY INTO @orders_data \nFROM\n(SELECT *\n  FROM snowflake_sample_data.tpch_sf10.orders LIMIT 1000 OFFSET 1000\n) \nINCLUDE_QUERY_ID=TRUE;\n\nALTER PIPE stg_orders_pp REFRESH;\n\nSELECT 'DEV_LZ.TPCH_ORDERS_SYS.stg_orders', count(1) FROM DEV_LZ.TPCH_ORDERS_SYS.stg_orders;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 5: Verifying the Expanded Order Detail Load\u003C/h3\u003E\n","\u003Cp\u003EData is now automatically flowing through all the layers via asynchronous tasks. With the results you can validate:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Verifying Full Business Vault Order Satellite Load --------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nSELECT *\n  FROM table(information_schema.task_history())\n  ORDER BY scheduled_time DESC\n;\n-- Repeat the above as needed to see the two tasks succeed...\n\nSELECT 'DEV_DV.CUSTSERV.stg_orders_strm', count(1) FROM DEV_DV.CUSTSERV.stg_orders_strm\nUNION ALL\nSELECT 'DEV_DV.CUSTSERV.rv_sat_order', count(1) FROM DEV_DV.CUSTSERV.rv_sat_order\nUNION ALL\nSELECT 'DEV_DV.CUSTSERV.rv_sat_order_strm', count(1) FROM DEV_DV.CUSTSERV.rv_sat_order_strm\nUNION ALL\nSELECT 'DEV_DV.CUSTSERV.bv_sat_order', count(1) FROM DEV_DV.CUSTSERV.bv_sat_order;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAssuming the tasks completed, we should now see 0 rows in the streams, and 2000 rows in the tables.\u003C/p\u003E\n","\u003Cp\u003ENow that we have \u003Cstrong\u003EBusiness Vault\u003C/strong\u003E objects complete, let's move into the \u003Cstrong\u003EInformation Delivery\u003C/strong\u003E zone.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild: Information Delivery\u003C/h2\u003E\n","\u003Cp\u003EWhen it comes to Information Delivery zone we are not changing the meaning of data, but we may change format to simplify users to access and work with the data products/output interfaces. Different consumers may have different needs and preferences, some would prefer star/snowflake dimensional schemas, some would adhere to use flattened objects or even transform data into JSON/parquet objects.\u003C/p\u003E\n","\u003Ch2\u003EStep 1: More Supporting Views in the Business Vault\u003C/h2\u003E\n","\u003Cp\u003ETo simplify working with satellites, let's create views that show the latest version for each key.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- BV: Sales & Marketing Current Views -----------------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.SALESMKT;\n\nCREATE OR REPLACE VIEW rv_sat_customer_current\nAS\nSELECT  *\nFROM    rv_sat_customer\nQUALIFY LEAD(ldts) OVER (PARTITION BY customer_hk ORDER BY ldts) IS NULL;\n\nCREATE OR REPLACE VIEW bv_sat_customer_current\nAS\nSELECT  *\nFROM    bv_sat_customer\nQUALIFY LEAD(ldts) OVER (PARTITION BY customer_hk ORDER BY ldts) IS NULL;\n\n-- BV: Customer Service Current Views ------------------------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nCREATE OR REPLACE VIEW rv_sat_order_current\nAS\nSELECT  *\nFROM    rv_sat_order\nQUALIFY LEAD(ldts) OVER (PARTITION BY order_hk ORDER BY ldts) IS NULL;\n\nCREATE OR REPLACE VIEW bv_sat_order_current\nAS\nSELECT  *\nFROM    bv_sat_order\nQUALIFY LEAD(ldts) OVER (PARTITION BY order_hk ORDER BY ldts) IS NULL;\n\n-- Note that the patterns in the views above, while correct, can be expensive for very large satellites. Point-in-Time (PIT) tables could be a performance-enhancing option.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Type 1 Dimensions and a Fact Table\u003C/h3\u003E\n","\u003Cp\u003ELet's create a simple dimensional structure. Again, we will keep it virtual (as views) to start with, but we already know that any of these could be materialized.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Sales & Marketing Type 1 Dimension: Customer --------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DW.SALESMKT;\n\nCREATE OR REPLACE VIEW dim1_customer \nAS \nSELECT hub.customer_bk                            AS customer_id\n     , sat.c_name\n     , sat.c_address\n     , sat.c_phone\n     , sat.c_acctbal\n     , sat.c_mktsegment\n     , sat.c_comment\n     , sat.nation_name\n     , sat.region_name\n     , sat.ldts                                   AS effective_dts\n     , sat.rsrc                                   AS record_source\n  FROM DEV_DV.SALESMKT.rv_hub_customer            hub\n  JOIN DEV_DV.SALESMKT.bv_sat_customer_current    sat ON hub.customer_hk = sat.customer_hk;\n\n-- Customer Service Type 1 Dimension: Order ------------------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DW.CUSTSERV;\n\nCREATE OR REPLACE VIEW dim1_order\nAS \nSELECT hub.order_bk                               AS order_id\n     , sat.o_orderstatus\n     , sat.o_totalprice\n     , sat.o_orderdate\n     , sat.o_orderpriority\n     , sat.o_clerk\n     , sat.o_shippriority\n     , sat.o_comment\n     , sat.order_priority_bucket\n     , sat.ldts                                   AS effective_dts\n     , sat.rsrc                                   AS record_source\n  FROM DEV_DV.CUSTSERV.rv_hub_order               hub\n  JOIN DEV_DV.CUSTSERV.bv_sat_order_current       sat ON hub.order_hk = sat.order_hk;\n\n-- Customer Service Fact: Customer Order Details -------------------------------\nCREATE OR REPLACE VIEW fct_customer_order\nAS\nSELECT hub_c.customer_bk                          AS customer_id\n     , hub_o.order_bk                             AS order_id\n     , lnk.ldts                                   AS effective_dts     \n     , lnk.rsrc                                   AS record_source\n-- this is a factless fact, but here you can add any measures, calculated or derived\n  FROM DEV_DV.CUSTSERV.rv_lnk_customer_order      lnk\n  JOIN DEV_DV.SALESMKT.rv_hub_customer            hub_c ON lnk.customer_hk = hub_c.customer_hk\n  JOIN DEV_DV.CUSTSERV.rv_hub_order               hub_o ON lnk.order_hk    = hub_o.order_hk;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Testing the Information Delivery Views\u003C/h3\u003E\n","\u003Cp\u003EAll good so far? Now let's act as the QA Analyst, and try querying \u003Cstrong\u003Efct_customer_order\u003C/strong\u003E ...\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- QA Check --------------------------------------------------------------------\nUSE ROLE QA_ANALYST;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DW;\n\nSELECT 'Order Fact Count', COUNT(1) FROM DEV_DW.CUSTSERV.fct_customer_order; -- How many orders?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe loaded 2000 rows of orders... right? Does the SELECT statement above give us all 2000 orders? Or closer to none?\u003C/p\u003E\n","\u003Cp\u003EIf we recall, when we were unloading sample data, we took a subset of orders and a subset of customers. These samples might have no overlap at all! Therefore, the inner joins could result in all rows being eliminated.\u003C/p\u003E\n","\u003Cp\u003EThankfully, this is a Data Vault. All we need to do is land the full customer dataset back in the Landing Zone. Just think about it, there is no need to reprocess any links or fact tables simply because the customer feed was originally incomplete. Those who have used other architectures for data warehouses likely have painful experiences of such situations in the past. So, let's see how easy it is to fix it!\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- All the Customers -----------------------------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\nUSE SCHEMA DEV_LZ.TPCH_CUSTOMER_SYS;\n\nCOPY INTO @customer_data \nFROM\n(SELECT object_construct(*)\n  FROM snowflake_sample_data.tpch_sf10.customer\n  -- removed LIMIT 10\n) \nINCLUDE_QUERY_ID=TRUE;\n\nALTER PIPE stg_customer_pp   REFRESH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll we need to do is wait, and in a minute, our continuous data pipeline will automatically propagate new customer data into the Data Vault. Late arriving data? No problem.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- QA Check Again --------------------------------------------------------------\nUSE ROLE QA_ANALYST;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DW;\n\nSELECT 'Customer Dimension Count', COUNT(1) FROM DEV_DW.SALESMKT.dim1_customer; -- How many customers now?\nSELECT 'Order Fact Count', COUNT(1) FROM DEV_DW.CUSTSERV.fct_customer_order; -- How many orders now?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally let's put on the analyst's hat and run a query to break down orders by nation, region, and order_priority_bucket (all attributes we derived in the \u003Cstrong\u003EBusiness Vault\u003C/strong\u003E).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE QA_ANALYST;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DW;\n\nSELECT dc.nation_name\n     , dc.region_name\n     , do.order_priority_bucket\n     , COUNT(1)                                   cnt_orders\n     , SUM(o_totalprice)                          total_price\n     , FLOOR(total_price / cnt_orders)            avg_price\n  FROM CUSTSERV.fct_customer_order                fct\n     , SALESMKT.dim1_customer                     dc\n     , CUSTSERV.dim1_order                        do\n WHERE fct.customer_id = dc.customer_id\n   AND fct.order_id = do.order_id\nGROUP BY 1,2,3\nORDER BY 3, 4 DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs we are using Snowsight, we can quickly create a chart from this result set to better understand the data. For this, simply click on the 'Chart' section on the bottom pane.\u003C/p\u003E\n","\u003Cp\u003EFinally, to avoid consuming any additional credits after completion, be sure to suspend the tasks.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE CUSTSERV_ENGINEER;\nALTER TASK DEV_DV.CUSTSERV.stg_order_strm_tsk SUSPEND;\nALTER TASK DEV_DV.CUSTSERV.rv_sat_order_strm_tsk SUSPEND;\n\nUSE ROLE SALESMKT_ENGINEER;\nALTER TASK DEV_DV.SALESMKT.stg_customer_strm_tsk SUSPEND;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVoila! This concludes our journey for this guide. We hope you enjoyed it. Let's summarize key points in the next section.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ESimplicity of engineering, openness, scalable performance, enterprise-grade governance enabled by the core of the Snowflake platform are now allowing teams to focus on what matters most for the business and build truly agile, collaborative data environments. Teams can now connect data from all parts of the landscape, until there are no stones left unturned. They are even tapping into new datasets via live access to the Snowflake Marketplace. The Snowflake Data Cloud combined with a Data Vault 2.1 approach is allowing teams to democratize access to all their data assets at any scale. We can now easily derive more and more value through insights and intelligence, day after day, bringing businesses to the next level of being truly data-driven.\u003C/p\u003E\n","\u003Cp\u003EDelivering more usable data faster is no longer an option for today’s business environment. Using the Snowflake platform, combined with the Data Vault 2.1 architecture it is now possible to build a world class analytics platform that delivers data for all users in near real-time.\u003C/p\u003E\n","\u003Ch3\u003EWhat we've covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUnloading and loading back data using COPY and Snowpipe\u003C/li\u003E\u003Cli\u003EEngineering data pipelines using virtualization, streams and tasks\u003C/li\u003E\u003Cli\u003EPopulating data in a multi-zone Data Vault on Snowflake:\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/img19.png?v=7a7bb18b\" alt=\"Multi-Zone Data Vault Environment\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECall to action\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWe made examples limited in size, but feel free to scale the data volumes and virtual warehouse size to see scalability in action.\u003C/li\u003E\u003Cli\u003ECould we make the data vault perform faster by joining on business keys and not hash keys? Check out \u003Ca href=\"/blog/tips-for-optimizing-the-data-vault-architecture-on-snowflake-part-3/\"\u003Ethis great blog post\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003ECould we use triggered, serverless tasks, eliminating the need to poll the source? Check out \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-triggered\"\u003Eserverless triggered tasks\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003ETap into the numerous communities highlighting Snowflake and Data Vault.\u003C/li\u003E\u003Cli\u003ETalk to us about modernizing your data landscape! We have the expertise and product to meet your needs.\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Building a Real-Time Data Vault in Snowflake",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Continuous Data\n\nToday, with the ever-increasing availability and volume of data from many types of sources such as IoT, mobile devices, and weblogs, there is a growing need, and yes, demand, to go from batch load processes to streaming continuous real-time (RT) data. Businesses change at a rapid rate, becoming more competitive all the time. Those that can harness the value of their data faster to drive better decision making and better business outcomes will prevail.\n\nOne of the benefits of using the Data Vault system is that it was designed from inception not only to accept data loaded using traditional batch mode (which was the prevailing mode in the early 2000s when [Dan Linstedt](https://datavaultalliance.com/#about) introduced Data Vault) but also to easily accept data loading continuously in real or near-realtime (NRT). In the early 2000s, that was a nice-to-have aspect of the approach and meant the methodology was effectively future-proofed from that perspective. Still, few relational database management systems had the ability to support that kind of requirement. Today, RT or at least NRT loading is almost becoming a mandatory requirement for modern data platforms. Granted, not all loads or use cases need to be NRT, but most forward-thinking organizations need to onboard data for analytics in an NRT manner.\n\nThose who have been using the Data Vault approach don’t need to change much other than figure out how to engineer their data pipeline to serve up data to the Data Vault in NRT. The data models don’t need to change; the reporting views don’t need to change; even the loading patterns don’t need to change. For those that aren’t using Data Vault already, if they have real-time loading requirements, this architecture and method might be worth considering.\n\n### Data Vault on Snowflake\n\nThere have been numerous [blog posts](/blog/tips-for-optimizing-the-data-vault-architecture-on-snowflake/), user groups, and webinars over the years, discussing the best practices and customer success stories of implementing Data Vaults on Snowflake.  But, how do we build a Data Vault on Snowflake that has real-time or near real-time continuous data streaming into it?\n\nLuckily, streaming data is one of the [use-cases](/cloud-data-platform/) that Snowflake was built to support, so we have many features to help us achieve this goal. **This guide is an extended version of the [article](https://datavaultalliance.com/news/building-a-real-time-data-vault-in-snowflake/) posted on the Data Vault Alliance website, with practical steps to build an example of a real-time Data Vault on Snowflake. Join us for simple-to-follow steps to see it in action.**\n\n### Prerequisites\n- Familiarity with [Snowflake key concepts and architecture](https://docs.snowflake.com/en/user-guide/intro-key-concepts)\n- Familiarity with [Data Vault methodology and architecture](https://datavaultalliance.com/#resources)\n\n### What You’ll Learn\n- How to use Data Vault modeling on Snowflake\n- How to build basic objects and write ELT code for them\n- How to leverage [Snowpipe](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro) and [Continuous Data Pipelines](https://docs.snowflake.com/en/user-guide/data-pipelines) to automate data processing\n- How to apply data virtualization to accelerate data access\n\n### What You’ll Need \n- A Snowflake account -- we recommend starting with a [trial](https://trial.snowflake.com/) account\n- Architectural objects created through the [Defensible Analytics using Data Vault and Snowflake](https://www.snowflake.com/en/developers/guides/defensible-analytics-using-data-vault-and-snowflake/) guide. If you haven't used this guide to set up foundational architecture in your account, you'll need to alter the references to roles, databases, schemas, warehouses, and other Snowflake objects in order to work with your specific account design.\n\n\n### What You’ll Build \n- Data Vault models on Snowflake, based on sample dataset \n- Data pipelines, leveraging streams, tasks and Snowpipe\n\n\n\u003C!-- ------------------------ --\u003E\n## Reference Architecture and Environment Setup\n\nThis is the target architecture. \n\n![Multi-Tier Data Vault Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/multitierdatavaultarchitecture.png?v=7a7bb18b)  \n\nIn the [Defensible Analytics using Data Vault and Snowflake](https://www.snowflake.com/en/developers/guides/defensible-analytics-using-data-vault-and-snowflake/) guide, we implement components of this architecture, which we'll use in this guide. If you haven't already executed the steps in that guide in your Snowflake account, you'll need to do that now, or change the examples given in this guide to accommodate your design.\n\n\n\u003C!-- ------------------------ --\u003E\n## Data Pipelines: Design\n\n![Continuous Data Pipeline Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/continuousdatapipelinearchitecture.png?v=7a7bb18b)  \n\nSnowflake supports multiple options for engineering data pipelines. In this guide, we'll demonstrate one of the most efficient ways to implement incremental continuous data integration with Snowflake [Continuous Data Pipelines](https://docs.snowflake.com/en/user-guide/data-pipelines). Let's take a look at the architecture diagram above to understand how it works. \n\nWe'll use [stream](https://docs.snowflake.com/en/user-guide/streams) objects to track data inserts into our Landing Zone tables. This process is automatic, and unlike a traditional RDBMS, this change-tracking will never impact the speed of data loading. The change data captured in a stream is automatically consumed once there is a successfully completed DML operation using the stream object as a source. After loading new data into a table, the changes are immediately reflected in the stream showing the [delta](https://docs.snowflake.com/en/user-guide/streams#data-flow) that requires processing.\n\nWe'll then use [tasks](https://docs.snowflake.com/en/user-guide/tasks-intro). Tasks are a powerful way to automate data processing, waking up on a defined schedule (here, every 1 minute), checking for new data in the associated stream, and if so, they execute SQL, pushing into the Raw Vault objects. Tasks can be arranged in a [tree-like dependency graph](https://docs.snowflake.com/en/user-guide/tasks-intro#simple-tree-of-tasks), executing child tasks the moment the predecessor finished its part. \n\nFollowing Data Vault best practices for continuous data integration -- loading data in parallel -- we'll use Snowflake’s [multi-table insert (MTI)](https://docs.snowflake.com/en/sql-reference/sql/insert-multi-table) capability in our tasks to populate multiple Raw Vault objects in our Enterprise Memory Zone using a single DML transaction. These tasks utilize scalable virtual warehouses, which means we always have enough [compute power](https://docs.snowflake.com/en/user-guide/warehouses-overview#warehouse-size) (from XS to 6XL) to handle any workload, whilst the [multi-cluster virtual warehouse](https://docs.snowflake.com/en/user-guide/warehouses-multicluster#multi-cluster-warehouses) option will automatically scale-out and load balance all the tasks as more hubs, links and satellites are introduced.\n\nFinally, as our Raw Vault is updated, streams will be used to propagate changes to Business Vault objects (such as derived Satellites, or PITs and Bridges, if needed for performance) in the Information Delivery Zone. This setup can be repeated, continuously transforming data through in small increments, quickly and efficiently. Virtualized facts and dimensions are delivered to data consumers, with a truly agile data pipeline backing them up.\n\nFollowing this approach will result in a fully automated, reliable and defensible production data pipeline that powers decisions based on the latest data available.\n\n\n\u003C!-- ------------------------ --\u003E\n## Build: Landing Zone, Sample Data and Staging for the Raw Vault\n\nEvery new Snowflake trial account includes access to [sample data sets](https://docs.snowflake.com/en/user-guide/sample-data), and we can find these in the SNOWFLAKE_SAMPLE_DATA database. For this guide, we'll use a subset of objects from [TPC-H](https://docs.snowflake.com/en/user-guide/sample-data-tpch) set, representing **customers** and their **orders**. We'll also store reference data about **nations** and **regions**. \n\n| Dataset | System | Specific Source | Load Scenario | Mechanism |\n|---------|-------------|--------|---------------|-----------|\n| Nation | Static Reference | snowflake.sample_data.tpch_sf10.nation | one-off CTAS | SQL |\n| Region | Static Reference | snowflake.sample_data.tpch_sf10.region | one-off CTAS | SQL |\n| Customer | Customer System | snowflake.sample_data.tpch_sf10.customer | incremental JSON files | Snowpipe |\n| Orders | Orders System | snowflake.sample_data.tpch_sf10.orders | incremental CSV files | Snowpipe |\n\n\u003E In the real world, we rarely have a singular source system with all our data. So, even though our sample dataset comes from a single Snowflake share, we'll act as if it comes from three different source systems, and simulate the customer detail comes in the form of a semi-structured raw JSON object.\n\n### Step 1: LZ - Static Reference Data\n\nLet's start with the static reference data, loading that with simple one-off CTAS (CREATE TABLE ... AS SELECT ...) statements. If you've just completed the [Defensible Analytics using Data Vault and Snowflake](https://www.snowflake.com/en/developers/guides/defensible-analytics-using-data-vault-and-snowflake/) guide, create a new SQL file in Workspaces named DVRealTime.sql.\n\n\u003E **Data Vault audit columns:** Every entity in this guide carries two mandatory audit columns. `ldts` (*Load Date Time Stamp*) is the immutable timestamp of when the row was loaded into the vault — set once on arrival and never changed, with meaning only to the data vault. This is not when the data originated, but when loaded in from the source. `rsrc` (*Record Source*) identifies the originating system or feed. Together they ensure every record is fully traceable: you can always answer *when* did this arrive and *where* did it come from, satisfying both auditability and compliance requirements.\n\n```sql\n-- LZ: Static Reference Data ---------------------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\nUSE DATABASE DEV_LZ;\nUSE SCHEMA TPCH_REF;\n\nCREATE OR REPLACE TABLE stg_nation \nAS \nSELECT src.*\n     , CURRENT_TIMESTAMP()    ldts \n     , 'Static Reference'     rsrc \n  FROM snowflake_sample_data.tpch_sf10.nation src\n;\n\nCREATE OR REPLACE TABLE stg_region\nAS \nSELECT src.*\n     , CURRENT_TIMESTAMP()    ldts \n     , 'Static Reference'     rsrc \n  FROM snowflake_sample_data.tpch_sf10.region src\n;\n-- Note that we're using OR REPLACE here in the guide for ease of correcting mistakes. In a production landing zone, data should be immutable.\n```\n\n### Step 2: LZ - Raw Customer and Order Data\n\nNext, let's create staging tables for our future data pipeline. This syntax should be familiar to most. It is ANSI SQL compliant DDL, but with a key exception - for stg_customer we'll load the full payload of JSON into the raw_json column using the data type [VARIANT](https://docs.snowflake.com/en/sql-reference/data-types-semistructured.html). We'll also enable change tracking, enabling our future use of streams.\n\nAs we load data, we'll also add some technical metadata, like the row number in a file. \n\n```sql\n-- LZ: Customer System ---------------------------------------------------------\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nCREATE OR REPLACE TABLE stg_customer\n(\n  raw_json                VARIANT\n, filename                STRING    NOT NULL\n, file_row_seq            NUMBER    NOT NULL\n, ldts                    TIMESTAMP NOT NULL\n, rsrc                    STRING    NOT NULL\n)\nCHANGE_TRACKING = TRUE;\n\n-- LZ: Orders System -----------------------------------------------------------\nUSE SCHEMA TPCH_ORDERS_SYS;\n\nCREATE OR REPLACE TABLE stg_orders\n(\n  o_orderkey              NUMBER\n, o_custkey               NUMBER\n, o_orderstatus           STRING\n, o_totalprice            NUMBER\n, o_orderdate             DATE\n, o_orderpriority         STRING\n, o_clerk                 STRING\n, o_shippriority          NUMBER\n, o_comment               STRING\n, filename                STRING    NOT NULL\n, file_row_seq            NUMBER    NOT NULL\n, ldts                    TIMESTAMP NOT NULL\n, rsrc                    STRING    NOT NULL\n)\nCHANGE_TRACKING = TRUE;\n```\n\n### Step 3: DV - Tracking New Data to be loaded into the Raw Vault\n\nThe tables we created will be used by Snowpipe to drip-feed the data as it lands in the stage. In order to easily detect and incrementally process the new portion of data we'll create [streams](https://docs.snowflake.com/en/user-guide/streams.html) on these staging tables. These streams are part of the mechanism used to load data into the Enterprise Memory Zone's Raw Vault, not into the Landing Zone, so we place them accordingly.\n\n```sql\n--- DV: Tracking Customer Changes ----------------------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DV;\nUSE SCHEMA SALESMKT;\n\nCREATE OR REPLACE STREAM stg_customer_strm ON TABLE DEV_LZ.TPCH_CUSTOMER_SYS.stg_customer;\n\n-- DV: Tracking Orders Changes ------------------------------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA CUSTSERV;\n\nCREATE OR REPLACE STREAM stg_orders_strm ON TABLE DEV_LZ.TPCH_ORDERS_SYS.stg_orders;\n```\n\n\u003E Note the use of two different roles here. We are effectively acting as a data engineer for two domains. Domains are not teams. A single person can have multiple functional roles, akin to \"wearing different hats.\" In a real-world scenario, we're likely working with subject matter experts and getting approval from leaders specific to either the Sales & Marketing domain (perhaps the CRO, or VP of Sales), or the Customer Service domain (perhaps the CCO, or VP of Customer Success). The process is similar, but the specific people are often different.\n\n### Step 4: LZ - Simulating New Data into the Landing Zone\n\nNext we'll produce some sample data. And for the sake of simplicity we'll take a bit of a shortcut here, generating data by unloading subset of data from our TPCH sample dataset into files. Then we'll use Snowpipe to load it back into our Landing Zone, simulating the streaming feed.\n\nLet's start by creating two stages for each data class type, orders and customer. In real-life scenarios these could be internal or external stages, or these feeds could be sourced via the [Snowflake Connector for Kafka](https://docs.snowflake.com/en/user-guide/kafka-connector/index), using high-performance [Snowpipe Streaming](https://docs.snowflake.com/en/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview). The world is your oyster. \n\n```sql\n-- LZ: Simulating Customer System Data -----------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\nUSE DATABASE DEV_LZ;\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nCREATE OR REPLACE STAGE customer_data FILE_FORMAT = (TYPE = JSON);\n\n-- LZ: Simulating Orders System Data -------------------------------------------\nUSE SCHEMA TPCH_ORDERS_SYS;\n\nCREATE OR REPLACE STAGE orders_data   FILE_FORMAT = (TYPE = CSV );\n```\n\nNext we'll generate and unload sample data. There are couple of things going on.\n\nFirst, we are using [object_construct](https://docs.snowflake.com/en/sql-reference/functions/object_construct.html) as a quick way to create a object/document from all columns and subset of rows for **customer** data and offload it into customer_data stage. **Orders** data would be extracted into compressed CSV files. There are many additonal options in the [COPY INTO stage](https://docs.snowflake.com/en/sql-reference/sql/copy-into-location.html) construct that would fit most requirements, but in this case we are using INCLUDE_QUERY_ID to make it easier to generate new incremental files, as we'll run these commands over and over again, without a need to deal with file overriding. \n\n```sql\n-- LZ: Simulating Customer System Data -----------------------------------------\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nCOPY INTO @customer_data \nFROM\n(SELECT object_construct(*)\n  FROM snowflake_sample_data.tpch_sf10.customer limit 10\n) \nINCLUDE_QUERY_ID=TRUE;\n\n-- LZ: Simulating Orders System Data -------------------------------------------\nUSE SCHEMA TPCH_ORDERS_SYS;\n\nCOPY INTO @orders_data \nFROM\n(SELECT *\n  FROM snowflake_sample_data.tpch_sf10.orders limit 1000\n) \nINCLUDE_QUERY_ID=TRUE;\n```\n\nWe can now run the following to validate that the JSON object data is now stored in files:\n\n```sql\n-- LZ: Inspecting Customer System Data -----------------------------------------\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nLIST @customer_data;\n\nSELECT METADATA$FILENAME,$1 FROM @customer_data; \n```\n![Semistructured JSON Customer Data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/semistructuredcustomerdata.png?v=7a7bb18b)\n\nNext, we will setup Snowpipe to load data from files in a stage into staging tables. \n\nFor better transparency, we will trigger Snowpipe explicitly to scan for new files. However, in real projects, we would likely enable AUTO_INGEST, connecting it to cloud storage events (like AWS SNS) to process new files automatically. \n\n```sql\n-- LZ: Simulating Orders System Data -------------------------------------------\nUSE SCHEMA TPCH_ORDERS_SYS;\n\nCREATE OR REPLACE PIPE stg_orders_pp \nAS \nCOPY INTO stg_orders \nFROM\n(\nSELECT $1,$2,$3,$4,$5,$6,$7,$8,$9 \n     , metadata$filename\n     , metadata$file_row_number\n     , CURRENT_TIMESTAMP()\n     , 'Orders System'\n  FROM @orders_data\n);\n\nALTER PIPE DEV_LZ.TPCH_ORDERS_SYS.stg_orders_pp REFRESH;\n\n-- LZ: Simulating Customer System Data -----------------------------------------\nUSE SCHEMA TPCH_CUSTOMER_SYS;\n\nCREATE OR REPLACE PIPE stg_customer_pp \n--AUTO_INGEST = TRUE\n--aws_sns_topic = 'arn:aws:sns:mybucketdetails'\nAS \nCOPY INTO stg_customer\nFROM \n(\nSELECT $1\n     , metadata$filename\n     , metadata$file_row_number\n     , CURRENT_TIMESTAMP()\n     , 'Customer System'\n  FROM @customer_data\n);\n\nALTER PIPE DEV_LZ.TPCH_CUSTOMER_SYS.stg_customer_pp REFRESH;\n```\n\nOnce this done, we should see data is appearing in the target tables and the stream on these tables. As we'd expect, the number of rows in the streams are the same as in the tables. That's because we haven't yet consumed the content of the streams, and the tables had zero rows when we created the streams. Stay tuned! \n\n```sql\n-- DV: Inspecting Streams ------------------------------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\n\nSELECT 'DEV_LZ.TPCH_CUSTOMER_SYS.stg_customer', count(1) FROM DEV_LZ.TPCH_CUSTOMER_SYS.stg_customer\nUNION ALL\nSELECT 'DEV_DV.SALESMKT.stg_customer_strm', count(1) FROM DEV_DV.SALESMKT.stg_customer_strm\n;\n\nUSE ROLE CUSTSERV_ENGINEER;\n\nSELECT 'DEV_LZ.TPCH_ORDERS_SYS.stg_orders', count(1) FROM DEV_LZ.TPCH_ORDERS_SYS.stg_orders\nUNION ALL\nSELECT 'DEV_DV.CUSTSERV.stg_orders_strm', count(1) FROM DEV_DV.CUSTSERV.stg_orders_strm\n;\n```\n![Matching Row Counts](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/matchingrowcounts.png?v=7a7bb18b)\n\n### Step 5: DV - Preparing to Load the Raw Vault\n\nFinally, now that we established the basics and new data is logged in the stream, let's see how we can derive some of the business keys for the Data Vault entities we'll model. In this example, we'll model it as a view on top of the stream that should allow us to perform data parsing (raw_json -\u003E columns) and business_key, hash_diff derivation on the fly.\n\nAnother thing to notice here is the use of the SHA1_BINARY function in the hashing algorithm. There are many articles on choosing between MD5/SHA1(2)/other hash functions, so we won't focus on this. For this guide, we'll use fairly common SHA1 and its BINARY version from Snowflake arsenal of functions, using fewer bytes than a STRING. Note that our chosen delimiter is '^', '-1' represents our required null keys or missing descriptive data, '_hk' indicates a hash key, and '_hd' indicates a hash diff. While Data Vault 2.1 does not standardize a specific hashing algorithm or naming convention, the DV2.1 standards do state these must be defined and applied consistently across the implementation.\n\nAfter creating the views, be sure to take a good look at the sample output.\n\n```sql\n-- DV: Hard Business Rules and Hashing - Customer ------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DV;\nUSE SCHEMA SALESMKT;\n\nCREATE OR REPLACE VIEW stg_customer_strm_outbound AS \nSELECT src.*\n     , raw_json:C_CUSTKEY::NUMBER           c_custkey\n     , raw_json:C_NAME::STRING              c_name\n     , raw_json:C_ADDRESS::STRING           c_address\n     , raw_json:C_NATIONKEY::NUMBER         C_nationcode\n     , raw_json:C_PHONE::STRING             c_phone\n     , raw_json:C_ACCTBAL::NUMBER           c_acctbal\n     , raw_json:C_MKTSEGMENT::STRING        c_mktsegment\n     , raw_json:C_COMMENT::STRING           c_comment     \n--------------------------------------------------------------------\n-- derived hashes\n--------------------------------------------------------------------\n     , SHA1_BINARY(UPPER(TRIM(NVL(c_custkey,'-1'))))  AS  customer_hk\n     , SHA1_BINARY(UPPER(ARRAY_TO_STRING(ARRAY_CONSTRUCT( \n                                              NVL(TRIM(c_name)       ,'-1')\n                                            , NVL(TRIM(c_address)    ,'-1')              \n                                            , NVL(TRIM(c_nationcode) ,'-1')                 \n                                            , NVL(TRIM(c_phone)      ,'-1')            \n                                            , NVL(TRIM(c_acctbal)    ,'-1')               \n                                            , NVL(TRIM(c_mktsegment) ,'-1')                 \n                                            , NVL(TRIM(c_comment)    ,'-1')               \n                                            ), '^')))  AS customer_hd\n  FROM stg_customer_strm src\n;\n\nSELECT * FROM stg_customer_strm_outbound LIMIT 5;\n\n-- DV: Hard Business Rules and Hashing - Orders --------------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nCREATE OR REPLACE VIEW stg_order_strm_outbound AS \nSELECT src.*\n--------------------------------------------------------------------\n-- derived hashes\n--------------------------------------------------------------------\n     , SHA1_BINARY(UPPER(TRIM(o_orderkey)))                        AS order_hk\n     , SHA1_BINARY(UPPER(TRIM(o_custkey)))                         AS customer_hk\n     , SHA1_BINARY(UPPER(ARRAY_TO_STRING(ARRAY_CONSTRUCT( NVL(TRIM(o_orderkey)       ,'-1')\n                                                        , NVL(TRIM(o_custkey)        ,'-1')\n                                                        ), '^')))  AS customer_order_hk\n     , SHA1_BINARY(UPPER(ARRAY_TO_STRING(ARRAY_CONSTRUCT( NVL(TRIM(o_orderstatus)    , '-1')         \n                                                        , NVL(TRIM(o_totalprice)     , '-1')        \n                                                        , NVL(TRIM(o_orderdate)      , '-1')       \n                                                        , NVL(TRIM(o_orderpriority)  , '-1')           \n                                                        , NVL(TRIM(o_clerk)          , '-1')    \n                                                        , NVL(TRIM(o_shippriority)   , '-1')          \n                                                        , NVL(TRIM(o_comment)        , '-1')      \n                                                        ), '^')))  AS order_hd     \n  FROM DEV_DV.CUSTSERV.stg_orders_strm src\n;\n\nSELECT * FROM stg_order_strm_outbound LIMIT 5;\n```\n\n![Staged Customer Stream Outbound View](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/customerstreamoutbound.png?v=7a7bb18b)\n\nDoes the output look good? Well done! We've built our staging/inbound pipeline, ready to accommodate streaming data with defined business keys, hash keys, and hash diffs that we'll use in our Raw Vault. Let's move on to the next step!\n\n\n\u003C!-- ------------------------ --\u003E\n## Build: Data Vault - Raw Vault\n\nIn this section, we will start building structures and pipelines for the **Raw Vault**. This is the ERD of the objects we'll deploy:\n![Raw Vault ERD](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/img16.png?v=7a7bb18b)\n\n### Step 1: Raw Vault Tables\n\nWe'll start with DDL for the Hubs, Links and Satellites. This guide won't detail the Data Vault 2.1 model standards. For certified training in DV2.1, we highly recommend working with experts & partners from Data Vault Alliance. While primary and foreign keys are identified by constraints in the code below, it's important to remember that Snowflake does not enforce them.\n\n```sql\n-- DV: Raw Vault - Sales & Marketing -------------------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.SALESMKT;\n\n-- Sales & Marketing: Region Reference\nCREATE OR REPLACE TABLE ref_region \n( \n  regioncode            NUMBER    NOT NULL\n, ldts                  TIMESTAMP NOT NULL\n, rsrc                  STRING    NOT NULL\n, r_name                STRING\n, r_comment             STRING\n, CONSTRAINT pk_ref_region        PRIMARY KEY (regioncode)                                                                             \n)\nAS \nSELECT r_regionkey\n     , ldts\n     , rsrc\n     , r_name\n     , r_comment\n  FROM DEV_LZ.TPCH_REF.stg_region;\n\n-- Sales & Marketing: Nation Reference\nCREATE OR REPLACE TABLE ref_nation \n( \n  nationcode            NUMBER    NOT NULL\n, regioncode            NUMBER    NOT NULL\n, ldts                  TIMESTAMP NOT NULL\n, rsrc                  STRING    NOT NULL\n, n_name                STRING\n, n_comment             STRING\n, CONSTRAINT pk_ref_nation        PRIMARY KEY (nationcode)                                                                             \n, CONSTRAINT fk_ref_region        FOREIGN KEY (regioncode) REFERENCES ref_region(regioncode)  \n)\nAS \nSELECT n_nationkey\n     , n_regionkey\n     , ldts\n     , rsrc\n     , n_name\n     , n_comment\n  FROM DEV_LZ.TPCH_REF.stg_nation;\n\n-- Sales & Marketing: Raw Vault Customer Hub\nCREATE OR REPLACE TABLE rv_hub_customer \n( \n  customer_hk             BINARY    NOT NULL   \n, customer_bk             NUMBER    NOT NULL                                                                                 \n, ldts                    TIMESTAMP NOT NULL\n, rsrc                    STRING    NOT NULL\n, CONSTRAINT pk_rv_hub_customer     PRIMARY KEY(customer_hk)\n);\n\n-- Sales & Marketing: Raw Vault Customer Satellite\nCREATE OR REPLACE TABLE rv_sat_customer \n( \n  customer_hk            BINARY    NOT NULL\n, ldts                   TIMESTAMP NOT NULL\n, rsrc                   STRING    NOT NULL  \n, customer_hd            BINARY    NOT NULL\n, c_name                 STRING\n, c_address              STRING\n, c_phone                STRING \n, c_acctbal              NUMBER\n, c_mktsegment           STRING    \n, c_comment              STRING\n, nationcode             NUMBER\n, CONSTRAINT pk_rv_sat_customer    PRIMARY KEY(customer_hk, ldts)\n, CONSTRAINT fk_rv_sat_customer    FOREIGN KEY(customer_hk) REFERENCES rv_hub_customer\n);\n\n-- DV: Raw Vault - Customer Service\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\n-- Customer Service: Raw Vault Order Hub\nCREATE OR REPLACE TABLE rv_hub_order \n( \n  order_hk                BINARY    NOT NULL   \n, order_bk                NUMBER    NOT NULL                                                                                 \n, ldts                    TIMESTAMP NOT NULL\n, rsrc                    STRING    NOT NULL\n, CONSTRAINT pk_rv_hub_order        PRIMARY KEY(order_hk)\n);\n\n-- Customer Service: Raw Vault Order Satellite\nCREATE OR REPLACE TABLE rv_sat_order \n( \n  order_hk               BINARY    NOT NULL   \n, ldts                   TIMESTAMP NOT NULL\n, rsrc                   STRING    NOT NULL   \n, order_hd               BINARY    NOT NULL\n, o_orderstatus          STRING   \n, o_totalprice           NUMBER\n, o_orderdate            DATE\n, o_orderpriority        STRING\n, o_clerk                STRING    \n, o_shippriority         NUMBER\n, o_comment              STRING\n, CONSTRAINT pk_rv_sat_order       PRIMARY KEY(order_hk, ldts)\n, CONSTRAINT fk_rv_sat_order       FOREIGN KEY(order_hk) REFERENCES rv_hub_order\n);\n\n-- Customer Service: Raw Vault Customer-Order Link\nCREATE OR REPLACE TABLE rv_lnk_customer_order\n(\n  customer_order_hk       BINARY       NOT NULL   \n, customer_hk             BINARY \n, order_hk                BINARY \n, ldts                    TIMESTAMP    NOT NULL\n, rsrc                    STRING       NOT NULL  \n, CONSTRAINT pk_rv_lnk_customer_order  PRIMARY KEY(customer_order_hk)\n, CONSTRAINT fk1_rv_lnk_customer_order FOREIGN KEY(customer_hk) REFERENCES DEV_DV.SALESMKT.rv_hub_customer\n, CONSTRAINT fk2_rv_lnk_customer_order FOREIGN KEY(order_hk)    REFERENCES rv_hub_order\n);\n```\n\n### Step 2: Continuous Loading into the Raw Vault Tables\n\nNow, we have source data waiting in our staging streams & views, and we have target Raw Vault tables ready.\n\nLet's connect the dots. We'll create tasks, one task per stream, so when new records are available, that new data will be incrementally loaded to all dependent Raw Vault models in one operation. To achieve that, we'll use **multi-table insert** capability of Snowflake mentioned earlier. Tasks can be set up to run on a pre-defined frequency (every 1 minute in this guide) and use a dedicated virtual warehouse for compute. To minimize compute costs, before waking the warehouse, tasks will check for new data in the stream. We only pay for the compute we use.\n\n```sql\n-- Sales & Marketing: Raw Vault Customer System Data MTI -----------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE SCHEMA DEV_DV.SALESMKT;\n\nCREATE OR REPLACE TASK stg_customer_strm_tsk\n  WAREHOUSE = DEV_XFORM_WH\n  SCHEDULE = '1 minute'\nWHEN\n  SYSTEM$STREAM_HAS_DATA('DEV_DV.SALESMKT.STG_CUSTOMER_STRM')\nAS \nINSERT ALL\nWHEN (SELECT COUNT(1) FROM rv_hub_customer tgt WHERE tgt.customer_hk = src_customer_hk) = 0\nTHEN INTO rv_hub_customer  \n( customer_hk\n, customer_bk\n, ldts\n, rsrc\n)\nVALUES \n( src_customer_hk\n, src_customer_bk\n, src_ldts\n, src_rsrc\n)\nWHEN (SELECT COUNT(1) FROM rv_sat_customer tgt WHERE tgt.customer_hk = src_customer_hk AND tgt.customer_hd = src_customer_hd) = 0\nTHEN INTO rv_sat_customer  \n(\n  customer_hk  \n, ldts              \n, c_name            \n, c_address         \n, c_phone           \n, c_acctbal         \n, c_mktsegment      \n, c_comment         \n, nationcode        \n, customer_hd         \n, rsrc              \n)\nVALUES \n(\n  src_customer_hk\n, src_ldts              \n, src_c_name            \n, src_c_address         \n, src_c_phone           \n, src_c_acctbal         \n, src_c_mktsegment      \n, src_c_comment         \n, src_nationcode        \n, src_customer_hd         \n, src_rsrc              \n)\nSELECT customer_hk         src_customer_hk\n     , c_custkey           src_customer_bk\n     , c_name              src_c_name\n     , c_address           src_c_address\n     , c_nationcode        src_nationcode\n     , c_phone             src_c_phone\n     , c_acctbal           src_c_acctbal\n     , c_mktsegment        src_c_mktsegment\n     , c_comment           src_c_comment    \n     , customer_hd         src_customer_hd\n     , ldts                src_ldts\n     , rsrc                src_rsrc\n  FROM stg_customer_strm_outbound src\n;\n\n-- Customer Service: Raw Vault Orders System Data MTI --------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nCREATE OR REPLACE TASK stg_order_strm_tsk\n  WAREHOUSE = DEV_XFORM_WH\n  SCHEDULE = '1 minute'\nWHEN\n  SYSTEM$STREAM_HAS_DATA('DEV_DV.CUSTSERV.STG_ORDERS_STRM')\nAS \nINSERT ALL\nWHEN (SELECT COUNT(1) FROM rv_hub_order tgt WHERE tgt.order_hk = src_order_hk) = 0\nTHEN INTO rv_hub_order  \n( order_hk\n, order_bk\n, ldts\n, rsrc\n)  \nVALUES \n( src_order_hk\n, src_order_bk\n, src_ldts\n, src_rsrc\n)  \nWHEN (SELECT COUNT(1) FROM rv_sat_order tgt WHERE tgt.order_hk = src_order_hk AND tgt.order_hd = src_order_hd) = 0\nTHEN INTO rv_sat_order\n(\n  order_hk\n, ldts\n, o_orderstatus\n, o_totalprice\n, o_orderdate\n, o_orderpriority\n, o_clerk\n, o_shippriority\n, o_comment\n, order_hd\n, rsrc\n)  \nVALUES \n(\n  src_order_hk\n, src_ldts\n, src_o_orderstatus\n, src_o_totalprice\n, src_o_orderdate\n, src_o_orderpriority\n, src_o_clerk\n, src_o_shippriority\n, src_o_comment\n, src_order_hd\n, src_rsrc\n)\nWHEN (SELECT COUNT(1) FROM rv_lnk_customer_order tgt WHERE tgt.customer_order_hk = src_customer_order_hk) = 0\nTHEN INTO rv_lnk_customer_order  \n(\n  customer_order_hk\n, customer_hk\n, order_hk\n, ldts\n, rsrc\n)  \nVALUES \n(\n  src_customer_order_hk\n, src_customer_hk\n, src_order_hk\n, src_ldts\n, src_rsrc\n)\nSELECT order_hk                src_order_hk\n     , customer_order_hk       src_customer_order_hk\n     , customer_hk             src_customer_hk\n     , o_orderkey              src_order_bk\n     , o_orderstatus           src_o_orderstatus  \n     , o_totalprice            src_o_totalprice   \n     , o_orderdate             src_o_orderdate    \n     , o_orderpriority         src_o_orderpriority\n     , o_clerk                 src_o_clerk        \n     , o_shippriority          src_o_shippriority \n     , o_comment               src_o_comment      \n     , order_hd                src_order_hd\n     , ldts                    src_ldts\n     , rsrc                    src_rsrc\n  FROM stg_order_strm_outbound src\n;\n```\n\n### Step 3: Continuous Loading into the Raw Vault Tables\n\nWhen tasks are created, they are initially suspended; Resuming the tasks lets them automatically run on schedule. After resuming, we can look at the task execution history to see the process. \n\n```sql\n-- Customer Service: Resuming the Raw Vault MTI Loading Task -------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nALTER TASK stg_order_strm_tsk RESUME;\n\nSELECT * FROM table(information_schema.task_history()) ORDER BY scheduled_time DESC;\n\n-- Repeat the SELECT above over a few minutes to see the results over time.\n--   The row representing the next scheduled run will have a STATE of SCHEDULED.\n--   A STATE of FAILED means the task failed, and the ERROR_MESSAGE explains why.\n--   Repeated failures will result in a STATE of FAILED_AND_SUSPENDED.\n--   A STATE of SUCCEEDED means the task succeeded.\n--   A STATE of SKIPPED means the task didn't find any new data.\n\n-- Sales & Marketing: Resuming the Raw Vault MTI Loading Task ------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE SCHEMA DEV_DV.SALESMKT;\n\nALTER TASK stg_customer_strm_tsk RESUME;\n\nSELECT * FROM table(information_schema.task_history()) ORDER BY scheduled_time DESC;\n```\n\nIf we see SUCCEEDED, the task ran successfully!\n\n![Task Details](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/taskhistory.png?v=7a7bb18b)\n\nAll tasks can be monitored in one location by clicking on Transformation -\u003E Tasks. Opening Task Details from there, or directly through the Horizon Catalog, shows us key details like the state of the task, its schedule, the condition in which it runs, and how many failures are attempted before auto-suspending. The Task Graph and Run History are just a tab-click away.\n\n![Task Details](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/taskdetails.png?v=7a7bb18b)\n\n### Step 4: Verifying the Successful Loads\n\nWe can use the SQL below to check the row counts in our Raw Vault tables, and our two outbound streams.\n\n```sql\n-- Verifying the Loads ---------------------------------------------------------\nSELECT 'rv_hub_customer', count(1) FROM DEV_DV.SALESMKT.rv_hub_customer\nUNION ALL\nSELECT 'rv_hub_order', count(1) FROM DEV_DV.CUSTSERV.rv_hub_order\nUNION ALL\nSELECT 'rv_sat_customer', count(1) FROM DEV_DV.SALESMKT.rv_sat_customer\nUNION ALL\nSELECT 'rv_sat_order', count(1) FROM DEV_DV.CUSTSERV.rv_sat_order\nUNION ALL\nSELECT 'rv_lnk_customer_order', count(1) FROM DEV_DV.CUSTSERV.rv_lnk_customer_order\nUNION ALL\nSELECT 'stg_customer_strm_outbound', count(1) FROM DEV_DV.SALESMKT.stg_customer_strm_outbound\nUNION ALL\nSELECT 'stg_order_strm_outbound', count(1) FROM DEV_DV.CUSTSERV.stg_order_strm_outbound\n;\n```\n\nThe views on streams in our staging area no longer return any rows, and as a result, the tasks simply wait to be triggered on the stream having data again. We never deleted rows or truncated a table. The prior querying of the views did not consume the contents of the stream. The task paired with the stream automatically consumed the rows of the stream. The multi-table insert allowed us to load **five** target tables with just **two** stream / task combinations. Do we need to spend any more time implementing incremental detection and processing logic on the application side? No.\n\nNotice that we didn't use two different roles when selecting rows from the two domains. While only the specific domain-oriented role is able to perform **create and write** actions, **cross-domain reading and referencing** can be performed. We intentionally designed this in the [Defensible Analytics using Data Vault and Snowflake](https://www.snowflake.com/en/developers/guides/defensible-analytics-using-data-vault-and-snowflake/) guide. Those with dominion over Sales & Marketing govern the data, while those operating in other domains are able to use that data, eliminating duplication of effort and establishing clear data lineage and accountability.\n\nWe now have data in our **Raw Vault**. Let's move on and consider virtualization in our continuous Data Vault solution.\n\n\u003C!-- ------------------------ --\u003E\n## Views for Agile Reporting\n\nOne of the great benefits of Snowflake is that it is now possible to default our Business Vault and Information Mart objects as views. Numerous customers use this approach in production today. No longer is there a need to argue that there are “too many joins” or that the response \"won’t be fast enough.\" The elasticity of the Snowflake virtual warehouses combined with our dynamic optimization engine have solved that problem. For more detail, see this [post](/blog/tips-for-optimizing-the-data-vault-architecture-on-snowflake-part-3/).\n\nWhen we want to deliver data to the business users and data scientists in near real time, using views is the best option. Once we have the streaming loads built to feed our Data Vault, the fastest way to make that data visible downstream will be views. Views allow us to deliver the data faster by eliminating any latency that would be incurred by ELT processes between the Data Vault and the data consumers downstream.\n\nAll the business logic, alignment, and formatting of the data can be coded into the view. That means fewer moving parts to debug, no sequential downstream latency, and minimized storage costs.\n\n![Data Vault Virtualization Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/datavaultvirtualization.png?v=7a7bb18b)  \n\nLooking at the diagram above, we see an example of how virtualization fits in the architecture. Here, solid lines represent physical tables and dotted lines represent views. We incrementally ingest data into the **Raw Vault**, and downstream transformations are applied in views. From a data consumer's perspective when working with a virtualized information mart, the query has access to the full, up-to-date enterprise memory, at the point in time the query is submitted. \n\nWith Snowflake, we have the ability to provide as much compute as required, on-demand, without a risk of performance impact on any surrounding processes, paying only for what we use. This makes materialization of transformations in the **Business Vault** or other **Information Delivery** objects optional, rather than required. Instead of “optimizing upfront,” we can now make this decision based on observed usage pattern characteristics, such as frequency of use, type of queries, latency requirements, readiness of the requirements, etc. As always, the Business Vault is sparsely built, maximizing the amount of work not done, allowing us to focus on decision effectiveness and delivering business outcomes.\n\nMany modern data engineering automation tools already support virtualization of logic. Several tools offer a low-code or configuration-like ability to switch between materializing an object as a view or a physical table, automatically generating all required DDL & DML. This could be applied to specific objects, zones, or be environment specific. So even if we start with a view, we can easily refactor to using a table, as requirements evolve.\n\nVirtualization is not only a way to improve time-to-value and provide near real time access to the data. Given the scalability and workload isolation of Snowflake, virtualization is a design technique that could make your Data Vault excel: minimizing cost-of-change, accelerating the time-to-delivery and becoming an extremely agile, future proof solution for ever changing business needs. \n\n\u003C!-- ------------------------ --\u003E\n## Build: Data Vault - Business Vault\n\nNext, we'll demonstrate enrichment of descriptive data in the Business Vault.\n\n### Step 1: Expanding Customer Detail\n\nLet's create a view that will perform additional derivations on the fly. Assuming non-functional capabilities are satisfying our requirements, deploying transformations (and re-deploying new versions) in this way is super easy. \n\n```sql\n-- Business Vault: Expanding Customer with Nation and Region Name\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.SALESMKT;\n\nCREATE OR REPLACE VIEW bv_sat_customer\nAS\nSELECT rsc.customer_hk\n     , rsc.ldts                   \n     , rsc.c_name                 \n     , rsc.c_address              \n     , rsc.c_phone                 \n     , rsc.c_acctbal              \n     , rsc.c_mktsegment               \n     , rsc.c_comment              \n     , rsc.nationcode             \n     , rsc.rsrc \n     -- derived \n     , rrn.n_name        nation_name\n     , rrr.r_name        region_name\n  FROM rv_sat_customer rsc\n  LEFT OUTER JOIN ref_nation rrn\n    ON (rsc.nationcode = rrn.nationcode)\n  LEFT OUTER JOIN ref_region rrr\n    ON (rrn.regioncode = rrr.regioncode)\n;\n```\n\n### Step 2: Expanding Order Detail\n\nNow, let's imagine we have a heavier transformation to perform on our order data. It could be more data volume, could be more complex logic, PITs, bridges or even an object that will be used frequently and by many users.\n\nSnowflake provides [multiple options](https://docs.snowflake.com/en/user-guide/overview-view-mview-dts) in these situations:\n- A new bv_sat_order Table, loaded through the use of another Stream / Task combination, where the new Task is triggered to run immediately after stg_order_strm_tsk. This option is available on all Snowflake Editions.\n- A new bv_sat_order [Dynamic Table](https://docs.snowflake.com/en/user-guide/dynamic-tables-about). This simplifies the implementation significantly as compared to using a Stream and Task, using a definition similar to a CTAS statement, with a warehouse specified and a target lag. This option is also available on all Snowflake Editions.\n- A new bv_sat_order [Materialized View](https://docs.snowflake.com/en/user-guide/views-materialized). This also simplifies the implementation dramatically, being defined just as you would a view. However, this option requires Enterprise Edition or above, and is limited to a query of a single table.\n\nFor this example, let's consider a soft business rule where orders with an Urgent or High priority, and having a total price of 200000 or more, are considered Tier-1. Urgent, High, or Medium and between 150000 and 200000 are Tier-2, and the rest Tier-3. We first try building this into a view, but find that performance is poor, due to regular querying with a filter set to just Tier-1 orders. We decide for performance reasons to materialize this, and we want it updated immediately, with no delay.\n\nIn this particular case, a Materialized View could be used, because the view would query a single table, and assuming we're using Enterprise Edition. Given the underlying table is insert-only, auto-refresh will be incremental (and thus cheap). And because there is only a simple CASE derivation, with no joins, aggregates, or window functions, a Materialized View could be an ideal choice. A Dynamic Table would be much less limited, but would introduce at least some target lag between the raw vault and business vault structures (and hey, this is a near-real-time data vault we're building). So, we'll stick with the Streams & Tasks method. Feel free to experiment with the other options, if desired.\n\nLet's first build a new business vault satellite, using our existing data. We'll first suspend our raw vault order loading, so we don't miss any data.\n\n```sql\n-- Business Vault: Expanding Order with Order Priority Bucket (soft business rule)\nUSE ROLE CUSTSERV_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\n-- Suspend loading rv_sat_order (and verify it's not currently running)\nALTER TASK stg_order_strm_tsk SUSPEND;\n\nSELECT * FROM table(information_schema.task_history()) ORDER BY scheduled_time DESC;\n\n-- Initialize the new bv_sat_order table\nCREATE OR REPLACE TABLE bv_sat_order\n( \n  order_hk               BINARY    NOT NULL   \n, ldts                   TIMESTAMP NOT NULL\n, o_orderstatus          STRING   \n, o_totalprice           NUMBER\n, o_orderdate            DATE\n, o_orderpriority        STRING\n, o_clerk                STRING    \n, o_shippriority         NUMBER\n, o_comment              STRING  \n, order_hd               BINARY    NOT NULL\n, rsrc                   STRING    NOT NULL   \n-- additional attributes\n, order_priority_bucket  STRING\n, CONSTRAINT pk_bv_sat_order PRIMARY KEY(order_hk, ldts)\n, CONSTRAINT fk_bv_sat_order FOREIGN KEY(order_hk) REFERENCES rv_hub_order\n)\nAS \nSELECT order_hk\n     , ldts\n     , o_orderstatus\n     , o_totalprice\n     , o_orderdate\n     , o_orderpriority\n     , o_clerk\n     , o_shippriority\n     , o_comment\n     , order_hd\n     , rsrc\n     -- derived additional attributes\n     , CASE WHEN o_orderpriority IN ('2-HIGH', '1-URGENT')             AND o_totalprice \u003E= 200000 THEN 'Tier-1'\n            WHEN o_orderpriority IN ('3-MEDIUM', '2-HIGH', '1-URGENT') AND o_totalprice BETWEEN 150000 AND 200000 THEN 'Tier-2'  \n            ELSE 'Tier-3'\n       END order_priority_bucket\nFROM rv_sat_order;\n```\n\n### Step 3: Loading the Expanded Order Detail\n\nFrom a processing and orchestration perspective, we will extend our order processing pipeline so that when the task populates **rv_sat_order**, this will generate a new stream of changes, and those changes will be propagated by a dependent task to **bv_sat_order**. Tasks in Snowflake can be not only schedule-based but also start automatically once a parent task completes.\n\n```sql\n-- Business Vault: Loading Order with Order Priority Bucket (soft business rule)\nUSE ROLE CUSTSERV_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\n-- Create a new stream\nCREATE OR REPLACE STREAM rv_sat_order_strm ON TABLE rv_sat_order;\n\n-- Create the new task to consume the stream\nCREATE OR REPLACE TASK rv_sat_order_strm_tsk\n  WAREHOUSE = DEV_XFORM_WH\n  AFTER stg_order_strm_tsk\nAS\nINSERT INTO bv_sat_order\nSELECT\n  order_hk\n, ldts\n, o_orderstatus\n, o_totalprice\n, o_orderdate\n, o_orderpriority\n, o_clerk\n, o_shippriority\n, o_comment\n, order_hd\n, rsrc\n-- derived additional attributes\n, CASE WHEN o_orderpriority IN ('2-HIGH', '1-URGENT')             AND o_totalprice \u003E= 200000 THEN 'Tier-1'\n       WHEN o_orderpriority IN ('3-MEDIUM', '2-HIGH', '1-URGENT') AND o_totalprice BETWEEN 150000 AND 200000 THEN 'Tier-2'\n       ELSE 'Tier-3'\n  END order_priority_bucket\nFROM rv_sat_order_strm;\n\n-- Resume the new and old task\nALTER TASK rv_sat_order_strm_tsk RESUME;\nALTER TASK stg_order_strm_tsk RESUME;\n```\n\n### Step 4: Testing the Expanded Order Detail Load\n\nNow, let's go back to our staging area to process another slice of data to test the task. \n\n```sql\n-- LZ: Simulating More Orders System Data --------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\nUSE SCHEMA DEV_LZ.TPCH_ORDERS_SYS;\n\nCOPY INTO @orders_data \nFROM\n(SELECT *\n  FROM snowflake_sample_data.tpch_sf10.orders LIMIT 1000 OFFSET 1000\n) \nINCLUDE_QUERY_ID=TRUE;\n\nALTER PIPE stg_orders_pp REFRESH;\n\nSELECT 'DEV_LZ.TPCH_ORDERS_SYS.stg_orders', count(1) FROM DEV_LZ.TPCH_ORDERS_SYS.stg_orders;\n```\n\n### Step 5: Verifying the Expanded Order Detail Load\n\nData is now automatically flowing through all the layers via asynchronous tasks. With the results you can validate: \n\n```sql\n-- Verifying Full Business Vault Order Satellite Load --------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nSELECT *\n  FROM table(information_schema.task_history())\n  ORDER BY scheduled_time DESC\n;\n-- Repeat the above as needed to see the two tasks succeed...\n\nSELECT 'DEV_DV.CUSTSERV.stg_orders_strm', count(1) FROM DEV_DV.CUSTSERV.stg_orders_strm\nUNION ALL\nSELECT 'DEV_DV.CUSTSERV.rv_sat_order', count(1) FROM DEV_DV.CUSTSERV.rv_sat_order\nUNION ALL\nSELECT 'DEV_DV.CUSTSERV.rv_sat_order_strm', count(1) FROM DEV_DV.CUSTSERV.rv_sat_order_strm\nUNION ALL\nSELECT 'DEV_DV.CUSTSERV.bv_sat_order', count(1) FROM DEV_DV.CUSTSERV.bv_sat_order;\n```\n\nAssuming the tasks completed, we should now see 0 rows in the streams, and 2000 rows in the tables.\n\nNow that we have **Business Vault** objects complete, let's move into the **Information Delivery** zone. \n\n\u003C!-- ------------------------ --\u003E\n## Build: Information Delivery\n\nWhen it comes to Information Delivery zone we are not changing the meaning of data, but we may change format to simplify users to access and work with the data products/output interfaces. Different consumers may have different needs and preferences, some would prefer star/snowflake dimensional schemas, some would adhere to use flattened objects or even transform data into JSON/parquet objects. \n\n## Step 1: More Supporting Views in the Business Vault\n\nTo simplify working with satellites, let's create views that show the latest version for each key. \n\n```sql\n-- BV: Sales & Marketing Current Views -----------------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DV.SALESMKT;\n\nCREATE OR REPLACE VIEW rv_sat_customer_current\nAS\nSELECT  *\nFROM    rv_sat_customer\nQUALIFY LEAD(ldts) OVER (PARTITION BY customer_hk ORDER BY ldts) IS NULL;\n\nCREATE OR REPLACE VIEW bv_sat_customer_current\nAS\nSELECT  *\nFROM    bv_sat_customer\nQUALIFY LEAD(ldts) OVER (PARTITION BY customer_hk ORDER BY ldts) IS NULL;\n\n-- BV: Customer Service Current Views ------------------------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DV.CUSTSERV;\n\nCREATE OR REPLACE VIEW rv_sat_order_current\nAS\nSELECT  *\nFROM    rv_sat_order\nQUALIFY LEAD(ldts) OVER (PARTITION BY order_hk ORDER BY ldts) IS NULL;\n\nCREATE OR REPLACE VIEW bv_sat_order_current\nAS\nSELECT  *\nFROM    bv_sat_order\nQUALIFY LEAD(ldts) OVER (PARTITION BY order_hk ORDER BY ldts) IS NULL;\n\n-- Note that the patterns in the views above, while correct, can be expensive for very large satellites. Point-in-Time (PIT) tables could be a performance-enhancing option.\n```\n\n### Step 2: Type 1 Dimensions and a Fact Table\n\nLet's create a simple dimensional structure. Again, we will keep it virtual (as views) to start with, but we already know that any of these could be materialized. \n\n```sql\n-- Sales & Marketing Type 1 Dimension: Customer --------------------------------\nUSE ROLE SALESMKT_ENGINEER;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE SCHEMA DEV_DW.SALESMKT;\n\nCREATE OR REPLACE VIEW dim1_customer \nAS \nSELECT hub.customer_bk                            AS customer_id\n     , sat.c_name\n     , sat.c_address\n     , sat.c_phone\n     , sat.c_acctbal\n     , sat.c_mktsegment\n     , sat.c_comment\n     , sat.nation_name\n     , sat.region_name\n     , sat.ldts                                   AS effective_dts\n     , sat.rsrc                                   AS record_source\n  FROM DEV_DV.SALESMKT.rv_hub_customer            hub\n  JOIN DEV_DV.SALESMKT.bv_sat_customer_current    sat ON hub.customer_hk = sat.customer_hk;\n\n-- Customer Service Type 1 Dimension: Order ------------------------------------\nUSE ROLE CUSTSERV_ENGINEER;\nUSE SCHEMA DEV_DW.CUSTSERV;\n\nCREATE OR REPLACE VIEW dim1_order\nAS \nSELECT hub.order_bk                               AS order_id\n     , sat.o_orderstatus\n     , sat.o_totalprice\n     , sat.o_orderdate\n     , sat.o_orderpriority\n     , sat.o_clerk\n     , sat.o_shippriority\n     , sat.o_comment\n     , sat.order_priority_bucket\n     , sat.ldts                                   AS effective_dts\n     , sat.rsrc                                   AS record_source\n  FROM DEV_DV.CUSTSERV.rv_hub_order               hub\n  JOIN DEV_DV.CUSTSERV.bv_sat_order_current       sat ON hub.order_hk = sat.order_hk;\n\n-- Customer Service Fact: Customer Order Details -------------------------------\nCREATE OR REPLACE VIEW fct_customer_order\nAS\nSELECT hub_c.customer_bk                          AS customer_id\n     , hub_o.order_bk                             AS order_id\n     , lnk.ldts                                   AS effective_dts     \n     , lnk.rsrc                                   AS record_source\n-- this is a factless fact, but here you can add any measures, calculated or derived\n  FROM DEV_DV.CUSTSERV.rv_lnk_customer_order      lnk\n  JOIN DEV_DV.SALESMKT.rv_hub_customer            hub_c ON lnk.customer_hk = hub_c.customer_hk\n  JOIN DEV_DV.CUSTSERV.rv_hub_order               hub_o ON lnk.order_hk    = hub_o.order_hk;\n```\n\n### Step 3: Testing the Information Delivery Views\n\nAll good so far? Now let's act as the QA Analyst, and try querying **fct_customer_order** ...\n\n```sql\n-- QA Check --------------------------------------------------------------------\nUSE ROLE QA_ANALYST;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DW;\n\nSELECT 'Order Fact Count', COUNT(1) FROM DEV_DW.CUSTSERV.fct_customer_order; -- How many orders?\n```\n\nWe loaded 2000 rows of orders... right? Does the SELECT statement above give us all 2000 orders? Or closer to none?\n\nIf we recall, when we were unloading sample data, we took a subset of orders and a subset of customers. These samples might have no overlap at all! Therefore, the inner joins could result in all rows being eliminated. \n\nThankfully, this is a Data Vault. All we need to do is land the full customer dataset back in the Landing Zone. Just think about it, there is no need to reprocess any links or fact tables simply because the customer feed was originally incomplete. Those who have used other architectures for data warehouses likely have painful experiences of such situations in the past. So, let's see how easy it is to fix it!\n\n```sql\n-- All the Customers -----------------------------------------------------------\nUSE ROLE DEV_LZ_INGEST;\nUSE WAREHOUSE DEV_INGEST_WH;\nUSE SCHEMA DEV_LZ.TPCH_CUSTOMER_SYS;\n\nCOPY INTO @customer_data \nFROM\n(SELECT object_construct(*)\n  FROM snowflake_sample_data.tpch_sf10.customer\n  -- removed LIMIT 10\n) \nINCLUDE_QUERY_ID=TRUE;\n\nALTER PIPE stg_customer_pp   REFRESH;\n```\n\nAll we need to do is wait, and in a minute, our continuous data pipeline will automatically propagate new customer data into the Data Vault. Late arriving data? No problem.\n\n```sql\n-- QA Check Again --------------------------------------------------------------\nUSE ROLE QA_ANALYST;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DW;\n\nSELECT 'Customer Dimension Count', COUNT(1) FROM DEV_DW.SALESMKT.dim1_customer; -- How many customers now?\nSELECT 'Order Fact Count', COUNT(1) FROM DEV_DW.CUSTSERV.fct_customer_order; -- How many orders now?\n```\n\nFinally let's put on the analyst's hat and run a query to break down orders by nation, region, and order_priority_bucket (all attributes we derived in the **Business Vault**).\n\n```sql\nUSE ROLE QA_ANALYST;\nUSE WAREHOUSE ENGINEERING_WH;\nUSE DATABASE DEV_DW;\n\nSELECT dc.nation_name\n     , dc.region_name\n     , do.order_priority_bucket\n     , COUNT(1)                                   cnt_orders\n     , SUM(o_totalprice)                          total_price\n     , FLOOR(total_price / cnt_orders)            avg_price\n  FROM CUSTSERV.fct_customer_order                fct\n     , SALESMKT.dim1_customer                     dc\n     , CUSTSERV.dim1_order                        do\n WHERE fct.customer_id = dc.customer_id\n   AND fct.order_id = do.order_id\nGROUP BY 1,2,3\nORDER BY 3, 4 DESC;\n```\n\nAs we are using Snowsight, we can quickly create a chart from this result set to better understand the data. For this, simply click on the 'Chart' section on the bottom pane.\n\nFinally, to avoid consuming any additional credits after completion, be sure to suspend the tasks.\n\n```sql\nUSE ROLE CUSTSERV_ENGINEER;\nALTER TASK DEV_DV.CUSTSERV.stg_order_strm_tsk SUSPEND;\nALTER TASK DEV_DV.CUSTSERV.rv_sat_order_strm_tsk SUSPEND;\n\nUSE ROLE SALESMKT_ENGINEER;\nALTER TASK DEV_DV.SALESMKT.stg_customer_strm_tsk SUSPEND;\n```\n\nVoila! This concludes our journey for this guide. We hope you enjoyed it. Let's summarize key points in the next section.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n\nSimplicity of engineering, openness, scalable performance, enterprise-grade governance enabled by the core of the Snowflake platform are now allowing teams to focus on what matters most for the business and build truly agile, collaborative data environments. Teams can now connect data from all parts of the landscape, until there are no stones left unturned. They are even tapping into new datasets via live access to the Snowflake Marketplace. The Snowflake Data Cloud combined with a Data Vault 2.1 approach is allowing teams to democratize access to all their data assets at any scale. We can now easily derive more and more value through insights and intelligence, day after day, bringing businesses to the next level of being truly data-driven.  \n\nDelivering more usable data faster is no longer an option for today’s business environment. Using the Snowflake platform, combined with the Data Vault 2.1 architecture it is now possible to build a world class analytics platform that delivers data for all users in near real-time. \n\n### What we've covered\n- Unloading and loading back data using COPY and Snowpipe\n- Engineering data pipelines using virtualization, streams and tasks\n- Populating data in a multi-zone Data Vault on Snowflake: \n\n![Multi-Zone Data Vault Environment](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-data-vault/img19.png?v=7a7bb18b)  \n\n### Call to action\n- We made examples limited in size, but feel free to scale the data volumes and virtual warehouse size to see scalability in action.\n- Could we make the data vault perform faster by joining on business keys and not hash keys? Check out [this great blog post](/blog/tips-for-optimizing-the-data-vault-architecture-on-snowflake-part-3/).\n- Could we use triggered, serverless tasks, eliminating the need to poll the source? Check out [serverless triggered tasks](https://docs.snowflake.com/en/user-guide/tasks-triggered).\n- Tap into the numerous communities highlighting Snowflake and Data Vault.\n- Talk to us about modernizing your data landscape! We have the expertise and product to meet your needs.\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-c76eed0a61","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-c56ccdf1a0",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-197116c81d","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-07",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-2760dfd3e3","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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Data\u003C/h2\u003E","\u003Ch2\u003EReference Architecture and Environment Setup\u003C/h2\u003E","\u003Ch2\u003EData Pipelines: Design\u003C/h2\u003E","\u003Ch2\u003EBuild: Landing Zone, Sample Data and Staging for the Raw Vault\u003C/h2\u003E","\u003Ch2\u003EBuild: Data Vault - Raw Vault\u003C/h2\u003E","\u003Ch2\u003EViews for Agile Reporting\u003C/h2\u003E","\u003Ch2\u003EBuild: Data Vault - Business Vault\u003C/h2\u003E","\u003Ch2\u003EBuild: Information Delivery\u003C/h2\u003E","\u003Ch2\u003EStep 1: More Supporting Views in the Business 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