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Started with ABINZ Orbit on Snowflake","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EABINZ Orbit is a \u003Cstrong\u003ESnowflake Native App\u003C/strong\u003E that creates a governed semantic layer on top of your Snowflake tables.\u003C/p\u003E\n","\u003Cp\u003EIt automatically profiles your data, discovers relationships between tables, and generates a \u003Cstrong\u003ECortex Analyst semantic model\u003C/strong\u003E — so users can query data in plain English and receive SQL-backed results.\u003C/p\u003E\n","\u003Cp\u003EThe app runs entirely within your Snowflake account using the Native Apps framework. No data leaves your environment.\u003C/p\u003E\n","\u003Cp\u003EABINZ Orbit operates entirely within Snowflake using:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Native Apps framework\u003C/li\u003E\u003Cli\u003EServerless Tasks for background processing\u003C/li\u003E\u003Cli\u003ESnowflake Cortex for natural language query interpretation\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn most environments, this setup can be completed within an hour.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account (any cloud, any region)\u003C/li\u003E\u003Cli\u003EACCOUNTADMIN role for installation and initial grants\u003C/li\u003E\u003Cli\u003EAt least one database with tables you want to analyze\u003C/li\u003E\u003Cli\u003ESnowflake Cortex enabled in your account (required for AI query functionality)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to install a Snowflake Native App from the Marketplace\u003C/li\u003E\u003Cli\u003EHow to profile tables and detect PII automatically using Snowflake Serverless Tasks\u003C/li\u003E\u003Cli\u003EHow ABINZ Orbit discovers table relationships using AI-assisted heuristics\u003C/li\u003E\u003Cli\u003EHow to generate a Cortex Analyst semantic model from your data\u003C/li\u003E\u003Cli\u003EHow to query your data in plain English using Snowflake Cortex\u003C/li\u003E\u003Cli\u003E(Optional) How to use the Metrics Library to define and reference reusable business metrics\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA fully governed AI semantic layer on top of your Snowflake tables\u003C/li\u003E\u003Cli\u003EA Cortex Analyst model you can query in plain English\u003C/li\u003E\u003Cli\u003E(Optional) A KPI Outcome Contract to track business results over time\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E No sample data is required. ABINZ Orbit works with tables already in your Snowflake account. To follow along with a known dataset, use \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA.TPCH_SF1\u003C/code\u003E which is available in all Snowflake accounts.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInstall ABINZ Orbit from the Marketplace\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELog in to your Snowflake account at \u003Cstrong\u003Eapp.snowflake.com\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EData Products\u003C/strong\u003E → \u003Cstrong\u003EMarketplace\u003C/strong\u003E in the left sidebar\u003C/li\u003E\u003Cli\u003ESearch for \u003Cstrong\u003EABINZ Orbit\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EGet\u003C/strong\u003E on the listing\u003C/li\u003E\u003Cli\u003EChoose a warehouse (e.g., \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EGet\u003C/strong\u003E to confirm — the app installs in under a minute\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EOnce installed, navigate to \u003Cstrong\u003EData Products\u003C/strong\u003E → \u003Cstrong\u003EApps\u003C/strong\u003E → \u003Cstrong\u003EABINZ Orbit\u003C/strong\u003E to open it.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGrant the App Access to Your Data\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EABINZ Orbit can only read tables you explicitly grant it access to. Run the following as \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Grants are scoped only to the schemas you specify. ABINZ Orbit does not have access to any data unless explicitly granted.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Grant access to a specific database and schema\nGRANT USAGE ON DATABASE <YOUR_DATABASE> TO APPLICATION ABINZ_ORBIT;\nGRANT USAGE ON SCHEMA <YOUR_DATABASE>.<YOUR_SCHEMA> TO APPLICATION ABINZ_ORBIT;\nGRANT SELECT ON ALL TABLES IN SCHEMA <YOUR_DATABASE>.<YOUR_SCHEMA> TO APPLICATION ABINZ_ORBIT;\n\n-- (Optional) Include future tables automatically\nGRANT SELECT ON FUTURE TABLES IN SCHEMA <YOUR_DATABASE>.<YOUR_SCHEMA> TO APPLICATION ABINZ_ORBIT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample using Snowflake sample data:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT USAGE ON DATABASE SNOWFLAKE_SAMPLE_DATA TO APPLICATION ABINZ_ORBIT;\nGRANT USAGE ON SCHEMA SNOWFLAKE_SAMPLE_DATA.TPCH_SF1 TO APPLICATION ABINZ_ORBIT;\nGRANT SELECT ON ALL TABLES IN SCHEMA SNOWFLAKE_SAMPLE_DATA.TPCH_SF1 TO APPLICATION ABINZ_ORBIT;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGrant App Roles to Your Users\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Cp\u003EABINZ Orbit has two application roles:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWho gets it\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECapabilities\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EABINZ_APP_ADMIN_ROLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYou (the admin)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate/delete workspaces, start workers, manage settings\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EABINZ_APP_ANALYST_ROLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour team members\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate workspaces, run analysis, ask AI questions\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EGrant analyst access using the built-in stored procedure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL ABINZ_ORBIT.ABINZ_KG.GRANT_ANALYST_ACCESS_TO_USER('<YOUR_SNOWFLAKE_USERNAME>');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo assign admin-level access, grant the application role directly:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT APPLICATION ROLE ABINZ_ORBIT.ABINZ_APP_ADMIN_ROLE TO ROLE <ACCOUNT_ROLE_NAME>;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStart the Background Worker\u003C/h2\u003E\n","\u003Cp\u003EDuration: 1\u003C/p\u003E\n","\u003Cp\u003EABINZ Orbit uses a \u003Cstrong\u003ESnowflake Serverless Task\u003C/strong\u003E — no warehouse needed — to process metadata in the background. This is a one-time setup:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL ABINZ_ORBIT.ABINZ_KG.START_BACKGROUND_WORKERS();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates a task that runs every 60 seconds to process analysis jobs in the background. The task runs on a schedule and processes jobs only when work is available.\u003C/p\u003E\n","\u003Cp\u003EVerify the worker is running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW TASKS IN APPLICATION ABINZ_ORBIT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA healthy output shows the task with \u003Ccode\u003Estate = started\u003C/code\u003E and a recent \u003Ccode\u003Escheduled_time\u003C/code\u003E timestamp. If state shows \u003Ccode\u003Esuspended\u003C/code\u003E, re-run the \u003Ccode\u003ESTART_BACKGROUND_WORKERS\u003C/code\u003E call.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Starting the worker now ensures it is ready to pick up jobs the moment you add tables in the next steps.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E If no tables move out of \u003Ccode\u003EPENDING\u003C/code\u003E, verify that the background worker is running and that required privileges were granted.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate a Workspace\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERole note:\u003C/strong\u003E From this step onward, ACCOUNTADMIN is no longer required. Proceed as a user with \u003Ccode\u003EABINZ_APP_ADMIN_ROLE\u003C/code\u003E or \u003Ccode\u003EABINZ_APP_ANALYST_ROLE\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EA workspace is an isolated project that holds your analysis for a specific database and schema combination.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen ABINZ Orbit — you land on the \u003Cstrong\u003EHome\u003C/strong\u003E module\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate New Workspace\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EName it something descriptive, e.g., \u003Ccode\u003EMy First Workspace\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EAll analysis, relationships, and AI queries in this workspace are isolated from other workspaces.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnect Your Data Source\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGo to the \u003Cstrong\u003EData Sources\u003C/strong\u003E module (left sidebar)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EBrowse Databases\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect the database and schema you granted access to in the \u003Cstrong\u003EGrant the App Access\u003C/strong\u003E step\u003C/li\u003E\u003Cli\u003ESelect the tables you want to include — you can pick individual tables or include all\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EAdd to Workspace\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EOnce you add tables, ABINZ Orbit automatically starts analyzing them in the background.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun Metadata Analysis\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EThe background worker processes your tables automatically. For each table it:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECounts rows and columns\u003C/li\u003E\u003Cli\u003EProfiles each column: data type, cardinality, null rate, sample values\u003C/li\u003E\u003Cli\u003EFlags potential PII columns (e.g., SSN, DOB, email) based on column names and naming patterns — flagging is \u003Cstrong\u003Einformational only\u003C/strong\u003E and does not enforce access restrictions\u003C/li\u003E\u003Cli\u003ECalculates a \u003Cstrong\u003Etrust score\u003C/strong\u003E (0–100) based on completeness and consistency\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWatch progress in the \u003Cstrong\u003EData Sources\u003C/strong\u003E module:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStatus\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMeaning\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EPENDING\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQueued, waiting for worker\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ERUNNING\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECurrently being analyzed\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECOMPLETED\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAnalysis done, ready to use\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFAILED\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EError — check the table for issues\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EAnalysis typically completes in 1–3 minutes per table, depending on table size and warehouse performance.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELarge tables:\u003C/strong\u003E For tables over 10,000 rows, ABINZ Orbit automatically samples 500 rows to keep analysis fast. Full row counts are still captured.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDiscover Relationships\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EOnce at least two tables are analyzed, go to the \u003Cstrong\u003ERelationships\u003C/strong\u003E module and click \u003Cstrong\u003EDiscover Relationships\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EABINZ Orbit's NAME_MATCH heuristic:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECompares column names across tables (e.g., \u003Ccode\u003Eorder_key\u003C/code\u003E in one table → \u003Ccode\u003Eorder_key\u003C/code\u003E in another)\u003C/li\u003E\u003Cli\u003EChecks data types and sample value overlap\u003C/li\u003E\u003Cli\u003EAssigns a \u003Cstrong\u003Econfidence score\u003C/strong\u003E from 0.0 to 1.0\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis approach enables relationship discovery even when foreign keys are not explicitly defined.\u003C/p\u003E\n","\u003Cp\u003EHigh-confidence relationships are automatically accepted (default threshold: 0.7, configurable in Settings). Lower the threshold (e.g., 0.5) to surface more candidates for manual review; raise it (e.g., 0.85) to accept only the highest-certainty matches automatically.\u003C/p\u003E\n","\u003Cp\u003EYou can:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReview and confirm suggested joins\u003C/li\u003E\u003Cli\u003EAdd manual relationships the heuristic missed\u003C/li\u003E\u003Cli\u003ERemove false positives\u003C/li\u003E\u003Cli\u003EAdjust the confidence threshold in Settings\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Relationship discovery typically completes in 1–2 minutes. For schemas with many tables, it may take slightly longer — this is normal.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAccepted relationships feed directly into the semantic model generation step.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate a Cortex Analyst Semantic Model\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EGo to the \u003Cstrong\u003EModel Preview\u003C/strong\u003E module and click \u003Cstrong\u003EGenerate Semantic Model\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EABINZ Orbit builds a Cortex Analyst-compatible YAML model from your tables and confirmed relationships. The model defines:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETables\u003C/strong\u003E with human-readable descriptions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EColumns\u003C/strong\u003E with business-friendly names and data types\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EJoins\u003C/strong\u003E based on confirmed relationships\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMetrics\u003C/strong\u003E for common aggregations (count, sum, average)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou can review and edit the YAML directly in the module before saving. The model is stored in your Snowflake account — Cortex Analyst uses it to translate plain English into SQL.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAsk a Question in Plain English\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EGo to the \u003Cstrong\u003EAI Assistant\u003C/strong\u003E module and type a question in the chat box. For example:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cem\u003E"What are the top 10 orders by total price?"\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cem\u003E"Show me the count of line items by status."\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cem\u003E"Which parts have the highest average supply cost?"\u003C/em\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EABINZ Orbit uses \u003Cstrong\u003ESnowflake Cortex\u003C/strong\u003E to interpret your question using the semantic model:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGenerates a SQL query based on your intent\u003C/li\u003E\u003Cli\u003EExecutes it inside your Snowflake account\u003C/li\u003E\u003Cli\u003EReturns results in a table and natural language summary\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIf the generated SQL fails (e.g., due to a schema change), the \u003Cstrong\u003ESelf-Healing SQL\u003C/strong\u003E engine attempts to detect the error, adjust the query, and retry execution using updated schema context — without any action from you.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E(Optional) Set a KPI Outcome Contract\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EBeyond querying data, Orbit also lets you track whether insights translate into real business outcomes.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis step is optional and not required for querying data.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EKPI Studio\u003C/strong\u003E module lets you define a KPI, set a target, and measure actual lift over time.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGo to \u003Cstrong\u003EKPI Studio\u003C/strong\u003E → \u003Cstrong\u003EOutcome Contracts\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ENew Contract\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EDefine your KPI (e.g., "Order Fulfillment Rate")\u003C/li\u003E\u003Cli\u003ESet a baseline value and a target\u003C/li\u003E\u003Cli\u003ELog the intervention you are testing\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave Contract\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EABINZ Orbit tracks the KPI over time and shows the measured lift — the actual change vs. your target — giving you a quantifiable record of business outcomes.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E(Optional) Use the Metrics Library\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Cp\u003EGo to the \u003Cstrong\u003EMetrics Library\u003C/strong\u003E module to find pre-defined reusable metrics. You can:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBrowse metrics by type: \u003Ccode\u003ESIMPLE\u003C/code\u003E, \u003Ccode\u003EDERIVED\u003C/code\u003E, \u003Ccode\u003ERATIO\u003C/code\u003E, \u003Ccode\u003ECUMULATIVE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EAdd custom metrics relevant to your business\u003C/li\u003E\u003Cli\u003EReference metrics in AI queries by name (e.g., "Show me order_fulfillment_rate by month")\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EDuration: 1\u003C/p\u003E\n","\u003Cp\u003EYou have successfully:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EInstalled ABINZ Orbit as a Snowflake Native App with no additional infrastructure\u003C/li\u003E\u003Cli\u003EConnected your data without moving it outside Snowflake\u003C/li\u003E\u003Cli\u003EProfiled tables and flagged potential PII automatically\u003C/li\u003E\u003Cli\u003EDiscovered table joins using AI-assisted relationship matching\u003C/li\u003E\u003Cli\u003EGenerated a Cortex Analyst semantic model\u003C/li\u003E\u003Cli\u003EQueried your data in plain English using Snowflake Cortex\u003C/li\u003E\u003Cli\u003E(Optionally) set up KPI tracking with Outcome Contracts\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAll of this ran entirely inside your Snowflake account. No data left your environment, and no external services were required. Your existing Snowflake governance, RBAC, and encryption policies continue to apply automatically.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to install and configure a Snowflake Native App\u003C/li\u003E\u003Cli\u003EHow ABINZ Orbit profiles tables and detects PII using Snowflake Serverless Tasks\u003C/li\u003E\u003Cli\u003EHow AI-assisted relationship discovery works and how to tune confidence thresholds\u003C/li\u003E\u003Cli\u003EHow to generate and use a Cortex Analyst semantic model\u003C/li\u003E\u003Cli\u003EHow to query Snowflake data in plain English with Self-Healing SQL\u003C/li\u003E\u003Cli\u003E(Optional) How to use the Metrics Library to define and track reusable business metrics\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EInstall ABINZ Orbit from the Snowflake Marketplace and connect your first dataset to get started.\u003C/p\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZU6Z1HLBPAXN/abinz-llc-abinz-orbit\"\u003EABINZ Orbit — Free Trial on Snowflake Marketplace\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZU6Z1HLBPBCL/abinz-llc-abinz-orbit-enterprise-edition\"\u003EABINZ Orbit Enterprise Edition\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/abinz-aiml/abinz-orbit-quickstart\"\u003EFull Quickstart on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview\"\u003ESnowflake Cortex AI Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about\"\u003ESnowflake Native Apps Framework\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://abinz.ai\"\u003Eabinz.ai\u003C/a\u003E | \u003Ca href=\"mailto:info@abinz.ai\"\u003Einfo@abinz.ai\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview\nDuration: 5\n\nABINZ Orbit is a **Snowflake Native App** that creates a governed semantic layer on top of your Snowflake tables.\n\nIt automatically profiles your data, discovers relationships between tables, and generates a **Cortex Analyst semantic model** — so users can query data in plain English and receive SQL-backed results.\n\nThe app runs entirely within your Snowflake account using the Native Apps framework. No data leaves your environment.\n\nABINZ Orbit operates entirely within Snowflake using:\n- Snowflake Native Apps framework\n- Serverless Tasks for background processing\n- Snowflake Cortex for natural language query interpretation\n\nIn most environments, this setup can be completed within an hour.\n\n### Prerequisites\n- A Snowflake account (any cloud, any region)\n- ACCOUNTADMIN role for installation and initial grants\n- At least one database with tables you want to analyze\n- Snowflake Cortex enabled in your account (required for AI query functionality)\n\n### What You'll Learn\n- How to install a Snowflake Native App from the Marketplace\n- How to profile tables and detect PII automatically using Snowflake Serverless Tasks\n- How ABINZ Orbit discovers table relationships using AI-assisted heuristics\n- How to generate a Cortex Analyst semantic model from your data\n- How to query your data in plain English using Snowflake Cortex\n- (Optional) How to use the Metrics Library to define and reference reusable business metrics\n\n### What You'll Build\n- A fully governed AI semantic layer on top of your Snowflake tables\n- A Cortex Analyst model you can query in plain English\n- (Optional) A KPI Outcome Contract to track business results over time\n\n\u003E **Note:** No sample data is required. ABINZ Orbit works with tables already in your Snowflake account. To follow along with a known dataset, use `SNOWFLAKE_SAMPLE_DATA.TPCH_SF1` which is available in all Snowflake accounts.\n\n\u003C!-- ------------------------ --\u003E\n## Install ABINZ Orbit from the Marketplace\nDuration: 2\n\n1. Log in to your Snowflake account at **app.snowflake.com**\n2. Click **Data Products** → **Marketplace** in the left sidebar\n3. Search for **ABINZ Orbit**\n4. Click **Get** on the listing\n5. Choose a warehouse (e.g., `COMPUTE_WH`)\n6. Click **Get** to confirm — the app installs in under a minute\n\nOnce installed, navigate to **Data Products** → **Apps** → **ABINZ Orbit** to open it.\n\n\u003C!-- ------------------------ --\u003E\n## Grant the App Access to Your Data\nDuration: 3\n\nABINZ Orbit can only read tables you explicitly grant it access to. Run the following as **ACCOUNTADMIN**:\n\n\u003E **Important:** Grants are scoped only to the schemas you specify. ABINZ Orbit does not have access to any data unless explicitly granted.\n\n```sql\n-- Grant access to a specific database and schema\nGRANT USAGE ON DATABASE \u003CYOUR_DATABASE\u003E TO APPLICATION ABINZ_ORBIT;\nGRANT USAGE ON SCHEMA \u003CYOUR_DATABASE\u003E.\u003CYOUR_SCHEMA\u003E TO APPLICATION ABINZ_ORBIT;\nGRANT SELECT ON ALL TABLES IN SCHEMA \u003CYOUR_DATABASE\u003E.\u003CYOUR_SCHEMA\u003E TO APPLICATION ABINZ_ORBIT;\n\n-- (Optional) Include future tables automatically\nGRANT SELECT ON FUTURE TABLES IN SCHEMA \u003CYOUR_DATABASE\u003E.\u003CYOUR_SCHEMA\u003E TO APPLICATION ABINZ_ORBIT;\n```\n\n**Example using Snowflake sample data:**\n\n```sql\nGRANT USAGE ON DATABASE SNOWFLAKE_SAMPLE_DATA TO APPLICATION ABINZ_ORBIT;\nGRANT USAGE ON SCHEMA SNOWFLAKE_SAMPLE_DATA.TPCH_SF1 TO APPLICATION ABINZ_ORBIT;\nGRANT SELECT ON ALL TABLES IN SCHEMA SNOWFLAKE_SAMPLE_DATA.TPCH_SF1 TO APPLICATION ABINZ_ORBIT;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Grant App Roles to Your Users\nDuration: 2\n\nABINZ Orbit has two application roles:\n\n| Role | Who gets it | Capabilities |\n|---|---|---|\n| `ABINZ_APP_ADMIN_ROLE` | You (the admin) | Create/delete workspaces, start workers, manage settings |\n| `ABINZ_APP_ANALYST_ROLE` | Your team members | Create workspaces, run analysis, ask AI questions |\n\nGrant analyst access using the built-in stored procedure:\n\n```sql\nCALL ABINZ_ORBIT.ABINZ_KG.GRANT_ANALYST_ACCESS_TO_USER('\u003CYOUR_SNOWFLAKE_USERNAME\u003E');\n```\n\nTo assign admin-level access, grant the application role directly:\n\n```sql\nGRANT APPLICATION ROLE ABINZ_ORBIT.ABINZ_APP_ADMIN_ROLE TO ROLE \u003CACCOUNT_ROLE_NAME\u003E;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Start the Background Worker\nDuration: 1\n\nABINZ Orbit uses a **Snowflake Serverless Task** — no warehouse needed — to process metadata in the background. This is a one-time setup:\n\n```sql\nCALL ABINZ_ORBIT.ABINZ_KG.START_BACKGROUND_WORKERS();\n```\n\nThis creates a task that runs every 60 seconds to process analysis jobs in the background. The task runs on a schedule and processes jobs only when work is available.\n\nVerify the worker is running:\n\n```sql\nSHOW TASKS IN APPLICATION ABINZ_ORBIT;\n```\n\nA healthy output shows the task with `state = started` and a recent `scheduled_time` timestamp. If state shows `suspended`, re-run the `START_BACKGROUND_WORKERS` call.\n\n\u003E **Note:** Starting the worker now ensures it is ready to pick up jobs the moment you add tables in the next steps.\n\n\u003E **Tip:** If no tables move out of `PENDING`, verify that the background worker is running and that required privileges were granted.\n\n\u003C!-- ------------------------ --\u003E\n## Create a Workspace\nDuration: 2\n\n\u003E **Role note:** From this step onward, ACCOUNTADMIN is no longer required. Proceed as a user with `ABINZ_APP_ADMIN_ROLE` or `ABINZ_APP_ANALYST_ROLE`.\n\nA workspace is an isolated project that holds your analysis for a specific database and schema combination.\n\n1. Open ABINZ Orbit — you land on the **Home** module\n2. Click **Create New Workspace**\n3. Name it something descriptive, e.g., `My First Workspace`\n4. Click **Create**\n\nAll analysis, relationships, and AI queries in this workspace are isolated from other workspaces.\n\n\u003C!-- ------------------------ --\u003E\n## Connect Your Data Source\nDuration: 2\n\n1. Go to the **Data Sources** module (left sidebar)\n2. Click **Browse Databases**\n3. Select the database and schema you granted access to in the **Grant the App Access** step\n4. Select the tables you want to include — you can pick individual tables or include all\n5. Click **Add to Workspace**\n\nOnce you add tables, ABINZ Orbit automatically starts analyzing them in the background.\n\n\u003C!-- ------------------------ --\u003E\n## Run Metadata Analysis\nDuration: 5\n\nThe background worker processes your tables automatically. For each table it:\n\n- Counts rows and columns\n- Profiles each column: data type, cardinality, null rate, sample values\n- Flags potential PII columns (e.g., SSN, DOB, email) based on column names and naming patterns — flagging is **informational only** and does not enforce access restrictions\n- Calculates a **trust score** (0–100) based on completeness and consistency\n\nWatch progress in the **Data Sources** module:\n\n| Status | Meaning |\n|---|---|\n| `PENDING` | Queued, waiting for worker |\n| `RUNNING` | Currently being analyzed |\n| `COMPLETED` | Analysis done, ready to use |\n| `FAILED` | Error — check the table for issues |\n\nAnalysis typically completes in 1–3 minutes per table, depending on table size and warehouse performance.\n\n\u003E **Large tables:** For tables over 10,000 rows, ABINZ Orbit automatically samples 500 rows to keep analysis fast. Full row counts are still captured.\n\n\u003C!-- ------------------------ --\u003E\n## Discover Relationships\nDuration: 5\n\nOnce at least two tables are analyzed, go to the **Relationships** module and click **Discover Relationships**.\n\nABINZ Orbit's NAME_MATCH heuristic:\n- Compares column names across tables (e.g., `order_key` in one table → `order_key` in another)\n- Checks data types and sample value overlap\n- Assigns a **confidence score** from 0.0 to 1.0\n\nThis approach enables relationship discovery even when foreign keys are not explicitly defined.\n\nHigh-confidence relationships are automatically accepted (default threshold: 0.7, configurable in Settings). Lower the threshold (e.g., 0.5) to surface more candidates for manual review; raise it (e.g., 0.85) to accept only the highest-certainty matches automatically.\n\nYou can:\n- Review and confirm suggested joins\n- Add manual relationships the heuristic missed\n- Remove false positives\n- Adjust the confidence threshold in Settings\n\n\u003E **Note:** Relationship discovery typically completes in 1–2 minutes. For schemas with many tables, it may take slightly longer — this is normal.\n\nAccepted relationships feed directly into the semantic model generation step.\n\n\u003C!-- ------------------------ --\u003E\n## Generate a Cortex Analyst Semantic Model\nDuration: 3\n\nGo to the **Model Preview** module and click **Generate Semantic Model**.\n\nABINZ Orbit builds a Cortex Analyst-compatible YAML model from your tables and confirmed relationships. The model defines:\n\n- **Tables** with human-readable descriptions\n- **Columns** with business-friendly names and data types\n- **Joins** based on confirmed relationships\n- **Metrics** for common aggregations (count, sum, average)\n\nYou can review and edit the YAML directly in the module before saving. The model is stored in your Snowflake account — Cortex Analyst uses it to translate plain English into SQL.\n\n\u003C!-- ------------------------ --\u003E\n## Ask a Question in Plain English\nDuration: 3\n\nGo to the **AI Assistant** module and type a question in the chat box. For example:\n\n- *\"What are the top 10 orders by total price?\"*\n- *\"Show me the count of line items by status.\"*\n- *\"Which parts have the highest average supply cost?\"*\n\nABINZ Orbit uses **Snowflake Cortex** to interpret your question using the semantic model:\n\n1. Generates a SQL query based on your intent\n2. Executes it inside your Snowflake account\n3. Returns results in a table and natural language summary\n\nIf the generated SQL fails (e.g., due to a schema change), the **Self-Healing SQL** engine attempts to detect the error, adjust the query, and retry execution using updated schema context — without any action from you.\n\n\u003C!-- ------------------------ --\u003E\n## (Optional) Set a KPI Outcome Contract\nDuration: 3\n\nBeyond querying data, Orbit also lets you track whether insights translate into real business outcomes.\n\n\u003E This step is optional and not required for querying data.\n\nThe **KPI Studio** module lets you define a KPI, set a target, and measure actual lift over time.\n\n1. Go to **KPI Studio** → **Outcome Contracts**\n2. Click **New Contract**\n3. Define your KPI (e.g., \"Order Fulfillment Rate\")\n4. Set a baseline value and a target\n5. Log the intervention you are testing\n6. Click **Save Contract**\n\nABINZ Orbit tracks the KPI over time and shows the measured lift — the actual change vs. your target — giving you a quantifiable record of business outcomes.\n\n\u003C!-- ------------------------ --\u003E\n## (Optional) Use the Metrics Library\nDuration: 2\n\nGo to the **Metrics Library** module to find pre-defined reusable metrics. You can:\n\n- Browse metrics by type: `SIMPLE`, `DERIVED`, `RATIO`, `CUMULATIVE`\n- Add custom metrics relevant to your business\n- Reference metrics in AI queries by name (e.g., \"Show me order_fulfillment_rate by month\")\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\nDuration: 1\n\nYou have successfully:\n\n- Installed ABINZ Orbit as a Snowflake Native App with no additional infrastructure\n- Connected your data without moving it outside Snowflake\n- Profiled tables and flagged potential PII automatically\n- Discovered table joins using AI-assisted relationship matching\n- Generated a Cortex Analyst semantic model\n- Queried your data in plain English using Snowflake Cortex\n- (Optionally) set up KPI tracking with Outcome Contracts\n\nAll of this ran entirely inside your Snowflake account. No data left your environment, and no external services were required. Your existing Snowflake governance, RBAC, and encryption policies continue to apply automatically.\n\n### What You Learned\n- How to install and configure a Snowflake Native App\n- How ABINZ Orbit profiles tables and detects PII using Snowflake Serverless Tasks\n- How AI-assisted relationship discovery works and how to tune confidence thresholds\n- How to generate and use a Cortex Analyst semantic model\n- How to query Snowflake data in plain English with Self-Healing SQL\n- (Optional) How to use the Metrics Library to define and track reusable business metrics\n\nInstall ABINZ Orbit from the Snowflake Marketplace and connect your first dataset to get started.\n\n### Related Resources\n- [ABINZ Orbit — Free Trial on Snowflake Marketplace](https://app.snowflake.com/marketplace/listing/GZU6Z1HLBPAXN/abinz-llc-abinz-orbit)\n- [ABINZ Orbit Enterprise Edition](https://app.snowflake.com/marketplace/listing/GZU6Z1HLBPBCL/abinz-llc-abinz-orbit-enterprise-edition)\n- [Full Quickstart on GitHub](https://github.com/abinz-aiml/abinz-orbit-quickstart)\n- [Snowflake Cortex AI Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview)\n- [Cortex Analyst Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)\n- [Snowflake Native Apps Framework](https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about)\n- [abinz.ai](https://abinz.ai) | [info@abinz.ai](mailto:info@abinz.ai)\n","title":"Quickstart Article Body",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c6d1840e9d","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-ee07ad4e8a","layout":"SIMPLE",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-d0bffee2f9","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-27",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-0d1180d17b","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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ABINZ Orbit from the Marketplace\u003C/h2\u003E","\u003Ch2\u003EGrant the App Access to Your Data\u003C/h2\u003E","\u003Ch2\u003EGrant App Roles to Your Users\u003C/h2\u003E","\u003Ch2\u003EStart the Background Worker\u003C/h2\u003E","\u003Ch2\u003ECreate a Workspace\u003C/h2\u003E","\u003Ch2\u003EConnect Your Data Source\u003C/h2\u003E","\u003Ch2\u003ERun Metadata Analysis\u003C/h2\u003E","\u003Ch2\u003EDiscover Relationships\u003C/h2\u003E","\u003Ch2\u003EGenerate a Cortex Analyst Semantic Model\u003C/h2\u003E","\u003Ch2\u003EAsk a Question in Plain English\u003C/h2\u003E","\u003Ch2\u003E(Optional) Set a KPI Outcome Contract\u003C/h2\u003E","\u003Ch2\u003E(Optional) Use the Metrics Library\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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class=\"sf-footer__column-title\"\u003EProduct\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/platform/\"\u003EPlatform\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/data-engineering/\"\u003EData Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/ai/\"\u003EAI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/pricing-options/\"\u003EPricing\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"text_copy":{"id":"text-eb09d809d9","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStatus\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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