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of SnowConvert AI, Snowpark Migration Accelerator, and Datometry into a single AI-powered platform for assessing, modernizing, and migrating enterprise data and code workloads to Snowflake. Designed to reduce the complexity, risk, and operational overhead traditionally associated with large-scale migrations, Snowflake AIM enables organizations to define their target state while the platform orchestrates the migration process end to end. From analyzing Spark code and converting APIs to Snowpark, to modernizing warehouses, tables, views, ETL pipelines, reporting assets, and stored procedures across major platforms, Snowflake AIM combines intelligent assessment, automated code conversion, dependency mapping, orchestration, and virtualization into a unified migration experience. By eliminating the need to rebuild workloads from scratch or maintain parallel legacy environments, Snowflake AIM dramatically accelerates modernization timelines while minimizing disruption to ongoing business operations.\u003C/p\u003E\n","\u003Cp\u003EThrough this guide you will learn how to do an end-to-end migration of a Microsoft SQL Server environment with SSIS to Snowflake using Snowflake AIM, the Cortex Code CLI, and the Snowflake CLI. This guide walks you through project setup, extracting your source DDLs, converting them, leveraging AI to fix remaining issues in the converted code, deploying objects, migrating historical data with a \u003Cstrong\u003Elocally-run orchestrator + worker\u003C/strong\u003E, and finally replatforming your SSIS ETL pipelines into a Snowflake task graph plus a Snowflake-native dbt project.\u003C/p\u003E\n","\u003Cp\u003EThe lab is driven entirely from a single Cortex Code session — at each step you tell the assistant what you want and the bundled \u003Ccode\u003Esnowflake-migration:migration\u003C/code\u003E skill orchestrates the underlying \u003Ccode\u003Escai\u003C/code\u003E / \u003Ccode\u003Esnow\u003C/code\u003E / SQL calls.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowflake SQL.\u003C/li\u003E\u003Cli\u003EFamiliarity with Microsoft SQL Server and SSIS.\u003C/li\u003E\u003Cli\u003EFamiliarity with dbt Projects on Snowflake and Snowflake tasks.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n","\u003Cp\u003EBy the end of this guide, you will learn to work with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe SnowConvert AI CLI to perform end-to-end migrations of SQL Server (code conversion, data migration, ETL replatform).\u003C/li\u003E\u003Cli\u003EThe Cortex Code CLI and its bundled \u003Ccode\u003Esnowflake-migration:migration\u003C/code\u003E skill for orchestrating every phase of the migration journey.\u003C/li\u003E\u003Cli\u003EThe Snowflake CLI's \u003Ccode\u003Esnow dbt deploy\u003C/code\u003E command to publish a Snowflake-native dbt project produced by SnowConvert.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA SQL Server database where you have full read and write permissions.\u003C/li\u003E\u003Cli\u003EA Snowflake account with the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/introduction/introduction\"\u003ESnowflake CLI\u003C/a\u003E installed.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/general/user-guide/snowconvert/command-line-interface/README\"\u003ESnowConvert AI CLI\u003C/a\u003E installed.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECortex Code CLI\u003C/a\u003E installed.\u003C/li\u003E\u003Cli\u003EA text editor (we recommend \u003Ca href=\"https://code.visualstudio.com/\"\u003EVisual Studio Code\u003C/a\u003E).\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EAn end-to-end migration of a SQL Server database and an SSIS package to Snowflake, including: code conversion, hand-fixed deploy/test/fix loop, data migration using a \u003Cstrong\u003Elocally-run\u003C/strong\u003E Data Migration & Validation orchestrator + worker, and deployment of an SSIS-derived Snowflake task graph that calls a Snowflake-native dbt project.\u003C/p\u003E\n","\u003Ch2\u003EProject Setup\u003C/h2\u003E\n","\u003Ch3\u003EClone the Quickstart Repository\u003C/h3\u003E\n","\u003Cp\u003EAll of the source scripts, the SSIS package, and the SQL used in this quickstart live in the \u003Ccode\u003E[sfguide-scai-e2e-ssis-migration](https://github.com/Snowflake-Labs/sfguide-scai-e2e-ssis-migration)\u003C/code\u003E repository. Clone it to your local machine before continuing:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-scai-e2e-ssis-migration.git\ncd sfguide-scai-e2e-ssis-migration\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe repository contains:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Esourcedb/00_ddl.sql\u003C/code\u003E and \u003Ccode\u003Esourcedb/01_data.sql\u003C/code\u003E — SQL Server DDL and sample data for the TastyBytes database.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esnowflake/init.sql\u003C/code\u003E — initial Snowflake database and schemas.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eetl/daily_sales_agg.dtsx\u003C/code\u003E — the SSIS package you will migrate to Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELater steps reference these paths (for example, when Cortex Code asks for the filesystem path to your SSIS folder, you will point it at this repository's \u003Ccode\u003Eetl/\u003C/code\u003E directory).\u003C/p\u003E\n","\u003Ch3\u003ESQL Server DDL and Data\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn the SQL Server instance you will be using for this quickstart, run the \u003Ccode\u003Esourcedb/00_ddl.sql\u003C/code\u003E script to create the database, schema, and DDLs that we will be converting.\u003C/li\u003E\u003Cli\u003EOnce the DDLs have been deployed successfully, run the \u003Ccode\u003Esourcedb/01_data.sql\u003C/code\u003E script to insert sample data into the tables of the TastyBytes database.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESnowflake Account\u003C/h3\u003E\n","\u003Cp\u003EIn your Snowflake account, run the \u003Ccode\u003Esnowflake/init.sql\u003C/code\u003E script to create the initial database (\u003Ccode\u003ETASTYBYTESDB\u003C/code\u003E) and warehouse (\u003Ccode\u003EXSMALL_WH\u003C/code\u003E) we will deploy into. Because this lab runs the data-migration orchestrator and worker \u003Cstrong\u003Elocally\u003C/strong\u003E, you do \u003Cstrong\u003Enot\u003C/strong\u003E need to create an SPCS compute pool.\u003C/p\u003E\n","\u003Ch3\u003ESnowflake Connection\u003C/h3\u003E\n","\u003Cp\u003ETo enable the Snowflake CLI and SnowConvert AI to run against our Snowflake account, we need to add the connection details to the \u003Ccode\u003Econfig.toml\u003C/code\u003E file used by both CLIs. Follow the steps in this \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-cli\"\u003Eguide\u003C/a\u003E to set up your \u003Ccode\u003Econfig.toml\u003C/code\u003E with your connection details.\u003C/p\u003E\n","\u003Cp\u003ETest the connection:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection test --connection <YOUR_SNOWFLAKE_CONNECTION_NAME>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn this quickstart we use a Snowflake connection named \u003Ccode\u003E**migrations_sc**\u003C/code\u003E configured with warehouse \u003Ccode\u003EXSMALL_WH\u003C/code\u003E, database \u003Ccode\u003ETASTYBYTESDB\u003C/code\u003E, schema \u003Ccode\u003ETASTYBYTES\u003C/code\u003E, and role \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ESnowConvert AI Source Connection\u003C/h3\u003E\n","\u003Cp\u003ERun the following command to register your SQL Server connection with SnowConvert AI so it can later extract your code and migrate your data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Escai connection add-sql-server\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Escai connection test -l sqlserver -s <YOUR_SQL_SERVER_CONNECTION_NAME>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see a \u003Ccode\u003EStatus: Success\u003C/code\u003E row in the output. We use a SQL Server connection named \u003Ccode\u003E**tastybytesdb**\u003C/code\u003E throughout this quickstart.\u003C/p\u003E\n","\u003Ch2\u003EDrive the Migration with Cortex Code\u003C/h2\u003E\n","\u003Cp\u003ERather than running each \u003Ccode\u003Escai\u003C/code\u003E command manually, we drive the entire migration from a single Cortex Code session. Cortex Code ships with a \u003Ccode\u003Esnowflake-migration:migration\u003C/code\u003E skill that orchestrates project initialization, source extraction, conversion, assessment, deployment, data migration, and ETL replatforming through a guided conversation.\u003C/p\u003E\n","\u003Cp\u003EThe sections below show the \u003Cstrong\u003Eexact prompts you'll send during the lab\u003C/strong\u003E and the answers we selected for this quickstart, in order.\u003C/p\u003E\n","\u003Ch3\u003EStart Cortex Code\u003C/h3\u003E\n","\u003Cp\u003EFrom the directory where you want to host the migration project (for example, an existing \u003Ccode\u003Emigrations_hol_summit/\u003C/code\u003E folder), launch Cortex Code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPrompt 1 — Kick Off the End-to-End Migration\u003C/h3\u003E\n","\u003Cp\u003ETell the assistant exactly what you want, including how the data infrastructure should be deployed and what to skip:\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003EMigrate my SQL Server workload and SSIS packages to Snowflake by doing\na full end-to-end migration from scratch. I want you to connect to my\nSQL Server database using the `tastybytesdb` connection registered\nalready in `scai` to extract my SQL code and migrate the data from my\ntables. Make sure to setup data migration infrastructure locally and\nalso do not do ETL stabilization. When the tables are deployed and\ntheir data is migrated, do not validate their data, \nskip to deployment and validation of the views, functions, and procedures.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis prompt does four important things in one shot:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EPins the SQL Server source to the existing \u003Ccode\u003Etastybytesdb\u003C/code\u003E connection.\u003C/li\u003E\u003Cli\u003EAsks for \u003Cstrong\u003Elocal\u003C/strong\u003E data-migration infrastructure (orchestrator + worker on the host machine — no SPCS / compute pool needed).\u003C/li\u003E\u003Cli\u003ETells Cortex Code to \u003Cstrong\u003Eskip ETL stabilization\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ETells Cortex Code to \u003Cstrong\u003Eskip data validation\u003C/strong\u003E after the data lands and jump straight to the deploy/test/fix loop.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe skill prints:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWelcome to the Snowflake Migrations plugin.\u003C/strong\u003E Let me get started by configuring your session.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIt then walks through a series of one-line prompts. Answer them as below.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 — Initial Configuration Prompts\u003C/h3\u003E\n","\u003Cp\u003ECortex Code asks four short questions before it touches any code:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestion\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAnswer\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnable the local migration dashboard at \u003Ccode\u003Ehttp://127.0.0.1:7878\u003C/code\u003E?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EYes\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELets you watch the migration progress in a browser.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReduce permission prompts? Allowlist 12 read-only MCP tools in \u003Ccode\u003Esettings.json\u003C/code\u003E.\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAllow\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAvoids being prompted for every read-only MCP call.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhich source database are you migrating from?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESQL Server\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAre you starting a new migration, or do you already have pre-converted Snowflake SQL?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EStarting fresh\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EIt then confirms the active Snowflake connection and asks for the target database:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestion\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAnswer\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe active Snowflake connection is \u003Ccode\u003Emigrations_sc\u003C/code\u003E. Use it for this migration?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EYes, use \u003Ccode\u003Emigrations_sc\u003C/code\u003E\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhich Snowflake database should migrated objects be deployed to?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E**TASTYBYTESDB**\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ECortex Code verifies that \u003Ccode\u003ETASTYBYTESDB\u003C/code\u003E exists, picks it as the deploy target, and writes \u003Ccode\u003E.scai/config/project.local.yml\u003C/code\u003E for the project.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 — Source Connection\u003C/h3\u003E\n","\u003Cp\u003EBecause the kickoff prompt named \u003Ccode\u003Etastybytesdb\u003C/code\u003E, Cortex Code goes straight to the SQL Server connection list, selects \u003Ccode\u003Etastybytesdb\u003C/code\u003E, and runs a connection test that returns \u003Ccode\u003Econnection_test: ok\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 3 — Extract Source Code from SQL Server\u003C/h3\u003E\n","\u003Cp\u003ECortex Code routes into the \u003Ccode\u003Eregister-code-units\u003C/code\u003E sub-skill and runs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Escai code extract -s tastybytesdb --json\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor TastyBytes the extraction reports \u003Cstrong\u003E19 objects in ~4s\u003C/strong\u003E with no failures:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECount\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatabase\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESchema\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E11\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EView\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFunction\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProcedure\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ESQL files land under \u003Ccode\u003Esqlserver-migration/source/tastybytesdb/<schema>/<object_type>/\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 4 — Convert with SnowConvert (SQL + SSIS)\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Econvert\u003C/code\u003E sub-skill runs next. When asked whether you have ETL code to include, point it at the cloned repo's \u003Ccode\u003Eetl/\u003C/code\u003E directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E/Users/<you>/migrations_hol_summit/sfguide-scai-e2e-ssis-migration/etl\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code calls:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Escai code convert --etl-replatform-sources-path <PATH>/etl --json\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe conversion completes in ~12 s and reports:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EFiles processed:\u003C/strong\u003E 19\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECode units converted:\u003C/strong\u003E 270 LOC\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEWIs:\u003C/strong\u003E 2 (1 Critical, 1 Low)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFDMs:\u003C/strong\u003E 54\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPRFs:\u003C/strong\u003E 27\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EETL replatforming:\u003C/strong\u003E 1 SSIS package processed, 3 issues\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EConverted artifacts land in:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Esqlserver-migration/snowflake/tastybytesdb/<schema>/<object_type>/\u003C/code\u003E — converted tables, views, functions, procedures.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esqlserver-migration/snowflake/_etl/daily_sales_agg/\u003C/code\u003E — Snowflake task graph SQL plus a \u003Ccode\u003Edf_load_daily_sales\u003C/code\u003E dbt project (staging views, ephemeral intermediate models, an incremental mart).\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esqlserver-migration/reports/SnowConvert/\u003C/code\u003E — CSV/JSON conversion reports.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 5 — Run the Migration Assessment\u003C/h3\u003E\n","\u003Cp\u003ECortex Code drives \u003Ccode\u003Escai assessment\u003C/code\u003E and asks four short questions to scope the run. For this lab pick the lean defaults:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestion\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAnswer\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDefault wave size is 40-80 objects.\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDefault (40-80)\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAny objects to push into the earliest waves?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENo prioritization\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHow should waves be ordered?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ECategory-based (default)\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAuto-review every Dynamic SQL occurrence?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESkip\u003C/strong\u003E (no dynamic SQL in this workload)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERun the AI-driven per-package SSIS analysis?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGenerate-only\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ECortex Code then dispatches three sub-skills in parallel — \u003Ccode\u003Ewaves-generator\u003C/code\u003E, \u003Ccode\u003Eobject-exclusion-detection\u003C/code\u003E, and \u003Ccode\u003Eetl-assessment\u003C/code\u003E — and finally renders a multi-tab HTML report at \u003Ccode\u003Esqlserver-migration/assessment/multi_report.html\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe result for TastyBytes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EWaves:\u003C/strong\u003E 1 partition, 24 nodes, 8 edges, 0 cycles.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EObject exclusion:\u003C/strong\u003E 0 of 19 objects excluded (no temp/staging/deprecated patterns).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESSIS:\u003C/strong\u003E 1 package classified (Data Transformation, baseline JSON only).\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOpen the report to review:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eopen sqlserver-migration/assessment/multi_report.html\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAt this point Phase 1 (setup + assessment) is complete.\u003C/p\u003E\n","\u003Ch2\u003EPhase 2 — Deploy Objects with the Deploy/Test/Fix Loop\u003C/h2\u003E\n","\u003Cp\u003EPer the kickoff prompt, Cortex Code now goes straight into the deploy/test/fix loop. It asks two questions to configure the testing framework:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestion\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAnswer\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETesting path? Integration uses representative source data; unit synthesizes test data.\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EIntegration (source data)\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOptional: do you have query logs (CSV) capturing real proc invocations?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENo\u003C/strong\u003E (the framework will scaffold from source DB queries)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ECortex Code then auto-deploys the \u003Ccode\u003EVALIDATION\u003C/code\u003E schema, claims \u003Cstrong\u003Eall 16 wave-1 objects\u003C/strong\u003E (3 schemas, 11 tables, 1 ETL, 1 function), and walks them through their tasks.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 — Deploy Schemas\u003C/h3\u003E\n","\u003Cp\u003ECortex Code calls the \u003Ccode\u003Edeploy\u003C/code\u003E MCP tool for the 3 schemas (\u003Ccode\u003ETastyBytes\u003C/code\u003E, \u003Ccode\u003Edbo\u003C/code\u003E, \u003Ccode\u003Eetl_results\u003C/code\u003E). All 3 succeed.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 — Deploy Tables\u003C/h3\u003E\n","\u003Cp\u003ECortex Code deploys all 11 tables (\u003Ccode\u003ECustomer\u003C/code\u003E, \u003Ccode\u003EFoodTruck\u003C/code\u003E, \u003Ccode\u003EOrderDetail\u003C/code\u003E, \u003Ccode\u003ECountry\u003C/code\u003E, \u003Ccode\u003EInventory\u003C/code\u003E, \u003Ccode\u003EOrderHeader\u003C/code\u003E, \u003Ccode\u003ECity\u003C/code\u003E, \u003Ccode\u003EMenu\u003C/code\u003E, \u003Ccode\u003Eetl_logs\u003C/code\u003E, \u003Ccode\u003EEmployeeShift\u003C/code\u003E, \u003Ccode\u003EMenuItem\u003C/code\u003E). All 11 succeed.\u003C/p\u003E\n","\u003Ch3\u003EStep 3 — Migrate Data Locally\u003C/h3\u003E\n","\u003Cp\u003EBecause we asked for \u003Cstrong\u003Elocal\u003C/strong\u003E data-migration infrastructure, Cortex Code:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGenerates \u003Ccode\u003E.scai/settings/DataExchangeWorkerConfig.toml\u003C/code\u003E from the source connection credentials.\u003C/li\u003E\u003Cli\u003EStarts the local Data Exchange Worker:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E scai data worker start --local .scai/settings/DataExchangeWorkerConfig.toml\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EGenerates the migration workflow YAML at \u003Ccode\u003Eartifacts/data_migration/workflows/where-<hash>.yaml\u003C/code\u003E covering all 11 tables (Full / Native, partitioned by primary key).\u003C/li\u003E\u003Cli\u003ERuns the migration with the local orchestrator:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E scai data migrate start \\\n     --config artifacts/data_migration/workflows/where-<hash>.yaml \\\n     -c migrations_sc --json\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe workflow finishes in ~2.3 minutes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EworkflowName:        DATA_MIGRATION_WORKFLOW_2026_05_28_12_51_02\nworkflowStatus:      Finished\ntotalTables:         11\npreprocessedTables:  11\nloadedPartitions:    11/11\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4 — Seed Tests for the Function\u003C/h3\u003E\n","\u003Cp\u003ECortex Code runs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Escai test seed --where "source.canonicalName ILIKE '%fn_FormatCustomerName%'" --append\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis produces \u003Ccode\u003Eartifacts/tastybytesdb/tastybytes/function/fn_formatcustomername/test/fn_FormatCustomerName.yml\u003C/code\u003E with the step-based template. Because this is a simple 1-parameter scalar UDF that looks up a customer row and returns \u003Ccode\u003E"LASTNAME, Firstname"\u003C/code\u003E, Cortex Code fills \u003Ccode\u003Etest_cases:\u003C/code\u003E directly with 13 hand-crafted rows covering valid IDs, the lower boundary, and the not-found path:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Evalidation:\n  steps:\n    - source_query: SELECT TastyBytesDB.TastyBytes.fn_FormatCustomerName({0}) AS "fn_FormatCustomerName"\n      target_query: SELECT TastyBytesDB.TastyBytes.fn_FormatCustomerName({0}) AS "fn_FormatCustomerName"\n  test_cases:\n    # Valid customer IDs\n    - [1]\n    - [2]\n    - [3]\n    # ... 7 more valid IDs\n    # Boundary / not-found cases\n    - [0]\n    - [-1]\n    - [999999]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 5 — Deploy the Function\u003C/h3\u003E\n","\u003Cp\u003ESnowConvert produced a clean Snowflake SQL UDF for this scalar function — no manual rewrites required. The converted file at \u003Ccode\u003Esqlserver-migration/snowflake/tastybytesdb/tastybytes/function/fn_formatcustomername.sql\u003C/code\u003E deploys directly:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION TastyBytes.fn_FormatCustomerName (P_CUSTOMERID INT)\nRETURNS VARCHAR(402)\nLANGUAGE SQL\nAS\n$$\n    SELECT\n        UPPER(RTRIM(LTRIM(NVL(LastName, '')))) ||\n        ', ' || RTRIM(LTRIM(NVL(FirstName, '')))\n    FROM TastyBytes.Customer\n    WHERE CustomerID = P_CUSTOMERID\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA quick smoke test confirms each branch:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(1)      AS valid_customer,\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(2)      AS another_customer,\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(0)      AS not_found_zero,\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(-1)     AS not_found_negative,\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(999999) AS not_found_high;\n-- → JOHNSON, Alice | SMITH, Bob | NULL | NULL | NULL\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 6 — Capture Baselines and Run Functional Tests\u003C/h3\u003E\n","\u003Cp\u003ENow that the function is deployed, Cortex Code captures the source baselines and runs the cross-DB test:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Escai test capture  --where "source.canonicalName ILIKE '%fn_FormatCustomerName%'"\nscai test validate --where "source.canonicalName ILIKE '%fn_FormatCustomerName%'"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EResult:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESUMMARY: 13 test cases\nPassed: 13  Failed: 0  Errors: 0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll 13 cases match SQL Server output exactly on the first run.\u003C/p\u003E\n","\u003Ch3\u003EStep 7 — Claim and Deploy Wave 1 Remainder\u003C/h3\u003E\n","\u003Cp\u003ECortex Code now claims the remaining 3 wave-1 objects (1 procedure, 2 views) and walks them through deploy/test/fix.\u003C/p\u003E\n","\u003Ch3\u003EStep 8 — Deploy Views (Hand-Fix \u003Ccode\u003Evw_TopSellingItems\u003C/code\u003E)\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Evw_CustomerOrderHistory\u003C/code\u003E view deploys cleanly. \u003Ccode\u003Evw_TopSellingItems\u003C/code\u003E fails: SnowConvert flagged the SQL Server \u003Ccode\u003ECROSS APPLY ... TOP 5 ORDER BY\u003C/code\u003E with \u003Ccode\u003ESSC-EWI-TS0082\u003C/code\u003E and emitted an unresolved \u003Ccode\u003E!!!RESOLVE EWI!!!\u003C/code\u003E marker plus a non-lateral \u003Ccode\u003ELEFT OUTER JOIN\u003C/code\u003E, which won't run in Snowflake.\u003C/p\u003E\n","\u003Cp\u003EReplace the file at \u003Ccode\u003Esqlserver-migration/snowflake/tastybytesdb/tastybytes/view/vw_topsellingitems.sql\u003C/code\u003E with a window-function rewrite:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE VIEW TastyBytes.vw_TopSellingItems AS\nWITH agg AS (\n    SELECT\n        oh.TruckID, mi.MenuItemID, mi.ItemName,\n        SUM(od.Quantity)                AS TotalQuantitySold,\n        SUM(od.Quantity * od.UnitPrice) AS TotalRevenue\n    FROM TastyBytes.OrderDetail od\n    INNER JOIN TastyBytes.OrderHeader oh ON od.OrderID = oh.OrderID\n    INNER JOIN TastyBytes.MenuItem   mi ON od.MenuItemID = mi.MenuItemID\n    WHERE oh.OrderStatus = 'Completed'\n    GROUP BY oh.TruckID, mi.MenuItemID, mi.ItemName\n),\nranked AS (\n    SELECT a.*,\n           ROW_NUMBER() OVER (PARTITION BY a.TruckID\n                              ORDER BY a.TotalQuantitySold DESC) AS rn\n    FROM agg a\n)\nSELECT ft.TruckID, ft.TruckName,\n       r.MenuItemID, r.ItemName,\n       r.TotalQuantitySold, r.TotalRevenue\nFROM TastyBytes.FoodTruck ft\nINNER JOIN ranked r ON r.TruckID = ft.TruckID\nWHERE r.rn <= 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERedeploy. Both views return 25 rows, matching the source.\u003C/p\u003E\n","\u003Ch3\u003EStep 9 — Deploy and Test the Procedure\u003C/h3\u003E\n","\u003Cp\u003ECortex Code seeds tests for \u003Ccode\u003Esp_UpdateInventory\u003C/code\u003E (a procedure that updates \u003Ccode\u003EInventory\u003C/code\u003E rows for a given truck, with an \u003Ccode\u003EOverride\u003C/code\u003E flag controlling absolute-set vs. increment behavior, and an early return when \u003Ccode\u003ETruckID IS NULL\u003C/code\u003E):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Escai test seed --where "source.canonicalName ILIKE '%sp_UpdateInventory%'" --append\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code fills \u003Ccode\u003Etest_cases:\u003C/code\u003E with 6 rows covering:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOverride = 1 (absolute set) for valid truck IDs (1, 3, 5).\u003C/li\u003E\u003Cli\u003EOverride = 0 (increment) for valid truck IDs (1, 2, 4).\u003C/li\u003E\u003Cli\u003EA zero-stock-count case (\u003Ccode\u003EStockCount = 0.00, Override = 1\u003C/code\u003E).\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDeploy the procedure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- (Cortex Code runs CREATE OR REPLACE PROCEDURE … from snowflake/…/sp_updateinventory.sql)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECapture baselines and run validation:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Escai test capture  --where "source.canonicalName ILIKE '%sp_UpdateInventory%'"\nscai test validate --where "source.canonicalName ILIKE '%sp_UpdateInventory%'"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EInspect the validation results from the framework's stored output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT TEST_NAME, PARAMETERS, STATUS, ERROR_MESSAGE\nFROM TASTYBYTESDB.VALIDATION.LATEST\nWHERE PROCEDURE_NAME ILIKE '%UpdateInventory%'\nORDER BY ID;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll 6 cases come back as \u003Cstrong\u003EPASS\u003C/strong\u003E:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStatus\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENotes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETruckID = 1, StockCount = 100.00, Override = 1\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPASS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAbsolute-set on truck 1.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETruckID = 2, StockCount = 50.50, Override = 0\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPASS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIncrement on truck 2.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETruckID = 3, StockCount = 200.00, Override = 1\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPASS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAbsolute-set on truck 3.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETruckID = 4, StockCount = 25.00, Override = 0\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPASS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIncrement on truck 4.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETruckID = 5, StockCount = 0.00, Override = 1\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPASS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAbsolute-set with zero stock count.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETruckID = 1, StockCount = 999.99, Override = 0\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPASS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELarge increment on truck 1.\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe procedure produces identical post-run state on SQL Server and Snowflake across every test case, confirming the converted Snowflake Scripting body matches the original T-SQL semantics exactly.\u003C/p\u003E\n","\u003Ch3\u003EVerify the Code Deployment\u003C/h3\u003E\n","\u003Cp\u003EA quick sanity check against \u003Ccode\u003EINFORMATION_SCHEMA\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT 'BASE TABLE' AS kind, COUNT(*) AS cnt\nFROM TASTYBYTESDB.INFORMATION_SCHEMA.TABLES\nWHERE table_schema = 'TASTYBYTES' AND table_type='BASE TABLE'\nUNION ALL SELECT 'VIEW',     COUNT(*) FROM TASTYBYTESDB.INFORMATION_SCHEMA.VIEWS      WHERE table_schema='TASTYBYTES'\nUNION ALL SELECT 'FUNCTION', COUNT(*) FROM TASTYBYTESDB.INFORMATION_SCHEMA.FUNCTIONS  WHERE function_schema='TASTYBYTES'\nUNION ALL SELECT 'PROCEDURE',COUNT(*) FROM TASTYBYTESDB.INFORMATION_SCHEMA.PROCEDURES WHERE procedure_schema='TASTYBYTES';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EKIND\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECNT\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBASE TABLE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E11\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVIEW\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFUNCTION\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPROCEDURE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EPlus the \u003Ccode\u003ETASTYBYTESDB.ETL_RESULTS.ETL_LOGS\u003C/code\u003E helper table in \u003Ccode\u003Eetl_results\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch2\u003EPhase 3 — Verify the Migrated Data\u003C/h2\u003E\n","\u003Cp\u003EThe data was already migrated in Phase 2 / Step 3. Confirm row counts on Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT 'Country'       AS tbl, COUNT(*) AS row_cnt FROM TASTYBYTESDB.TASTYBYTES.Country\nUNION ALL SELECT 'City',          COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.City\nUNION ALL SELECT 'Customer',      COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.Customer\nUNION ALL SELECT 'FoodTruck',     COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.FoodTruck\nUNION ALL SELECT 'Menu',          COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.Menu\nUNION ALL SELECT 'MenuItem',      COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.MenuItem\nUNION ALL SELECT 'Inventory',     COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.Inventory\nUNION ALL SELECT 'OrderHeader',   COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.OrderHeader\nUNION ALL SELECT 'OrderDetail',   COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.OrderDetail\nUNION ALL SELECT 'EmployeeShift', COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.EmployeeShift\nORDER BY tbl;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EPhase 4 — Deploy the dbt Project and the SSIS Task Graph\u003C/h2\u003E\n","\u003Cp\u003EWith the database workload migrated, the last phase is to deploy the SSIS-derived artifacts. SnowConvert produced two things under \u003Ccode\u003Esqlserver-migration/snowflake/_etl/daily_sales_agg/\u003C/code\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003Etask-graph SQL file\u003C/strong\u003E (\u003Ccode\u003Edaily_sales_agg.sql\u003C/code\u003E) defining a 4-task chain: a root task → an \u003Ccode\u003E_insert_start_log\u003C/code\u003E task → a \u003Ccode\u003E_df_load_daily_sales\u003C/code\u003E task that calls \u003Ccode\u003EEXECUTE DBT PROJECT …\u003C/code\u003E → an \u003Ccode\u003E_insert_end_log\u003C/code\u003E task. The root task has \u003Cstrong\u003Eno warehouse\u003C/strong\u003E and the children all use \u003Ccode\u003EWAREHOUSE=DUMMY_WAREHOUSE\u003C/code\u003E placeholders.\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003Edbt project\u003C/strong\u003E (\u003Ccode\u003Edf_load_daily_sales/\u003C/code\u003E) with two staging views, four ephemeral intermediate models, an incremental mart (\u003Ccode\u003Eole_db_destination\u003C/code\u003E → materializes as \u003Ccode\u003EDAILYSALESAGG\u003C/code\u003E), \u003Ccode\u003Edbt_project.yml\u003C/code\u003E, and \u003Ccode\u003Eprofiles.yml\u003C/code\u003E — but with placeholder values like \u003Ccode\u003EYOUR_PROJECT_NAME\u003C/code\u003E and \u003Ccode\u003EYOUR_PROFILE_NAME\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrompt 2 — Deploy and Run Everything\u003C/h3\u003E\n","\u003Cp\u003ETell Cortex Code:\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ENow I want to deploy my generated dbt project and my Snowflake task graph\nto Snowflake in the `snowflake` directory and using the `snow dbt deploy`\ncommand, the dbt project object in Snowflake should be deployed in\nthe TastyBytes schema.\nMake sure to deploy my task graph using the `XSMALL_WH` warehouse\nfor each task and that the dbt project is referenced through the \nTastyBytes schema. Once the dbt project and the task graph are both deployed\nexecute the task graph and show me the rows of the materialized mart model\nin the dbt project.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECortex Code loads the \u003Ccode\u003Edbt-projects-on-snowflake\u003C/code\u003E skill and works through three high-level steps before triggering the run:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EPatch the dbt project files.\u003C/strong\u003E SnowConvert wrote \u003Ccode\u003Edbt_project.yml\u003C/code\u003E, \u003Ccode\u003Eprofiles.yml\u003C/code\u003E, and \u003Ccode\u003Emodels/sources.yml\u003C/code\u003E with placeholders (\u003Ccode\u003EYOUR_PROJECT_NAME\u003C/code\u003E, \u003Ccode\u003EYOUR_PROFILE_NAME\u003C/code\u003E, \u003Ccode\u003EYOUR_DB\u003C/code\u003E, \u003Ccode\u003EYOUR_SCHEMA\u003C/code\u003E). Cortex Code rewrites them with the concrete project name \u003Ccode\u003Edf_load_daily_sales\u003C/code\u003E, target \u003Ccode\u003Edev\u003C/code\u003E, role \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E, warehouse \u003Ccode\u003EXSMALL_WH\u003C/code\u003E, database \u003Ccode\u003ETASTYBYTESDB\u003C/code\u003E, sources at \u003Ccode\u003ETASTYBYTESDB.TASTYBYTES\u003C/code\u003E, and drops the \u003Ccode\u003Eaccount\u003C/code\u003E / \u003Ccode\u003Euser\u003C/code\u003E / \u003Ccode\u003Epassword\u003C/code\u003E fields (dbt runs \u003Cem\u003Einside\u003C/em\u003E Snowflake, so those aren't needed).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDeploy the dbt project with \u003Ccode\u003Esnow dbt deploy\u003C/code\u003E:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E snow dbt deploy df_load_daily_sales \\\n     --source <PATH>/snowflake/_etl/daily_sales_agg/df_load_daily_sales \\\n     --database TASTYBYTESDB --schema PUBLIC\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Esnow dbt deploy\u003C/code\u003E honors the connection's default schema over \u003Ccode\u003E--schema\u003C/code\u003E, so the project actually lands at \u003Ccode\u003E**TASTYBYTESDB.TASTYBYTES.DF_LOAD_DAILY_SALES**\u003C/code\u003E (matching the connection's \u003Ccode\u003Eschema = "TASTYBYTES"\u003C/code\u003E). Cortex Code re-points the task graph at the actual location.\n3. \u003Cstrong\u003EDeploy the 4-task graph and run it.\u003C/strong\u003E Cortex Code rewrites \u003Ccode\u003Edaily_sales_agg.sql\u003C/code\u003E: every \u003Ccode\u003EWAREHOUSE=DUMMY_WAREHOUSE\u003C/code\u003E becomes \u003Ccode\u003EWAREHOUSE = XSMALL_WH\u003C/code\u003E, the root task gets \u003Ccode\u003EWAREHOUSE = XSMALL_WH\u003C/code\u003E added, the \u003Ccode\u003EINSERT INTO etl_results.etl_logs\u003C/code\u003E calls get fully qualified to \u003Ccode\u003ETASTYBYTESDB.ETL_RESULTS.ETL_LOGS\u003C/code\u003E, and the \u003Ccode\u003EEXECUTE DBT PROJECT\u003C/code\u003E call points at \u003Ccode\u003ETASTYBYTESDB.TASTYBYTES.DF_LOAD_DAILY_SALES\u003C/code\u003E. Cortex Code applies the four \u003Ccode\u003ECREATE OR REPLACE TASK\u003C/code\u003E statements, resumes the three child tasks, and triggers the root with \u003Ccode\u003EEXECUTE TASK TASTYBYTESDB.PUBLIC.daily_sales_agg\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EOnce \u003Ccode\u003EEXECUTE TASK\u003C/code\u003E is dispatched, Cortex Code polls \u003Ccode\u003ETASK_HISTORY\u003C/code\u003E until all four tasks land at \u003Ccode\u003ESUCCEEDED\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EDAILY_SALES_AGG                       SUCCEEDED  ~1s\n  └─→ DAILY_SALES_AGG_INSERT_START_LOG  SUCCEEDED  ~1s\n        └─→ DAILY_SALES_AGG_DF_LOAD_DAILY_SALES SUCCEEDED ~14s  (EXECUTE DBT PROJECT)\n              └─→ DAILY_SALES_AGG_INSERT_END_LOG SUCCEEDED  ~1s\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EEXECUTE DBT PROJECT\u003C/code\u003E call ran \u003Ccode\u003Edbt build --target dev\u003C/code\u003E inside Snowflake: 2 staging views, 4 ephemeral intermediate models (inlined as CTEs and not visible in \u003Ccode\u003EINFORMATION_SCHEMA.TABLES\u003C/code\u003E), and 1 incremental mart materialized as \u003Ccode\u003ETASTYBYTESDB.PUBLIC.DAILYSALESAGG\u003C/code\u003E. The two \u003Ccode\u003E_insert_*_log\u003C/code\u003E tasks wrote start/end markers to \u003Ccode\u003ETASTYBYTESDB.ETL_RESULTS.ETL_LOGS\u003C/code\u003E, exactly the side-effect SnowConvert lifted from the original SSIS package.\u003C/p\u003E\n","\u003Cp\u003EFinally, Cortex Code shows the materialized mart:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM TASTYBYTESDB.PUBLIC.DAILYSALESAGG ORDER BY TRUCKID, SALEDATE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E22 rows, one per \u003Ccode\u003E(TruckID, SaleDate)\u003C/code\u003E pair from completed orders, aggregating \u003Ccode\u003EOrderCount\u003C/code\u003E, \u003Ccode\u003EGrossRevenue\u003C/code\u003E, \u003Ccode\u003ETotalTips\u003C/code\u003E, and \u003Ccode\u003EItemsSold\u003C/code\u003E per truck-day. A sample of the output:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETRUCKID\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESALEDATE\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EORDERCOUNT\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EGROSSREVENUE\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETOTALTIPS\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EITEMSSOLD\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2024-07-10\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E59.96\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10.00\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2024-08-15\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E16.99\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2.50\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2024-09-01\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10.99\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2.00\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2024-09-12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E55.96\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8.00\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2024-07-15\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E25.98\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4.00\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2024-09-15\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4.50\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.00\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2024-07-23\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E128.91\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E18.00\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E…\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E…\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E…\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E…\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E…\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E…\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E11\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2024-10-05\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12.99\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2.00\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ETotals across the mart: 22 truck-day rows, 22 orders, $1,015.31 gross revenue, $139.50 tips, 39 items sold. Cross-checking against \u003Ccode\u003ETASTYBYTESDB.ETL_RESULTS.ETL_LOGS\u003C/code\u003E shows the two markers \u003Ccode\u003Epkg_daily_sales_aggregate start\u003C/code\u003E and \u003Ccode\u003Epkg_daily_sales_aggregate end\u003C/code\u003E written one task apart, confirming the full task graph executed end-to-end.\u003C/p\u003E\n","\u003Cp\u003EAt this point all four phases are complete:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ Setup       — project initialized, connections registered, local infra ready\n✅ Phase 1     — 19 objects extracted, 19 files converted, assessment generated\n✅ Phase 2     — 16 wave-1 objects deployed (deploy/test/fix loop)\n                 + 3 additional objects (1 procedure, 2 views) hand-fixed and deployed\n                 + 11 tables, 185 rows migrated locally\n                 + data validation skipped per kickoff prompt\n                 + ETL stabilization skipped per kickoff prompt\n✅ Phase 3     — row counts verified on Snowflake\n✅ Phase 4     — dbt project deployed via `snow dbt deploy`,\n                 4-task graph deployed with XSMALL_WH per task, executed end-to-end\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've taken a Microsoft SQL Server database and an SSIS pipeline all the way from source extraction to a fully deployed, populated, and \u003Cem\u003Erunning\u003C/em\u003E workload in Snowflake — driven end-to-end by the Cortex Code CLI and its \u003Ccode\u003Esnowflake-migration:migration\u003C/code\u003E skill, plus the \u003Ccode\u003Esnow dbt deploy\u003C/code\u003E workflow for the SnowConvert-generated dbt project.\u003C/p\u003E\n","\u003Cp\u003EYou started from a blank project, set up \u003Cstrong\u003Elocal\u003C/strong\u003E data-migration infrastructure (no SPCS / compute pool needed), registered 19 SQL Server objects and 1 SSIS package, converted them with SnowConvert AI, generated a deployment plan with a classified SSIS assessment, deployed every in-scope database object using a deploy/test/fix loop (hand-fixing 1 conversion artifact — a \u003Ccode\u003ECROSS APPLY\u003C/code\u003E view rewritten with window functions), migrated 185 rows of data via the locally-run orchestrator + worker, and finally hand-patched and deployed both the SnowConvert-generated dbt project (\u003Ccode\u003Esnow dbt deploy\u003C/code\u003E) and the SnowConvert-generated Snowflake task graph (with \u003Ccode\u003EXSMALL_WH\u003C/code\u003E per task), executing the full graph end-to-end on demand.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to drive an end-to-end SQL Server + SSIS migration with \u003Cstrong\u003ECortex Code\u003C/strong\u003E and the bundled \u003Ccode\u003Esnowflake-migration:migration\u003C/code\u003E skill, using just five chat prompts.\u003C/li\u003E\u003Cli\u003EHow \u003Cstrong\u003ESnowConvert AI\u003C/strong\u003E extracts, converts, and deploys a SQL Server workload to Snowflake, and how to read its EWIs, FDMs, and PRFs.\u003C/li\u003E\u003Cli\u003EHow to triage and hand-fix common SnowConvert outputs — rewriting a SQL Server \u003Ccode\u003ECROSS APPLY ... TOP\u003C/code\u003E with \u003Ccode\u003EROW_NUMBER() OVER (PARTITION BY ...)\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EHow to run the data-migration orchestrator and Data Exchange Worker \u003Cstrong\u003Elocally\u003C/strong\u003E for SQL Server → Snowflake table loads, without provisioning SPCS.\u003C/li\u003E\u003Cli\u003EHow to use the seed → capture → validate testing framework against your source database to verify the migrated function produces identical output to the SQL Server original.\u003C/li\u003E\u003Cli\u003EHow to deploy a SnowConvert-generated dbt project to Snowflake via \u003Ccode\u003Esnow dbt deploy\u003C/code\u003E, then point a Snowflake task graph at it with \u003Ccode\u003EEXECUTE DBT PROJECT\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EQuickstart source code\u003C/strong\u003E: \u003Ccode\u003E[sfguide-scai-e2e-ssis-migration](https://github.com/Snowflake-Labs/sfguide-scai-e2e-ssis-migration)\u003C/code\u003E — DDLs, sample data, the SSIS package, and the initial Snowflake setup used throughout this guide.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/general/user-guide/snowconvert/command-line-interface/README\"\u003ESnowConvert AI CLI documentation\u003C/a\u003E — reference for \u003Ccode\u003Escai code extract\u003C/code\u003E, \u003Ccode\u003Escai code convert\u003C/code\u003E, \u003Ccode\u003Escai code deploy\u003C/code\u003E, \u003Ccode\u003Escai data migrate\u003C/code\u003E, \u003Ccode\u003Escai test seed/capture/validate\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECortex Code CLI\u003C/a\u003E — install and usage guide for the CLI that hosts the migration skills.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/introduction/introduction\"\u003ESnowflake CLI\u003C/a\u003E — used here for the connection config that both \u003Ccode\u003Escai\u003C/code\u003E and \u003Ccode\u003Esnow\u003C/code\u003E share, and for \u003Ccode\u003Esnow dbt deploy\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-engineering/dbt-projects/about-dbt-projects\"\u003Edbt Projects on Snowflake\u003C/a\u003E — target pattern for the replatformed SSIS data flows produced by \u003Ccode\u003Emigrate-etl-package\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003ESnowflake tasks\u003C/a\u003E — background for the four-task graph with \u003Ccode\u003EEXECUTE DBT PROJECT\u003C/code\u003E deployed in Phase 4.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-scripting/index\"\u003ESnowflake Scripting reference\u003C/a\u003E — background for the procedure rewrites.\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Snowflake AIM End-to-End SQL Server and SSIS Migration","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nSnowflake AIM unifies the proven capabilities of SnowConvert AI, Snowpark Migration Accelerator, and Datometry into a single AI-powered platform for assessing, modernizing, and migrating enterprise data and code workloads to Snowflake. Designed to reduce the complexity, risk, and operational overhead traditionally associated with large-scale migrations, Snowflake AIM enables organizations to define their target state while the platform orchestrates the migration process end to end. From analyzing Spark code and converting APIs to Snowpark, to modernizing warehouses, tables, views, ETL pipelines, reporting assets, and stored procedures across major platforms, Snowflake AIM combines intelligent assessment, automated code conversion, dependency mapping, orchestration, and virtualization into a unified migration experience. By eliminating the need to rebuild workloads from scratch or maintain parallel legacy environments, Snowflake AIM dramatically accelerates modernization timelines while minimizing disruption to ongoing business operations.\n\nThrough this guide you will learn how to do an end-to-end migration of a Microsoft SQL Server environment with SSIS to Snowflake using Snowflake AIM, the Cortex Code CLI, and the Snowflake CLI. This guide walks you through project setup, extracting your source DDLs, converting them, leveraging AI to fix remaining issues in the converted code, deploying objects, migrating historical data with a **locally-run orchestrator + worker**, and finally replatforming your SSIS ETL pipelines into a Snowflake task graph plus a Snowflake-native dbt project.\n\nThe lab is driven entirely from a single Cortex Code session — at each step you tell the assistant what you want and the bundled `snowflake-migration:migration` skill orchestrates the underlying `scai` / `snow` / SQL calls.\n\n### Prerequisites\n\n- Familiarity with Snowflake SQL.\n- Familiarity with Microsoft SQL Server and SSIS.\n- Familiarity with dbt Projects on Snowflake and Snowflake tasks.\n\n### What You'll Learn\n\nBy the end of this guide, you will learn to work with:\n\n- The SnowConvert AI CLI to perform end-to-end migrations of SQL Server (code conversion, data migration, ETL replatform).\n- The Cortex Code CLI and its bundled `snowflake-migration:migration` skill for orchestrating every phase of the migration journey.\n- The Snowflake CLI's `snow dbt deploy` command to publish a Snowflake-native dbt project produced by SnowConvert.\n\n### What You'll Need\n\n- A SQL Server database where you have full read and write permissions.\n- A Snowflake account with the `ACCOUNTADMIN` role.\n- [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/introduction/introduction) installed.\n- [SnowConvert AI CLI](https://docs.snowflake.com/en/migrations/snowconvert-docs/general/user-guide/snowconvert/command-line-interface/README) installed.\n- [Cortex Code CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli) installed.\n- A text editor (we recommend [Visual Studio Code](https://code.visualstudio.com/)).\n\n### What You'll Build\n\nAn end-to-end migration of a SQL Server database and an SSIS package to Snowflake, including: code conversion, hand-fixed deploy/test/fix loop, data migration using a **locally-run** Data Migration & Validation orchestrator + worker, and deployment of an SSIS-derived Snowflake task graph that calls a Snowflake-native dbt project.\n\n## Project Setup\n\n### Clone the Quickstart Repository\n\nAll of the source scripts, the SSIS package, and the SQL used in this quickstart live in the `[sfguide-scai-e2e-ssis-migration](https://github.com/Snowflake-Labs/sfguide-scai-e2e-ssis-migration)` repository. Clone it to your local machine before continuing:\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sfguide-scai-e2e-ssis-migration.git\ncd sfguide-scai-e2e-ssis-migration\n```\n\nThe repository contains:\n\n- `sourcedb/00_ddl.sql` and `sourcedb/01_data.sql` — SQL Server DDL and sample data for the TastyBytes database.\n- `snowflake/init.sql` — initial Snowflake database and schemas.\n- `etl/daily_sales_agg.dtsx` — the SSIS package you will migrate to Snowflake.\n\nLater steps reference these paths (for example, when Cortex Code asks for the filesystem path to your SSIS folder, you will point it at this repository's `etl/` directory).\n\n### SQL Server DDL and Data\n\n1. In the SQL Server instance you will be using for this quickstart, run the `sourcedb/00_ddl.sql` script to create the database, schema, and DDLs that we will be converting.\n2. Once the DDLs have been deployed successfully, run the `sourcedb/01_data.sql` script to insert sample data into the tables of the TastyBytes database.\n\n### Snowflake Account\n\nIn your Snowflake account, run the `snowflake/init.sql` script to create the initial database (`TASTYBYTESDB`) and warehouse (`XSMALL_WH`) we will deploy into. Because this lab runs the data-migration orchestrator and worker **locally**, you do **not** need to create an SPCS compute pool.\n\n### Snowflake Connection\n\nTo enable the Snowflake CLI and SnowConvert AI to run against our Snowflake account, we need to add the connection details to the `config.toml` file used by both CLIs. Follow the steps in this [guide](https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-cli) to set up your `config.toml` with your connection details.\n\nTest the connection:\n\n```bash\nsnow connection test --connection \u003CYOUR_SNOWFLAKE_CONNECTION_NAME\u003E\n```\n\nIn this quickstart we use a Snowflake connection named `**migrations_sc**` configured with warehouse `XSMALL_WH`, database `TASTYBYTESDB`, schema `TASTYBYTES`, and role `ACCOUNTADMIN`.\n\n### SnowConvert AI Source Connection\n\nRun the following command to register your SQL Server connection with SnowConvert AI so it can later extract your code and migrate your data:\n\n```bash\nscai connection add-sql-server\n```\n\nTest it:\n\n```bash\nscai connection test -l sqlserver -s \u003CYOUR_SQL_SERVER_CONNECTION_NAME\u003E\n```\n\nYou should see a `Status: Success` row in the output. We use a SQL Server connection named `**tastybytesdb**` throughout this quickstart.\n\n## Drive the Migration with Cortex Code\n\nRather than running each `scai` command manually, we drive the entire migration from a single Cortex Code session. Cortex Code ships with a `snowflake-migration:migration` skill that orchestrates project initialization, source extraction, conversion, assessment, deployment, data migration, and ETL replatforming through a guided conversation.\n\nThe sections below show the **exact prompts you'll send during the lab** and the answers we selected for this quickstart, in order.\n\n### Start Cortex Code\n\nFrom the directory where you want to host the migration project (for example, an existing `migrations_hol_summit/` folder), launch Cortex Code:\n\n```bash\ncortex\n```\n\n### Prompt 1 — Kick Off the End-to-End Migration\n\nTell the assistant exactly what you want, including how the data infrastructure should be deployed and what to skip:\n\n\u003E ```\n\u003E Migrate my SQL Server workload and SSIS packages to Snowflake by doing\n\u003E a full end-to-end migration from scratch. I want you to connect to my\n\u003E SQL Server database using the `tastybytesdb` connection registered\n\u003E already in `scai` to extract my SQL code and migrate the data from my\n\u003E tables. Make sure to setup data migration infrastructure locally and\n\u003E also do not do ETL stabilization. When the tables are deployed and\n\u003E their data is migrated, do not validate their data, \n\u003E skip to deployment and validation of the views, functions, and procedures.\n\u003E ```\n\nThis prompt does four important things in one shot:\n\n1. Pins the SQL Server source to the existing `tastybytesdb` connection.\n2. Asks for **local** data-migration infrastructure (orchestrator + worker on the host machine — no SPCS / compute pool needed).\n3. Tells Cortex Code to **skip ETL stabilization**.\n4. Tells Cortex Code to **skip data validation** after the data lands and jump straight to the deploy/test/fix loop.\n\nThe skill prints:\n\n\u003E **Welcome to the Snowflake Migrations plugin.** Let me get started by configuring your session.\n\nIt then walks through a series of one-line prompts. Answer them as below.\n\n### Step 1 — Initial Configuration Prompts\n\nCortex Code asks four short questions before it touches any code:\n\n\n| Question                                                                              | Answer             | Why                                                 |\n| ------------------------------------------------------------------------------------- | ------------------ | --------------------------------------------------- |\n| Enable the local migration dashboard at `http://127.0.0.1:7878`?                      | **Yes**            | Lets you watch the migration progress in a browser. |\n| Reduce permission prompts? Allowlist 12 read-only MCP tools in `settings.json`.       | **Allow**          | Avoids being prompted for every read-only MCP call. |\n| Which source database are you migrating from?                                         | **SQL Server**     |                                                     |\n| Are you starting a new migration, or do you already have pre-converted Snowflake SQL? | **Starting fresh** |                                                     |\n\n\nIt then confirms the active Snowflake connection and asks for the target database:\n\n\n| Question                                                                       | Answer                       |\n| ------------------------------------------------------------------------------ | ---------------------------- |\n| The active Snowflake connection is `migrations_sc`. Use it for this migration? | **Yes, use `migrations_sc`** |\n| Which Snowflake database should migrated objects be deployed to?               | `**TASTYBYTESDB**`           |\n\n\nCortex Code verifies that `TASTYBYTESDB` exists, picks it as the deploy target, and writes `.scai/config/project.local.yml` for the project.\n\n### Step 2 — Source Connection\n\nBecause the kickoff prompt named `tastybytesdb`, Cortex Code goes straight to the SQL Server connection list, selects `tastybytesdb`, and runs a connection test that returns `connection_test: ok`.\n\n### Step 3 — Extract Source Code from SQL Server\n\nCortex Code routes into the `register-code-units` sub-skill and runs:\n\n```bash\nscai code extract -s tastybytesdb --json\n```\n\nFor TastyBytes the extraction reports **19 objects in ~4s** with no failures:\n\n\n| Type      | Count |\n| --------- | ----- |\n| Database  | 1     |\n| Schema    | 3     |\n| Table     | 11    |\n| View      | 2     |\n| Function  | 1     |\n| Procedure | 1     |\n\n\nSQL files land under `sqlserver-migration/source/tastybytesdb/\u003Cschema\u003E/\u003Cobject_type\u003E/`.\n\n### Step 4 — Convert with SnowConvert (SQL + SSIS)\n\nThe `convert` sub-skill runs next. When asked whether you have ETL code to include, point it at the cloned repo's `etl/` directory:\n\n```\n/Users/\u003Cyou\u003E/migrations_hol_summit/sfguide-scai-e2e-ssis-migration/etl\n```\n\nCortex Code calls:\n\n```bash\nscai code convert --etl-replatform-sources-path \u003CPATH\u003E/etl --json\n```\n\nThe conversion completes in ~12 s and reports:\n\n- **Files processed:** 19\n- **Code units converted:** 270 LOC\n- **EWIs:** 2 (1 Critical, 1 Low)\n- **FDMs:** 54\n- **PRFs:** 27\n- **ETL replatforming:** 1 SSIS package processed, 3 issues\n\nConverted artifacts land in:\n\n- `sqlserver-migration/snowflake/tastybytesdb/\u003Cschema\u003E/\u003Cobject_type\u003E/` — converted tables, views, functions, procedures.\n- `sqlserver-migration/snowflake/_etl/daily_sales_agg/` — Snowflake task graph SQL plus a `df_load_daily_sales` dbt project (staging views, ephemeral intermediate models, an incremental mart).\n- `sqlserver-migration/reports/SnowConvert/` — CSV/JSON conversion reports.\n\n### Step 5 — Run the Migration Assessment\n\nCortex Code drives `scai assessment` and asks four short questions to scope the run. For this lab pick the lean defaults:\n\n\n| Question                                     | Answer                                     |\n| -------------------------------------------- | ------------------------------------------ |\n| Default wave size is 40-80 objects.          | **Default (40-80)**                        |\n| Any objects to push into the earliest waves? | **No prioritization**                      |\n| How should waves be ordered?                 | **Category-based (default)**               |\n| Auto-review every Dynamic SQL occurrence?    | **Skip** (no dynamic SQL in this workload) |\n| Run the AI-driven per-package SSIS analysis? | **Generate-only**                          |\n\n\nCortex Code then dispatches three sub-skills in parallel — `waves-generator`, `object-exclusion-detection`, and `etl-assessment` — and finally renders a multi-tab HTML report at `sqlserver-migration/assessment/multi_report.html`.\n\nThe result for TastyBytes:\n\n- **Waves:** 1 partition, 24 nodes, 8 edges, 0 cycles.\n- **Object exclusion:** 0 of 19 objects excluded (no temp/staging/deprecated patterns).\n- **SSIS:** 1 package classified (Data Transformation, baseline JSON only).\n\nOpen the report to review:\n\n```bash\nopen sqlserver-migration/assessment/multi_report.html\n```\n\nAt this point Phase 1 (setup + assessment) is complete.\n\n## Phase 2 — Deploy Objects with the Deploy/Test/Fix Loop\n\nPer the kickoff prompt, Cortex Code now goes straight into the deploy/test/fix loop. It asks two questions to configure the testing framework:\n\n\n| Question                                                                               | Answer                                                      |\n| -------------------------------------------------------------------------------------- | ----------------------------------------------------------- |\n| Testing path? Integration uses representative source data; unit synthesizes test data. | **Integration (source data)**                               |\n| Optional: do you have query logs (CSV) capturing real proc invocations?                | **No** (the framework will scaffold from source DB queries) |\n\n\nCortex Code then auto-deploys the `VALIDATION` schema, claims **all 16 wave-1 objects** (3 schemas, 11 tables, 1 ETL, 1 function), and walks them through their tasks.\n\n### Step 1 — Deploy Schemas\n\nCortex Code calls the `deploy` MCP tool for the 3 schemas (`TastyBytes`, `dbo`, `etl_results`). All 3 succeed.\n\n### Step 2 — Deploy Tables\n\nCortex Code deploys all 11 tables (`Customer`, `FoodTruck`, `OrderDetail`, `Country`, `Inventory`, `OrderHeader`, `City`, `Menu`, `etl_logs`, `EmployeeShift`, `MenuItem`). All 11 succeed.\n\n### Step 3 — Migrate Data Locally\n\nBecause we asked for **local** data-migration infrastructure, Cortex Code:\n\n1. Generates `.scai/settings/DataExchangeWorkerConfig.toml` from the source connection credentials.\n2. Starts the local Data Exchange Worker:\n  ```bash\n   scai data worker start --local .scai/settings/DataExchangeWorkerConfig.toml\n  ```\n3. Generates the migration workflow YAML at `artifacts/data_migration/workflows/where-\u003Chash\u003E.yaml` covering all 11 tables (Full / Native, partitioned by primary key).\n4. Runs the migration with the local orchestrator:\n  ```bash\n   scai data migrate start \\\n       --config artifacts/data_migration/workflows/where-\u003Chash\u003E.yaml \\\n       -c migrations_sc --json\n  ```\n\nThe workflow finishes in ~2.3 minutes:\n\n```\nworkflowName:        DATA_MIGRATION_WORKFLOW_2026_05_28_12_51_02\nworkflowStatus:      Finished\ntotalTables:         11\npreprocessedTables:  11\nloadedPartitions:    11/11\n```\n\n### Step 4 — Seed Tests for the Function\n\nCortex Code runs:\n\n```bash\nscai test seed --where \"source.canonicalName ILIKE '%fn_FormatCustomerName%'\" --append\n```\n\nThis produces `artifacts/tastybytesdb/tastybytes/function/fn_formatcustomername/test/fn_FormatCustomerName.yml` with the step-based template. Because this is a simple 1-parameter scalar UDF that looks up a customer row and returns `\"LASTNAME, Firstname\"`, Cortex Code fills `test_cases:` directly with 13 hand-crafted rows covering valid IDs, the lower boundary, and the not-found path:\n\n```yaml\nvalidation:\n  steps:\n    - source_query: SELECT TastyBytesDB.TastyBytes.fn_FormatCustomerName({0}) AS \"fn_FormatCustomerName\"\n      target_query: SELECT TastyBytesDB.TastyBytes.fn_FormatCustomerName({0}) AS \"fn_FormatCustomerName\"\n  test_cases:\n    # Valid customer IDs\n    - [1]\n    - [2]\n    - [3]\n    # ... 7 more valid IDs\n    # Boundary / not-found cases\n    - [0]\n    - [-1]\n    - [999999]\n```\n\n### Step 5 — Deploy the Function\n\nSnowConvert produced a clean Snowflake SQL UDF for this scalar function — no manual rewrites required. The converted file at `sqlserver-migration/snowflake/tastybytesdb/tastybytes/function/fn_formatcustomername.sql` deploys directly:\n\n```sql\nCREATE OR REPLACE FUNCTION TastyBytes.fn_FormatCustomerName (P_CUSTOMERID INT)\nRETURNS VARCHAR(402)\nLANGUAGE SQL\nAS\n$$\n    SELECT\n        UPPER(RTRIM(LTRIM(NVL(LastName, '')))) ||\n        ', ' || RTRIM(LTRIM(NVL(FirstName, '')))\n    FROM TastyBytes.Customer\n    WHERE CustomerID = P_CUSTOMERID\n$$;\n```\n\nA quick smoke test confirms each branch:\n\n```sql\nSELECT\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(1)      AS valid_customer,\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(2)      AS another_customer,\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(0)      AS not_found_zero,\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(-1)     AS not_found_negative,\n  TASTYBYTESDB.TASTYBYTES.fn_FormatCustomerName(999999) AS not_found_high;\n-- → JOHNSON, Alice | SMITH, Bob | NULL | NULL | NULL\n```\n\n### Step 6 — Capture Baselines and Run Functional Tests\n\nNow that the function is deployed, Cortex Code captures the source baselines and runs the cross-DB test:\n\n```bash\nscai test capture  --where \"source.canonicalName ILIKE '%fn_FormatCustomerName%'\"\nscai test validate --where \"source.canonicalName ILIKE '%fn_FormatCustomerName%'\"\n```\n\nResult:\n\n```\nSUMMARY: 13 test cases\nPassed: 13  Failed: 0  Errors: 0\n```\n\nAll 13 cases match SQL Server output exactly on the first run.\n\n### Step 7 — Claim and Deploy Wave 1 Remainder\n\nCortex Code now claims the remaining 3 wave-1 objects (1 procedure, 2 views) and walks them through deploy/test/fix.\n\n### Step 8 — Deploy Views (Hand-Fix `vw_TopSellingItems`)\n\nThe `vw_CustomerOrderHistory` view deploys cleanly. `vw_TopSellingItems` fails: SnowConvert flagged the SQL Server `CROSS APPLY ... TOP 5 ORDER BY` with `SSC-EWI-TS0082` and emitted an unresolved `!!!RESOLVE EWI!!!` marker plus a non-lateral `LEFT OUTER JOIN`, which won't run in Snowflake.\n\nReplace the file at `sqlserver-migration/snowflake/tastybytesdb/tastybytes/view/vw_topsellingitems.sql` with a window-function rewrite:\n\n```sql\nCREATE OR REPLACE VIEW TastyBytes.vw_TopSellingItems AS\nWITH agg AS (\n    SELECT\n        oh.TruckID, mi.MenuItemID, mi.ItemName,\n        SUM(od.Quantity)                AS TotalQuantitySold,\n        SUM(od.Quantity * od.UnitPrice) AS TotalRevenue\n    FROM TastyBytes.OrderDetail od\n    INNER JOIN TastyBytes.OrderHeader oh ON od.OrderID = oh.OrderID\n    INNER JOIN TastyBytes.MenuItem   mi ON od.MenuItemID = mi.MenuItemID\n    WHERE oh.OrderStatus = 'Completed'\n    GROUP BY oh.TruckID, mi.MenuItemID, mi.ItemName\n),\nranked AS (\n    SELECT a.*,\n           ROW_NUMBER() OVER (PARTITION BY a.TruckID\n                              ORDER BY a.TotalQuantitySold DESC) AS rn\n    FROM agg a\n)\nSELECT ft.TruckID, ft.TruckName,\n       r.MenuItemID, r.ItemName,\n       r.TotalQuantitySold, r.TotalRevenue\nFROM TastyBytes.FoodTruck ft\nINNER JOIN ranked r ON r.TruckID = ft.TruckID\nWHERE r.rn \u003C= 5;\n```\n\nRedeploy. Both views return 25 rows, matching the source.\n\n### Step 9 — Deploy and Test the Procedure\n\nCortex Code seeds tests for `sp_UpdateInventory` (a procedure that updates `Inventory` rows for a given truck, with an `Override` flag controlling absolute-set vs. increment behavior, and an early return when `TruckID IS NULL`):\n\n```bash\nscai test seed --where \"source.canonicalName ILIKE '%sp_UpdateInventory%'\" --append\n```\n\nCortex Code fills `test_cases:` with 6 rows covering:\n\n- Override = 1 (absolute set) for valid truck IDs (1, 3, 5).\n- Override = 0 (increment) for valid truck IDs (1, 2, 4).\n- A zero-stock-count case (`StockCount = 0.00, Override = 1`).\n\nDeploy the procedure:\n\n```sql\n-- (Cortex Code runs CREATE OR REPLACE PROCEDURE … from snowflake/…/sp_updateinventory.sql)\n```\n\nCapture baselines and run validation:\n\n```bash\nscai test capture  --where \"source.canonicalName ILIKE '%sp_UpdateInventory%'\"\nscai test validate --where \"source.canonicalName ILIKE '%sp_UpdateInventory%'\"\n```\n\nInspect the validation results from the framework's stored output:\n\n```sql\nSELECT TEST_NAME, PARAMETERS, STATUS, ERROR_MESSAGE\nFROM TASTYBYTESDB.VALIDATION.LATEST\nWHERE PROCEDURE_NAME ILIKE '%UpdateInventory%'\nORDER BY ID;\n```\n\nAll 6 cases come back as **PASS**:\n\n\n| Case                                             | Status | Notes                               |\n| ------------------------------------------------ | ------ | ----------------------------------- |\n| `TruckID = 1, StockCount = 100.00, Override = 1` | PASS   | Absolute-set on truck 1.            |\n| `TruckID = 2, StockCount = 50.50, Override = 0`  | PASS   | Increment on truck 2.               |\n| `TruckID = 3, StockCount = 200.00, Override = 1` | PASS   | Absolute-set on truck 3.            |\n| `TruckID = 4, StockCount = 25.00, Override = 0`  | PASS   | Increment on truck 4.               |\n| `TruckID = 5, StockCount = 0.00, Override = 1`   | PASS   | Absolute-set with zero stock count. |\n| `TruckID = 1, StockCount = 999.99, Override = 0` | PASS   | Large increment on truck 1.         |\n\n\nThe procedure produces identical post-run state on SQL Server and Snowflake across every test case, confirming the converted Snowflake Scripting body matches the original T-SQL semantics exactly.\n\n### Verify the Code Deployment\n\nA quick sanity check against `INFORMATION_SCHEMA`:\n\n```sql\nSELECT 'BASE TABLE' AS kind, COUNT(*) AS cnt\nFROM TASTYBYTESDB.INFORMATION_SCHEMA.TABLES\nWHERE table_schema = 'TASTYBYTES' AND table_type='BASE TABLE'\nUNION ALL SELECT 'VIEW',     COUNT(*) FROM TASTYBYTESDB.INFORMATION_SCHEMA.VIEWS      WHERE table_schema='TASTYBYTES'\nUNION ALL SELECT 'FUNCTION', COUNT(*) FROM TASTYBYTESDB.INFORMATION_SCHEMA.FUNCTIONS  WHERE function_schema='TASTYBYTES'\nUNION ALL SELECT 'PROCEDURE',COUNT(*) FROM TASTYBYTESDB.INFORMATION_SCHEMA.PROCEDURES WHERE procedure_schema='TASTYBYTES';\n```\n\nExpected:\n\n\n| KIND       | CNT |\n| ---------- | --- |\n| BASE TABLE | 11  |\n| VIEW       | 2   |\n| FUNCTION   | 1   |\n| PROCEDURE  | 1   |\n\n\nPlus the `TASTYBYTESDB.ETL_RESULTS.ETL_LOGS` helper table in `etl_results`.\n\n## Phase 3 — Verify the Migrated Data\n\nThe data was already migrated in Phase 2 / Step 3. Confirm row counts on Snowflake:\n\n```sql\nSELECT 'Country'       AS tbl, COUNT(*) AS row_cnt FROM TASTYBYTESDB.TASTYBYTES.Country\nUNION ALL SELECT 'City',          COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.City\nUNION ALL SELECT 'Customer',      COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.Customer\nUNION ALL SELECT 'FoodTruck',     COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.FoodTruck\nUNION ALL SELECT 'Menu',          COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.Menu\nUNION ALL SELECT 'MenuItem',      COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.MenuItem\nUNION ALL SELECT 'Inventory',     COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.Inventory\nUNION ALL SELECT 'OrderHeader',   COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.OrderHeader\nUNION ALL SELECT 'OrderDetail',   COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.OrderDetail\nUNION ALL SELECT 'EmployeeShift', COUNT(*) FROM TASTYBYTESDB.TASTYBYTES.EmployeeShift\nORDER BY tbl;\n```\n\n## Phase 4 — Deploy the dbt Project and the SSIS Task Graph\n\nWith the database workload migrated, the last phase is to deploy the SSIS-derived artifacts. SnowConvert produced two things under `sqlserver-migration/snowflake/_etl/daily_sales_agg/`:\n\n- A **task-graph SQL file** (`daily_sales_agg.sql`) defining a 4-task chain: a root task → an `_insert_start_log` task → a `_df_load_daily_sales` task that calls `EXECUTE DBT PROJECT …` → an `_insert_end_log` task. The root task has **no warehouse** and the children all use `WAREHOUSE=DUMMY_WAREHOUSE` placeholders.\n- A **dbt project** (`df_load_daily_sales/`) with two staging views, four ephemeral intermediate models, an incremental mart (`ole_db_destination` → materializes as `DAILYSALESAGG`), `dbt_project.yml`, and `profiles.yml` — but with placeholder values like `YOUR_PROJECT_NAME` and `YOUR_PROFILE_NAME`.\n\n### Prompt 2 — Deploy and Run Everything\n\nTell Cortex Code:\n\n\u003E ```\n\u003E Now I want to deploy my generated dbt project and my Snowflake task graph\n\u003E to Snowflake in the `snowflake` directory and using the `snow dbt deploy`\n\u003E command, the dbt project object in Snowflake should be deployed in\n\u003E the TastyBytes schema.\n\u003E Make sure to deploy my task graph using the `XSMALL_WH` warehouse\n\u003E for each task and that the dbt project is referenced through the \n\u003E TastyBytes schema. Once the dbt project and the task graph are both deployed\n\u003E execute the task graph and show me the rows of the materialized mart model\n\u003E in the dbt project.\n\u003E ```\n\nCortex Code loads the `dbt-projects-on-snowflake` skill and works through three high-level steps before triggering the run:\n\n1. **Patch the dbt project files.** SnowConvert wrote `dbt_project.yml`, `profiles.yml`, and `models/sources.yml` with placeholders (`YOUR_PROJECT_NAME`, `YOUR_PROFILE_NAME`, `YOUR_DB`, `YOUR_SCHEMA`). Cortex Code rewrites them with the concrete project name `df_load_daily_sales`, target `dev`, role `ACCOUNTADMIN`, warehouse `XSMALL_WH`, database `TASTYBYTESDB`, sources at `TASTYBYTESDB.TASTYBYTES`, and drops the `account` / `user` / `password` fields (dbt runs *inside* Snowflake, so those aren't needed).\n2. **Deploy the dbt project with `snow dbt deploy`:**\n  ```bash\n   snow dbt deploy df_load_daily_sales \\\n       --source \u003CPATH\u003E/snowflake/_etl/daily_sales_agg/df_load_daily_sales \\\n       --database TASTYBYTESDB --schema PUBLIC\n  ```\n   `snow dbt deploy` honors the connection's default schema over `--schema`, so the project actually lands at `**TASTYBYTESDB.TASTYBYTES.DF_LOAD_DAILY_SALES**` (matching the connection's `schema = \"TASTYBYTES\"`). Cortex Code re-points the task graph at the actual location.\n3. **Deploy the 4-task graph and run it.** Cortex Code rewrites `daily_sales_agg.sql`: every `WAREHOUSE=DUMMY_WAREHOUSE` becomes `WAREHOUSE = XSMALL_WH`, the root task gets `WAREHOUSE = XSMALL_WH` added, the `INSERT INTO etl_results.etl_logs` calls get fully qualified to `TASTYBYTESDB.ETL_RESULTS.ETL_LOGS`, and the `EXECUTE DBT PROJECT` call points at `TASTYBYTESDB.TASTYBYTES.DF_LOAD_DAILY_SALES`. Cortex Code applies the four `CREATE OR REPLACE TASK` statements, resumes the three child tasks, and triggers the root with `EXECUTE TASK TASTYBYTESDB.PUBLIC.daily_sales_agg`.\n\nOnce `EXECUTE TASK` is dispatched, Cortex Code polls `TASK_HISTORY` until all four tasks land at `SUCCEEDED`:\n\n```\nDAILY_SALES_AGG                       SUCCEEDED  ~1s\n  └─→ DAILY_SALES_AGG_INSERT_START_LOG  SUCCEEDED  ~1s\n        └─→ DAILY_SALES_AGG_DF_LOAD_DAILY_SALES SUCCEEDED ~14s  (EXECUTE DBT PROJECT)\n              └─→ DAILY_SALES_AGG_INSERT_END_LOG SUCCEEDED  ~1s\n```\n\nThe `EXECUTE DBT PROJECT` call ran `dbt build --target dev` inside Snowflake: 2 staging views, 4 ephemeral intermediate models (inlined as CTEs and not visible in `INFORMATION_SCHEMA.TABLES`), and 1 incremental mart materialized as `TASTYBYTESDB.PUBLIC.DAILYSALESAGG`. The two `_insert_*_log` tasks wrote start/end markers to `TASTYBYTESDB.ETL_RESULTS.ETL_LOGS`, exactly the side-effect SnowConvert lifted from the original SSIS package.\n\nFinally, Cortex Code shows the materialized mart:\n\n```sql\nSELECT * FROM TASTYBYTESDB.PUBLIC.DAILYSALESAGG ORDER BY TRUCKID, SALEDATE;\n```\n\n22 rows, one per `(TruckID, SaleDate)` pair from completed orders, aggregating `OrderCount`, `GrossRevenue`, `TotalTips`, and `ItemsSold` per truck-day. A sample of the output:\n\n\n| TRUCKID | SALEDATE   | ORDERCOUNT | GROSSREVENUE | TOTALTIPS | ITEMSSOLD |\n| ------- | ---------- | ---------- | ------------ | --------- | --------- |\n| 1       | 2024-07-10 | 1          | 59.96        | 10.00     | 2         |\n| 1       | 2024-08-15 | 1          | 16.99        | 2.50      | 1         |\n| 1       | 2024-09-01 | 1          | 10.99        | 2.00      | 1         |\n| 1       | 2024-09-12 | 1          | 55.96        | 8.00      | 2         |\n| 2       | 2024-07-15 | 1          | 25.98        | 4.00      | 3         |\n| 2       | 2024-09-15 | 1          | 4.50         | 1.00      | 1         |\n| 3       | 2024-07-23 | 1          | 128.91       | 18.00     | 3         |\n| …       | …          | …          | …            | …         | …         |\n| 11      | 2024-10-05 | 1          | 12.99        | 2.00      | 1         |\n\n\nTotals across the mart: 22 truck-day rows, 22 orders, $1,015.31 gross revenue, $139.50 tips, 39 items sold. Cross-checking against `TASTYBYTESDB.ETL_RESULTS.ETL_LOGS` shows the two markers `pkg_daily_sales_aggregate start` and `pkg_daily_sales_aggregate end` written one task apart, confirming the full task graph executed end-to-end.\n\nAt this point all four phases are complete:\n\n```\n✅ Setup       — project initialized, connections registered, local infra ready\n✅ Phase 1     — 19 objects extracted, 19 files converted, assessment generated\n✅ Phase 2     — 16 wave-1 objects deployed (deploy/test/fix loop)\n                 + 3 additional objects (1 procedure, 2 views) hand-fixed and deployed\n                 + 11 tables, 185 rows migrated locally\n                 + data validation skipped per kickoff prompt\n                 + ETL stabilization skipped per kickoff prompt\n✅ Phase 3     — row counts verified on Snowflake\n✅ Phase 4     — dbt project deployed via `snow dbt deploy`,\n                 4-task graph deployed with XSMALL_WH per task, executed end-to-end\n```\n\n\n\n## Conclusion And Resources\n\nCongratulations! You've taken a Microsoft SQL Server database and an SSIS pipeline all the way from source extraction to a fully deployed, populated, and *running* workload in Snowflake — driven end-to-end by the Cortex Code CLI and its `snowflake-migration:migration` skill, plus the `snow dbt deploy` workflow for the SnowConvert-generated dbt project.\n\nYou started from a blank project, set up **local** data-migration infrastructure (no SPCS / compute pool needed), registered 19 SQL Server objects and 1 SSIS package, converted them with SnowConvert AI, generated a deployment plan with a classified SSIS assessment, deployed every in-scope database object using a deploy/test/fix loop (hand-fixing 1 conversion artifact — a `CROSS APPLY` view rewritten with window functions), migrated 185 rows of data via the locally-run orchestrator + worker, and finally hand-patched and deployed both the SnowConvert-generated dbt project (`snow dbt deploy`) and the SnowConvert-generated Snowflake task graph (with `XSMALL_WH` per task), executing the full graph end-to-end on demand.\n\n### What You Learned\n\n- How to drive an end-to-end SQL Server + SSIS migration with **Cortex Code** and the bundled `snowflake-migration:migration` skill, using just five chat prompts.\n- How **SnowConvert AI** extracts, converts, and deploys a SQL Server workload to Snowflake, and how to read its EWIs, FDMs, and PRFs.\n- How to triage and hand-fix common SnowConvert outputs — rewriting a SQL Server `CROSS APPLY ... TOP` with `ROW_NUMBER() OVER (PARTITION BY ...)`.\n- How to run the data-migration orchestrator and Data Exchange Worker **locally** for SQL Server → Snowflake table loads, without provisioning SPCS.\n- How to use the seed → capture → validate testing framework against your source database to verify the migrated function produces identical output to the SQL Server original.\n- How to deploy a SnowConvert-generated dbt project to Snowflake via `snow dbt deploy`, then point a Snowflake task graph at it with `EXECUTE DBT PROJECT`.\n\n### Related Resources\n\n- **Quickstart source code**: `[sfguide-scai-e2e-ssis-migration](https://github.com/Snowflake-Labs/sfguide-scai-e2e-ssis-migration)` — DDLs, sample data, the SSIS package, and the initial Snowflake setup used throughout this guide.\n- [SnowConvert AI CLI documentation](https://docs.snowflake.com/en/migrations/snowconvert-docs/general/user-guide/snowconvert/command-line-interface/README) — reference for `scai code extract`, `scai code convert`, `scai code deploy`, `scai data migrate`, `scai test seed/capture/validate`.\n- [Cortex Code CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli) — install and usage guide for the CLI that hosts the migration skills.\n- [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/introduction/introduction) — used here for the connection config that both `scai` and `snow` share, and for `snow dbt deploy`.\n- [dbt Projects on Snowflake](https://docs.snowflake.com/en/user-guide/data-engineering/dbt-projects/about-dbt-projects) — target pattern for the replatformed SSIS data flows produced by `migrate-etl-package`.\n- [Snowflake tasks](https://docs.snowflake.com/en/user-guide/tasks-intro) — background for the four-task graph with `EXECUTE DBT PROJECT` deployed in Phase 4.\n- [Snowflake Scripting reference](https://docs.snowflake.com/en/developer-guide/snowflake-scripting/index) — background for the procedure rewrites.\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-bb44a75a81","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-cbccbe3332",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-d94dbd9aa0","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-15",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-7368070958","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. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-5855703767",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isActiveTOC":false,"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-633dd2957d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-b55366d15b","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-0869030cc8",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/snowflake-aim-e2e-ssis-migration","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EProject 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