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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-4a7c8f7ad3","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["How To Accelerate Data Warehouse Migrations With SnowConvert 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data migration is the process of moving data between locations like on-premises servers, cloud platforms, or SaaS applications. This is typically driven by key business events like modernizing legacy infrastructure, centralizing data for analytics, or preparing datasets for AI training. However, an unsuccessful migration can lead to data loss, business disruptions and compliance issues.\u003C/p\u003E\n","\u003Cp\u003ESnowConvert AI and Snowpark Migration Accelerator are both tools from Snowflake designed to assist in code migration, but they serve different purposes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESnowConvert AI is an AI-powered solution for migrating entire data ecosystems, including code and objects, from a wide range of legacy data warehouses and BI tools (such as Teradata, Oracle, and Google BigQuery) to Snowflake. Its core function is to intelligently automate the conversion of SQL-based workloads, reducing manual effort. This guide provides a complete, end-to-end walkthrough of a SnowConvert AI migration project, from initial setup and extraction to code conversion, deployment, and final data validation.\u003C/p\u003E\n","\u003Cp\u003EIn contrast, the \u003Ca href=\"/en/data-cloud/snowpark/migration-accelerator/\"\u003ESnowpark Migration Accelerator\u003C/a\u003E is a specialized, free tool for migrations from Databricks or another Spark platform. Briefly, it converts existing Apache Spark codebases (in Python or Scala) to leverage the Snowpark API.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_03.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n","\u003Cp\u003EThis guide will walk you through the complete end-to-end migration process, which is broken down into four key parts:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPart 1: Preparation and Setup\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESetup and Installation\u003C/li\u003E\u003Cli\u003EUnderstanding SnowConvert AI Projects\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPart 2: Code Extraction and Conversion\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDatabase Object Extraction\u003C/li\u003E\u003Cli\u003ECode Conversion and Assessment\u003C/li\u003E\u003Cli\u003EAI Verification\u003C/li\u003E\u003Cli\u003ESSIS Replatform\u003C/li\u003E\u003Cli\u003EPower BI Repointing\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPart 3: Migration Execution\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDeployment to Snowflake\u003C/li\u003E\u003Cli\u003EData Migration\u003C/li\u003E\u003Cli\u003EData Validation\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPart 4: Post-Migration and Resources\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETroubleshooting\u003C/li\u003E\u003Cli\u003EBest Practices\u003C/li\u003E\u003Cli\u003EConclusion and Resources\u003C/li\u003E\u003Cli\u003ENext Steps\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EBy the end of this quickstart guide, you will have a complete understanding of the SnowConvert AI migration workflow and will be able to execute end-to-end database migrations from legacy platforms to Snowflake, including data extraction, code conversion, deployment, data migration, and validation.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EValid Snowflake account with appropriate permissions\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEnsure your target role has the appropriate privileges. Specifically, the role needs:\n\u003Col\u003E\u003Cli\u003EThe \u003Ccode\u003ECREATE MIGRATION\u003C/code\u003E privilege at the account level. You can grant this with the following command:\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EGRANT CREATE MIGRATION ON ACCOUNT TO ROLE <your_role>;\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003EThe ability to create databases and objects within them.\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAccess to source database system (Teradata, Oracle, SQL Server, etc.)\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETo prepare for the migration, verify that the role for your legacy data source has the necessary permissions. The role must have:\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ERead access\u003C/strong\u003E to all objects you plan to migrate.\u003C/li\u003E\u003Cli\u003EThe ability to extract the \u003Cstrong\u003EData Definition Language (DDL)\u003C/strong\u003E code for these objects.\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECompany domain email address for access code generation\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWindows 11+, macOS 13.3+, or Linux operating system\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EMinimum 4GB RAM\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EInternet connectivity for tool updates and licensing\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EMulti-factor authentication (MFA) enabled for Snowflake account\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup and Installation\u003C/h2\u003E\n","\u003Ch3\u003ESystem Requirements\u003C/h3\u003E\n","\u003Cp\u003EBefore installing \u003Ca href=\"/en/migrate-to-the-cloud/snowconvert-ai/\"\u003ESnowConvert AI\u003C/a\u003E, ensure your system meets the minimum requirements:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWindows:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWindows 11 or newer\u003C/li\u003E\u003Cli\u003EMinimum 4GB RAM (8GB+ recommended)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EmacOS:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EmacOS Ventura 13.3.1 or newer\u003C/li\u003E\u003Cli\u003EMinimum 4GB RAM\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELinux:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAny modern Linux distribution\u003C/li\u003E\u003Cli\u003EMinimum 4GB RAM\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EObtaining SnowConvert AI\u003C/h3\u003E\n","\u003Cp\u003ESnowConvert AI can be downloaded from this \u003Ca href=\"/en/migrate-to-the-cloud/snowconvert-ai/\"\u003Elink.\u003C/a\u003E It's highly recommended to complete the free course "\u003Ca href=\"https://training.snowflake.com/lmt/clmsCatalogDetails.prMain?site=sf&in_offeringId=130596852\"\u003ESnowConvert AI for Conversion\u003C/a\u003E" available at Snowflake Training, which provides both overview and hands-on technical training.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowConvert AI is free for all users\u003C/li\u003E\u003Cli\u003EDownload Snowconvert AI from any Snowflake account via Snowsight by navigating to Ingestion/Migrations in the left-hand menu.\u003C/li\u003E\u003Cli\u003EProfessional services are available for additional migration support. Learn more and contact a representative \u003Ca href=\"/en/solutions/professional-services/\"\u003Ehere\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EInstalling SnowConvert AI\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWindows Installation\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDownload the \u003Ccode\u003E.exe\u003C/code\u003E installer from the Snowflake portal\u003C/li\u003E\u003Cli\u003EDouble-click the downloaded file to start the installation\u003C/li\u003E\u003Cli\u003EFollow the installation wizard prompts\u003C/li\u003E\u003Cli\u003ELaunch SnowConvert AI from the Applications menu\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EmacOS Installation\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDownload the \u003Ccode\u003E.dmg\u003C/code\u003E file from the Snowflake portal\u003C/li\u003E\u003Cli\u003EDouble-click the \u003Ccode\u003E.dmg\u003C/code\u003E file to mount it\u003C/li\u003E\u003Cli\u003EDrag SnowConvert AI to the Applications folder\u003C/li\u003E\u003Cli\u003ELaunch the application from Applications\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EUnderstanding Access Codes\u003C/h3\u003E\n","\u003Cp\u003ETo ensure quality, access codes are required to use SnowConvert AI and are valid for 3 months. Starting with version 1.2.0, the tool automatically generates access codes and supports all source platforms with a single code. For more information on access codes, visit the \u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/general/getting-started/download-and-access\"\u003ESnowConvert AI - Download and Access\u003C/a\u003E page.\u003C/p\u003E\n","\u003Ch3\u003EObtaining an Access Code\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMethod 1: Through the Application\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen SnowConvert AI\u003C/li\u003E\u003Cli\u003EClick "Get an Access Code" link on the Project Creation page \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_04.png\" alt=\"\"\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_05.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote:\nIf you face connectivity issues due to restrictive firewall settings, please refer to this \u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/general/frequently-asked-questions-faq#why-i-am-not-receiving-an-access-code\"\u003Edocument\u003C/a\u003E for troubleshooting guidance\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EFill out the required information form\u003C/li\u003E\u003Cli\u003ESubmit and wait for email confirmation\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMethod 2: Help Menu\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_06.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELaunch SnowConvert AI\u003C/li\u003E\u003Cli\u003EGo to main menu bar\u003C/li\u003E\u003Cli\u003EClick "Get an access code" option\u003C/li\u003E\u003Cli\u003EComplete the form and submit\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch2\u003EUnderstanding SnowConvert AI Projects\u003C/h2\u003E\n","\u003Cp\u003EThe process begins when a user creates a new project and assigns it a name. The user then selects the source code to be migrated; in the provided example, the source is SQL Server. To utilize SnowConvert, a free access code is required, which can be requested and is sent immediately via email. A local working folder must also be set up on the user's machine for the project. In addition to code conversion, the tool offers optional features to replatform ETLs, like Informatica or SSIS, and to repoint Power BI reports to the new Snowflake database.\u003C/p\u003E\n","\u003Ch3\u003EWhat is a SnowConvert Project?\u003C/h3\u003E\n","\u003Cp\u003EA SnowConvert AI Project (\u003Ccode\u003E.snowct\u003C/code\u003E file) is a local file that stores:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESource platform configuration\u003C/li\u003E\u003Cli\u003EConversion settings\u003C/li\u003E\u003Cli\u003EProject status and progress\u003C/li\u003E\u003Cli\u003EAssessment and conversion results\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EEach time you "Save & Start Assessment", SnowConvert AI creates a \u003Ccode\u003E.snowct\u003C/code\u003E project file. The file is saved in the same folder as your input source code and it contains all project configurations and status.\u003C/p\u003E\n","\u003Cp\u003EHere are some of the benefits of SnowConvert AI project files:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EResume work from where you left off\u003C/li\u003E\u003Cli\u003EMaintain consistent conversion settings\u003C/li\u003E\u003Cli\u003ETrack progress across migration phases\u003C/li\u003E\u003Cli\u003EShare project configurations with team members\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EProject Setup\u003C/h3\u003E\n","\u003Cp\u003ETo initiate the migration process:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELaunch SnowConvert AI\u003C/li\u003E\u003Cli\u003EClick "Create New Project"\u003C/li\u003E\u003Cli\u003ESelect source platform (Teradata, Oracle, SQL Server, etc.)\u003C/li\u003E\u003Cli\u003EChoose input folder containing source code\u003C/li\u003E\u003Cli\u003ESelect output folder for converted code\u003C/li\u003E\u003Cli\u003EEnter access code\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EWhen resuming the migration process:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDouble-click on the \u003Ccode\u003E.snowct\u003C/code\u003E file. This will open SnowConvert AI.\u003C/li\u003E\u003Cli\u003EUse "Open Project" button in the application\u003C/li\u003E\u003Cli\u003EAccess recent projects via File → Open Recents\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EDatabase Object Extraction\u003C/h2\u003E\n","\u003Ch3\u003EExtraction Overview\u003C/h3\u003E\n","\u003Cp\u003EAfter the project setup, the extraction phase begins. For a source like SQL Server, SnowConvert AI can connect directly to the database catalog to extract the Data Definition Language (DDL). As an alternative, code can be accessed from a local drive or a Git repository. During the extraction, SnowConvert AI connects to the source database and pulls the definitions for objects such as tables, views, and stored procedures. Once complete, it provides a summary of all the objects that were successfully extracted, such as the number of databases, schemas, and tables.\u003C/p\u003E\n","\u003Cp\u003ESnowConvert AI provides direct database connectivity to extract objects from source systems, currently supporting SQL Server and Amazon Redshift.\u003C/p\u003E\n","\u003Cp\u003EFor other sources, you will still need to extract your SQL code into .sql files. You can use database export scripts here: \u003Ca href=\"https://github.com/Snowflake-Labs/SC.DDLExportScripts\"\u003Ehttps://github.com/Snowflake-Labs/SC.DDLExportScripts\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003ESQL Server Extraction\u003C/h3\u003E\n","\u003Cp\u003ESnowConvert AI offers flexible authentication options to seamlessly integrate with your existing SQL Server environments. The platform supports two primary methods, ensuring secure and efficient access to your databases:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStandard SQL Server Authentication:\u003C/strong\u003E Uses a username and password.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_07.png\" alt=\"\"\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWindows Authentication (Integrated Security):\u003C/strong\u003E Leverages the Windows user's security credentials, often preferred in enterprise environments for seamless, secure integration with existing Windows security policies.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_08.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESecurity Options:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETrust Server Certificate (required if database uses trusted certificates)\u003C/li\u003E\u003Cli\u003EEncrypt Connection (required for encrypted connections)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExtractable Objects:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETables (structure and metadata)\u003C/li\u003E\u003Cli\u003EViews (definitions and dependencies)\u003C/li\u003E\u003Cli\u003EFunctions (scalar and table-valued)\u003C/li\u003E\u003Cli\u003EStored procedures (complete logic)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAmazon Redshift Extraction\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAuthentication Methods:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIAM Provisioned Cluster\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_09.png\" alt=\"\"\u003E\u003C/li\u003E\u003Cli\u003EIAM Serverless\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_10.png\" alt=\"\"\u003E\u003C/li\u003E\u003Cli\u003EStandard authentication\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_11.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExtractable Objects:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETables\u003C/li\u003E\u003Cli\u003EViews\u003C/li\u003E\u003Cli\u003EMaterialized views\u003C/li\u003E\u003Cli\u003EStored procedures\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EExtraction Process\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_12.png\" alt=\"\"\u003E\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EConfigure Connection:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003ESelect authentication method\u003C/li\u003E\u003Cli\u003EProvide connection credentials\u003C/li\u003E\u003Cli\u003EConfigure security settings if required\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\u003Cstrong\u003EConnect to Database:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EClick "Connect to Database"\u003C/li\u003E\u003Cli\u003EWait for "Connection established" notification\u003C/li\u003E\u003Cli\u003EBrowse available databases and schemas\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\u003Cstrong\u003ESelect Objects:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_13.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EExpand database and schema trees\u003C/li\u003E\u003Cli\u003ESelect specific objects for extraction\u003C/li\u003E\u003Cli\u003EReview object dependencies\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\u003Cstrong\u003EExecute Extraction:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_14.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EClick "Extract Objects"\u003C/li\u003E\u003Cli\u003EMonitor extraction progress\u003C/li\u003E\u003Cli\u003EReview extraction results\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003E\u003Cstrong\u003EValidate Results:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_15.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EClick "View Last Extraction Results"\u003C/li\u003E\u003Cli\u003EConfirm all required objects extracted successfully\u003C/li\u003E\u003Cli\u003ENote any extraction failures or warnings\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example extracted object structure\n-- Original SQL Server procedure\nCREATE PROCEDURE GetCustomerOrders\n    @CustomerID INT\nAS\nBEGIN\n    SELECT OrderID, OrderDate, TotalAmount\n    FROM Orders\n    WHERE CustomerID = @CustomerID\nEND\n\n-- Will be prepared for conversion to Snowflake format\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECode Conversion and Assessment\u003C/h2\u003E\n","\u003Ch3\u003EConversion Process Overview\u003C/h3\u003E\n","\u003Cp\u003ESnowConvert AI performs both assessment and conversion in a single unified process, generating comprehensive reports and converted code simultaneously.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1: Conversion Settings\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EEncoding:\u003C/strong\u003E Select appropriate character encoding (UTF-8 default)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom Schema:\u003C/strong\u003E Specify target schema name if different from source\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase Name:\u003C/strong\u003E Set custom database name for converted objects\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETarget Language:\u003C/strong\u003E Choose SnowScript or JavaScript for procedures\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EComments:\u003C/strong\u003E Enable commenting of nodes with missing dependencies\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2: Execute Conversion\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick "Save & Start Assessment"\u003C/li\u003E\u003Cli\u003EMonitor conversion progress\u003C/li\u003E\u003Cli\u003EReview completion status\u003C/li\u003E\u003Cli\u003EExamine generated reports\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EUnderstanding Conversion Results\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECode Completeness Metric:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_16.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EScore below 100% indicates missing object references\u003C/li\u003E\u003Cli\u003EReview dependencies before deployment\u003C/li\u003E\u003Cli\u003EAddress missing references in source code\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConversion Status Indicators:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EGreen:\u003C/strong\u003E Successfully converted, ready for deployment\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EYellow:\u003C/strong\u003E Functional Data Model (FDM) warnings present\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERed:\u003C/strong\u003E Equivalent Work Item (EWI) errors require attention\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAssessment Reports:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EConversion summary statistics\u003C/li\u003E\u003Cli\u003EObject-by-object conversion status\u003C/li\u003E\u003Cli\u003EComplexity analysis and recommendations\u003C/li\u003E\u003Cli\u003EMigration effort estimates\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example conversion result\n-- Original Teradata SQL\nSELECT \n    customer_id,\n    SUM(order_amount) as total_orders\nFROM orders\nWHERE order_date >= ADD_MONTHS(CURRENT_DATE, -12)\nGROUP BY customer_id;\n\n-- Converted Snowflake SQL\nSELECT \n    customer_id,\n    SUM(order_amount) as total_orders\nFROM orders\nWHERE order_date >= DATEADD(MONTH, -12, CURRENT_DATE)\nGROUP BY customer_id;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EAI Verification\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EThe AI Verification step (currently in Public Preview) adds a layer of intelligence to migrations by using AI agents to automatically verify and resolve errors that occur after the deterministic conversion step (the Code Conversion step is executed by a transpiler). The AI fixes are grounded with tests over synthetically generated data. This improves accuracy, reduces manual effort, and makes migrations faster, more reliable, and more scalable. AI will attempt to execute the converted code and fix issues if it is not working.\u003C/p\u003E\n","\u003Ch3\u003EStep-by-Step flow\u003C/h3\u003E\n","\u003Cp\u003EAfter the code conversion step customers can opt to use AI Verification to improve the quality of the converted code. If a customer is not allowed to use AI, this step can be skipped and continue to the Deployment of the converted objects ( as you can see in the image below, SKIP AI VERIFICATION button shows in the lower left corner of the screen). To continue with AI Verification we need to select which objects we want to verify.  Sometimes certain objects will be auto selected by SnowConvert AI because they are dependencies of the selected objects. So don't be scared if you select one procedure but the tool auto selects a few tables, views and even other procedures. This means all those objects are required to be able to execute the selected stored procedure.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_17.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce objects are selected, the VERIFY CODE can be clicked, which will display a special screen with a set of disclaimers. It is important to read and understand these disclaimers before proceeding. The AI will be executed in the customer Snowflake account using a Cortex Complete function. The tool will use the same Snowflake connection that was used for Source Code Extraction and that will be used for Deployment and Data Migration.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_18.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce the user accepts all of the disclaimers, they can proceed with the actual verification. This step may take a long time as AI needs to upload files to a Snowflake stage to complete the verification process. At the end of the process, you will get a result screen similar to the image below.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_19.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAt the top of your screen, you'll find a summary of the AI Results, followed by detailed information for each validated object. To gain a deeper understanding of how the AI reviewed a specific object, click "SEE DETAILS" for that object. This will display an explanation similar to the example shown in the following image.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_20.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIt is important to understand that the migration engineer needs to review the AI results and merge them with the initial code conversion results.\u003C/p\u003E\n","\u003Ch2\u003ESSIS Replatform\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003ESSIS Replatform is a specialized SnowConvert AI capability designed to convert SQL Server Integration Services (SSIS) \u003Ccode\u003E.dtsx\u003C/code\u003E packages to Snowflake-native solutions. This feature transforms complex ETL workflows into modern data pipelines using Snowflake tasks, procedures, and dbt projects.\u003C/p\u003E\n","\u003Cp\u003EFor optimal conversion results, ensure that your input includes valid \u003Ccode\u003E.dtsx\u003C/code\u003E files and all dependent database objects (DDL scripts for tables, views, functions, and procedures referenced by the SSIS packages).\u003C/p\u003E\n","\u003Cp\u003EThe replatform process separates SSIS packages into two components:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EControl Flow Orchestration:\u003C/strong\u003E Converted to SQL scripts with Snowflake tasks and stored procedures.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData Flow Tasks:\u003C/strong\u003E Transformed into individual dbt projects for each data flow component.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EProject Setup for SSIS Replatform\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ELaunch SnowConvert AI\u003C/li\u003E\u003Cli\u003EClick "Create New Project"\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Select "SQL Server" as the source platform\u003C/li\u003E\u003Cli\u003EIn the extraction configuration section, select "Replatform" option\u003C/li\u003E\u003Cli\u003EEnter the path to your SSIS \u003Ccode\u003E.dtsx\u003C/code\u003E files\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EDTSX Conversion Process\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EProceed with the conversion process.\u003C/li\u003E\u003Cli\u003ESnowConvert AI analyzes all \u003Ccode\u003E.dtsx\u003C/code\u003E files and dependencies\u003C/li\u003E\u003Cli\u003EConversion proceeds following the standard SnowConvert AI workflow\u003C/li\u003E\u003Cli\u003EControl flow tasks converted to Snowflake stored procedures and tasks\u003C/li\u003E\u003Cli\u003EData flow tasks converted to individual dbt projects\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe converted ETL code will be available in the SnowConvert AI output folder under the \u003Ccode\u003EETL\u003C/code\u003E directory. An \u003Ccode\u003Eetl_configuration\u003C/code\u003E folder will also be created containing utilities and configuration files. Additionally, \u003Ccode\u003EETL.Elements.NA.csv\u003C/code\u003E and \u003Ccode\u003EETL.Issues.NA.csv\u003C/code\u003E files provide detailed information about the ETL conversion process and any issues encountered.\u003C/p\u003E\n","\u003Ch3\u003EOutput Structure\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eoutput/\n└── ETL/\n    └── [same folder structure as input]/\n        └── [Package_Name]/\n            ├── Orchestration.sql\n            ├── Data Flow Task 1/\n            │   ├── dbt_project.yml\n            │   ├── models/\n            │   └── sources/\n            └── [Additional Data Flow Tasks]/\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUnderstanding Conversion Results\u003C/h3\u003E\n","\u003Cp\u003EReview the generated CSV files for detailed conversion information:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EETL.Elements.NA.csv:\u003C/strong\u003E Details about converted ETL elements\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EETL.Issues.NA.csv:\u003C/strong\u003E Issues encountered during conversion\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDeployment Process\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EDeploy Snowflake tasks and stored procedures\u003C/li\u003E\u003Cli\u003EDeploy the dbt projects in your environment\u003C/li\u003E\u003Cli\u003EManually fix the failing components and validate model compilation and execution\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch2\u003EPower BI Repointing\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EPower BI repointing is a SnowConvert AI feature that allows users to redefine connections from different database sources to a Snowflake valid connector. Currently, the supported sources are:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/transact/etl-bi-repointing/power-bi-transact-repointing\"\u003ESQL Server\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/oracle/etl-bi-repointing/power-bi-oracle-repointing\"\u003EOracle\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/teradata/etl-bi-repointing/power-bi-teradata-repointing\"\u003ETeradata\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/redshift/etl-bi-repointing/power-bi-redshift-repointing\"\u003ERedshift\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/transact/etl-bi-repointing/power-bi-transact-repointing\"\u003EAzure Synapse\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/postgres/etl-bi-repointing/power-bi-postgres-repointing\"\u003EPostgreSQL\u003C/a\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIt is recommended that DDLs should be migrated with the Power BI repointing migration process, which would allow SnowConvert AI to identify objects such as tables and views.\u003C/p\u003E\n","\u003Ch3\u003EPower BI Repointing Set Up\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ESave your Power BI projects with .pbit extension.\u003C/li\u003E\u003Cli\u003EOpen SnowConvert AI.\u003C/li\u003E\u003Cli\u003EOptionally, add your DDLs.\u003C/li\u003E\u003Cli\u003ESelect the source language that you are using in your Power BI reports, i.e. SQL Server.\u003C/li\u003E\u003Cli\u003EAdd your .pbit files on the Power BI repointing section.\u003C/li\u003E\u003Cli\u003EClick on the “continue to conversion” button.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EPower BI Repointing Results\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen your repointed Power BI report.\u003C/li\u003E\u003Cli\u003EFill the Power BI parameters with your Snowflake account information: server link, warehouse name, and database name. (This is automatically added by SnowConvert AI).\u003C/li\u003E\u003Cli\u003ERefresh your data in your reports.\u003C/li\u003E\u003Cli\u003EDouble-check results.\u003C/li\u003E\u003Cli\u003ESave your repointed report with the format you need. i.e. .pbix.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EAssessment\u003C/h3\u003E\n","\u003Cp\u003ETo review the overall status of the repointed reports, you can review the “ETLAndBiRepointing” assessment report. This report contains the report name and a summary of the changed connectors.\u003C/p\u003E\n","\u003Ch2\u003EDeployment to Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EDeployment Overview\u003C/h3\u003E\n","\u003Cp\u003ESnowConvert AI facilitates the direct deployment of converted code objects and schemas to your Snowflake environment. This process includes comprehensive dependency management and precise execution ordering, ensuring a smooth and accurate transition.\u003C/p\u003E\n","\u003Ch3\u003EAuthentication Methods\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESSO Authentication:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIntegrates with the organization's Single Sign-On (SSO) provider for a seamless authentication experience.\u003C/li\u003E\u003Cli\u003ESupports enterprise identity management systems.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStandard Authentication:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUsername and password authentication\u003C/li\u003E\u003Cli\u003ERequires multi-factor authentication (MFA) enabled\u003C/li\u003E\u003Cli\u003EAccount identifier format: \u003Ccode\u003Eorgname-account-name\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPre-Deployment Validation\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1: Review Conversion Status\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EExamine status indicators for each object (e.g., successful, needs review).\u003C/li\u003E\u003Cli\u003EReview Converted Code: Examine the generated Snowflake SQL for key objects (e.g., procedures, functions) to confirm the logic is correctly translated before deployment.\u003C/li\u003E\u003Cli\u003EResolve any EWI (Error with Impact) errors before deployment.\u003C/li\u003E\u003Cli\u003EReview FDM (Further Development Mandatory) warnings and decide if deployment is acceptable.\u003C/li\u003E\u003Cli\u003EUse "Refresh Tree" to update status after code changes\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EMANDATORY:\nResolve any EWI (Error with Impact) errors before deployment. These errors represent logic that could not be reliably converted and will cause deployment failure or runtime errors if not corrected.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHandling EWI Errors:\u003C/strong\u003E\u003Cbr\u003E\nSnowConvert AI is designed to flag these high-impact errors, but cannot always fix them. To resolve an EWI, you must:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EExamine the code snippet\u003C/strong\u003E in the SnowConvert AI interface that triggered the EWI or better yet in your preferred Source Code IDE. \u003Ca href=\"/en/developers/guides/end2endmigration/\"\u003ELearn more\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EManually modify the converted source code\u003C/strong\u003E to fix the issue..\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnit test\u003C/strong\u003E on the corrected file.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EObject Selection:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect only successfully converted objects\u003C/li\u003E\u003Cli\u003EConsider deployment dependencies\u003C/li\u003E\u003Cli\u003EGroup related objects for batch deployment\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2: Initiate Deployment\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect objects for deployment\u003C/li\u003E\u003Cli\u003EClick "Deploy" button\u003C/li\u003E\u003Cli\u003EChoose authentication method\u003C/li\u003E\u003Cli\u003EProvide Snowflake credentials\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3: Authentication\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example Snowflake connection details\nAccount: myorg-myaccount\nUsername: migration_user\nWarehouse: MIGRATION_WH\nDatabase: TARGET_DB\nSchema: PUBLIC\nRole: MIGRATION_ROLE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 4: Execution Order\u003C/strong\u003E Deployment follows this automatic sequence:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDatabases (create container structure)\u003C/li\u003E\u003Cli\u003ESchemas (organize object namespaces)\u003C/li\u003E\u003Cli\u003ETables (establish data structures)\u003C/li\u003E\u003Cli\u003EViews (depend on table structures)\u003C/li\u003E\u003Cli\u003EFunctions (provide reusable logic)\u003C/li\u003E\u003Cli\u003EStored Procedures (may reference other objects)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 5: Monitor Progress\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETrack deployment status for each object\u003C/li\u003E\u003Cli\u003EReview execution logs and results\u003C/li\u003E\u003Cli\u003EAddress any deployment errors immediately\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDeployment Results\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESuccess Indicators:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EObject created successfully in Snowflake\u003C/li\u003E\u003Cli\u003EProper dependency resolution\u003C/li\u003E\u003Cli\u003ECorrect object permissions applied\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError Handling:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReview detailed error messages\u003C/li\u003E\u003Cli\u003EFix source code issues if needed\u003C/li\u003E\u003Cli\u003ERe-run conversion if necessary\u003C/li\u003E\u003Cli\u003ERetry deployment for failed objects\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example deployed object in Snowflake\nCREATE OR REPLACE PROCEDURE get_customer_orders(customer_id INTEGER)\nRETURNS STRING\nLANGUAGE SNOWSCRIPT\nAS\n$$\nBEGIN\n    LET result_cursor CURSOR FOR \n        SELECT order_id, order_date, total_amount\n        FROM orders\n        WHERE customer_id = :customer_id;\n    \n    RETURN 'Procedure executed successfully';\nEND;\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EData Migration\u003C/h2\u003E\n","\u003Ch3\u003EData Migration Overview\u003C/h3\u003E\n","\u003Cp\u003ESnowConvert AI provides automated data migration capabilities to transfer actual data from Teradata, Oracle, SQL Server, Amazon Redshift, Synapse, Sybase, BigQuery, Netezza, Postgres, Greenplum and Databricks SQL source tables to deployed Snowflake structures, with platform-specific optimization strategies.\u003C/p\u003E\n","\u003Cp\u003EFor migrations from Databricks or another Spark platform, you’ll use \u003Ca href=\"/en/data-cloud/snowpark/migration-accelerator/\"\u003ESnowpark Migration Accelerator\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EAmazon Redshift Data Migration\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMigration Strategy:\u003C/strong\u003E Redshift data is migrated using S3 as an intermediary storage layer:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EData unloaded from Redshift to PARQUET files in S3\u003C/li\u003E\u003Cli\u003EFiles copied directly from S3 to Snowflake tables\u003C/li\u003E\u003Cli\u003ETemporary files cleaned up automatically\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisites:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ES3 Bucket Configuration:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_21.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EBucket in same region as Redshift cluster\u003C/li\u003E\u003Cli\u003EEmpty bucket path (no existing files)\u003C/li\u003E\u003Cli\u003EProper IAM permissions configured\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\u003Cstrong\u003EIAM Role for Redshift:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis JSON snippet defines an AWS Identity and Access Management (IAM) role specifically for Amazon Redshift, which is crucial for data migration, particularly when Redshift data is migrated using S3 as an intermediary storage layer.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "Version": "2012-10-17",\n  "Statement": [\n    {\n      "Effect": "Allow",\n      "Action": [\n        "s3:PutObject",\n        "s3:GetObject",\n        "s3:GetBucketLocation",\n        "s3:ListBucket"\n      ],\n      "Resource": [\n        "arn:aws:s3:::your-bucket-name/*",\n        "arn:aws:s3:::your-bucket-name"\n      ]\n    }\n  ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\u003Cstrong\u003EIAM User for S3 Access:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis JSON snippet defines an AWS Identity and Access Management (IAM) user policy for S3 Access. This policy outlines the permissions granted to an IAM user to interact with a specific Amazon S3 bucket.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "Version": "2012-10-17",\n  "Statement": [\n    {\n      "Effect": "Allow",\n      "Action": [\n        "s3:PutObject",\n        "s3:GetObject",\n        "s3:GetObjectVersion",\n        "s3:DeleteObject",\n        "s3:DeleteObjectVersion",\n        "s3:ListBucket",\n        "s3:GetBucketLocation"\n      ],\n      "Resource": [\n        "arn:aws:s3:::your-bucket-name/*",\n        "arn:aws:s3:::your-bucket-name"\n      ]\n    }\n  ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMigration Process:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThe migration process outlines the essential steps for efficiently transferring data from Redshift to Snowflake, ensuring data integrity and performance.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfigure S3 Settings:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ES3 Bucket URL (must end with "/")\u003C/li\u003E\u003Cli\u003EIAM Role ARN for data unloading\u003C/li\u003E\u003Cli\u003EAccess Key ID and Secret Access Key\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESelect Tables:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_22.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EChoose tables for data migration\u003C/li\u003E\u003Cli\u003EReview table sizes and complexity\u003C/li\u003E\u003Cli\u003EConsider migration batch sizing\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\u003Cstrong\u003EExecute Migration:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EMonitor unload progress from Redshift\u003C/li\u003E\u003Cli\u003ETrack S3 file creation and validation\u003C/li\u003E\u003Cli\u003EObserve copy operations to Snowflake\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\u003Cstrong\u003EValidate Results:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_23.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003ECompare row counts between source and target\u003C/li\u003E\u003Cli\u003EVerify data integrity and completeness\u003C/li\u003E\u003Cli\u003EReview migration timing and performance\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESQL Server Data Migration\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMigration Strategy:\u003C/strong\u003E Direct data transfer using optimized connection protocols:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBulk data extraction from SQL Server\u003C/li\u003E\u003Cli\u003EStreaming transfer to Snowflake tables\u003C/li\u003E\u003Cli\u003EReal-time progress monitoring\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisites:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EValid SQL Server connection with read permissions\u003C/li\u003E\u003Cli\u003ENetwork connectivity to both systems\u003C/li\u003E\u003Cli\u003ESufficient bandwidth for data transfer\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMigration Process:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EVerify Connections:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003ETest source SQL Server connectivity\u003C/li\u003E\u003Cli\u003EConfirm Snowflake authentication\u003C/li\u003E\u003Cli\u003EValidate table structures match\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\u003Cstrong\u003ESelect Tables:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EChoose tables for migration\u003C/li\u003E\u003Cli\u003EReview dependencies and foreign keys\u003C/li\u003E\u003Cli\u003EPlan migration order if needed\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_24.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\u003Cstrong\u003EExecute Transfer:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EStart data migration process\u003C/li\u003E\u003Cli\u003EMonitor transfer progress and speed\u003C/li\u003E\u003Cli\u003EHandle any connection or timeout issues\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_25.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\u003Cstrong\u003EValidate Migration:\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003ECompare row counts automatically\u003C/li\u003E\u003Cli\u003EReview data type conversions\u003C/li\u003E\u003Cli\u003EVerify constraint compliance\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_26.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EData Validation\u003C/h2\u003E\n","\u003Ch3\u003EValidation Overview\u003C/h3\u003E\n","\u003Cp\u003ESnowConvert AI provides comprehensive data validation to ensure migration accuracy through schema validation and metrics validation, giving you confidence in your migrated data.\u003C/p\u003E\n","\u003Ch3\u003EValidation Prerequisites\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPython Requirements:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPython version ≥ 3.9 and < 3.13\u003C/li\u003E\u003Cli\u003EPython available in system PATH\u003C/li\u003E\u003Cli\u003EVirtual environment support\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003E# Verify Python installation\npython --version\n\n# Expected output format: Python 3.x.x\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EValidation Modes\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESchema Validations:\u003C/strong\u003E Verifies structural integrity of migrated tables:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETable names match exactly\u003C/li\u003E\u003Cli\u003EColumn names preserved correctly\u003C/li\u003E\u003Cli\u003EOrdinal positions maintained\u003C/li\u003E\u003Cli\u003EData types converted appropriately\u003C/li\u003E\u003Cli\u003ECharacter lengths preserved\u003C/li\u003E\u003Cli\u003ENumeric precision and scale maintained\u003C/li\u003E\u003Cli\u003ERow counts match between source and target\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMetrics Validations:\u003C/strong\u003E Compares aggregate data metrics between source and target:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMinimum and maximum values\u003C/li\u003E\u003Cli\u003EAverage calculations\u003C/li\u003E\u003Cli\u003ENull value counts\u003C/li\u003E\u003Cli\u003EDistinct value counts\u003C/li\u003E\u003Cli\u003EStandard deviation\u003C/li\u003E\u003Cli\u003EVariance calculations\u003C/li\u003E\u003Cli\u003ECustom business metrics\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EValidation Process\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisites:\u003C/strong\u003E Data validation requires Python ≥ 3.10 or < 3.13 to be installed and available in your PATH. To verify that Python is available in your PATH, run  \u003Ccode\u003Epython --version\u003C/code\u003E in your terminal (or Command Prompt on Windows). Download Python \u003Ca href=\"https://www.python.org/downloads/\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1: Environment Setup\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EData migration completion required\u003C/li\u003E\u003Cli\u003EPython environment validation\u003C/li\u003E\u003Cli\u003EVirtual environment creation\u003C/li\u003E\u003Cli\u003EDependency installation\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2: Validation Execution\u003C/strong\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_27.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick "Validate Data" in the migration interface\u003C/li\u003E\u003Cli\u003ESystem checks Python availability\u003C/li\u003E\u003Cli\u003ESets up isolated validation environment\u003C/li\u003E\u003Cli\u003EExecutes both schema and metrics validations\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3: Results Analysis\u003C/strong\u003E Validation results are categorized into three levels:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECategory\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDescription\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAction Required\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Cstrong\u003EPass\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EValues match exactly\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENo action needed\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Cstrong\u003EWarning\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EMinor differences (e.g., higher precision)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EReconcile data by: Applying transformation  Changing the ingestion process\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Cstrong\u003EFail\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EValues don't match\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EInvestigation required\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_28.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EValidation Reports\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESummary Table:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHigh-level validation status per table\u003C/li\u003E\u003Cli\u003EQuick identification of issues\u003C/li\u003E\u003Cli\u003EOverall migration success metrics\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDetailed CSV Reports:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EColumn-by-column validation results\u003C/li\u003E\u003Cli\u003ESpecific discrepancy details\u003C/li\u003E\u003Cli\u003ESharable with stakeholders\u003C/li\u003E\u003Cli\u003ESuitable for audit trails\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReport Structure:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Etable_name,column_name,validation_type,source_value,target_value,status,notes\ncustomers,customer_id,row_count,10000,10000,PASS,Counts match\norders,order_total,avg,156.78,156.78,PASS,Averages match\nproducts,price,max,999.99,999.99,PASS,Maximum values match\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ETroubleshooting\u003C/h2\u003E\n","\u003Ch3\u003ECommon Installation Issues\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPermission Problems:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EmacOS .config issues:\u003C/strong\u003E Ensure .config is a directory, not a file\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWindows AppData access:\u003C/strong\u003E Verify Snowflake Inc folder permissions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELinux installation:\u003C/strong\u003E May require sudo for system-wide installation\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENetwork Connectivity:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EFirewall blocking updates:\u003C/strong\u003E Whitelist \u003Ccode\u003Esctoolsartifacts.blob.core.windows.net\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAccess code generation:\u003C/strong\u003E Ensure access to \u003Ccode\u003Ehttps://snowconvert.snowflake.com/licensing\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELicense validation:\u003C/strong\u003E Check connectivity to licensing APIs\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVersion Management:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowConvert AI updates automatically when you launch it with an internet connection, but you can manually check for updates through the application's menu. If an update is available, the system will prompt you to download and install it. To verify the update is complete, you can check again in the menu to see the new version.\u003C/li\u003E\u003Cli\u003EIf facing any issues during the update, please uninstall previous versions and relaunch.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPerformance Optimization\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELarge Dataset Migration:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPlan migrations during off-peak hours\u003C/li\u003E\u003Cli\u003EUse incremental migration strategies for very large tables\u003C/li\u003E\u003Cli\u003EMonitor system resources during migration\u003C/li\u003E\u003Cli\u003EConsider table partitioning for optimal performance\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResource Management:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEnsure adequate memory (8GB+ recommended for large projects)\u003C/li\u003E\u003Cli\u003EMonitor disk space for temporary files\u003C/li\u003E\u003Cli\u003EUse SSD storage for better I/O performance\u003C/li\u003E\u003Cli\u003EClose unnecessary applications during migration\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESecurity Considerations\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAccess Control:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse principle of least privilege for database connections\u003C/li\u003E\u003Cli\u003EEnable MFA on all Snowflake accounts\u003C/li\u003E\u003Cli\u003ERegularly rotate access codes and credentials\u003C/li\u003E\u003Cli\u003EAudit migration activities and access logs\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Protection:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEncrypt sensitive data during transit\u003C/li\u003E\u003Cli\u003EUse secure connection protocols (SSL/TLS)\u003C/li\u003E\u003Cli\u003EImplement proper backup strategies\u003C/li\u003E\u003Cli\u003EMaintain audit trails for compliance\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example security configuration\n-- Grant minimal required permissions\nGRANT USAGE ON WAREHOUSE migration_wh TO ROLE migration_role;\nGRANT CREATE TABLE ON SCHEMA target_db.public TO ROLE migration_role;\nGRANT INSERT, SELECT ON ALL TABLES IN SCHEMA target_db.public TO ROLE migration_role;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMonitoring and Alerting\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMigration Monitoring:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETrack conversion success rates by object type\u003C/li\u003E\u003Cli\u003EMonitor data migration progress and timing\u003C/li\u003E\u003Cli\u003ESet up alerts for migration failures\u003C/li\u003E\u003Cli\u003EDocument migration performance baselines\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPost-Migration Monitoring:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EValidate application connectivity to new environment\u003C/li\u003E\u003Cli\u003EMonitor query performance in Snowflake\u003C/li\u003E\u003Cli\u003ETrack user adoption and satisfaction\u003C/li\u003E\u003Cli\u003EPlan for ongoing maintenance and optimization\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAlways test conversions in development environments first\u003C/li\u003E\u003Cli\u003EMaintain detailed migration documentation for future reference\u003C/li\u003E\u003Cli\u003EPlan rollback strategies for critical production migrations\u003C/li\u003E\u003Cli\u003EEngage Snowflake Professional Services for complex migrations\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EBest Practices\u003C/h2\u003E\n","\u003Cp\u003EAdhering to best practices is crucial for a successful and efficient migration to Snowflake. This section outlines key considerations across SSIS replatforming, general migration strategies, data validation, and code conversion, providing guidance to optimize performance, ensure data integrity, and streamline the overall process.\u003C/p\u003E\n","\u003Ch3\u003ESSIS Replatform\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EDocument SSIS package dependencies and custom components.\u003C/li\u003E\u003Cli\u003EReview \u003Ccode\u003EETL.Elements.NA.csv\u003C/code\u003E and \u003Ccode\u003EETL.Issues.NA.csv\u003C/code\u003E for conversion details.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EMigration\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPerformance Optimization:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMigrate large tables during off-peak hours\u003C/li\u003E\u003Cli\u003EUse parallel migration for multiple small tables\u003C/li\u003E\u003Cli\u003EMonitor network bandwidth utilization\u003C/li\u003E\u003Cli\u003EConsider table partitioning for very large datasets\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError Handling:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EImplement retry logic for transient failures\u003C/li\u003E\u003Cli\u003ELog detailed error information for troubleshooting\u003C/li\u003E\u003Cli\u003EMaintain rollback capabilities where possible\u003C/li\u003E\u003Cli\u003EValidate partial migrations before continuing\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Integrity Checks:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example validation query\n-- Compare row counts between source and target\nSELECT \n    'Source Count' as source,\n    COUNT(*) as row_count \nFROM source_database.schema.table_name\nUNION ALL\nSELECT \n    'Target Count' as source,\n    COUNT(*) as row_count \nFROM target_database.schema.table_name;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE:\nShould you require additional help resolving errors and ensuring data integrity, please email \u003Ca href=\"mailto:snowconvert-info@snowflake.com\"\u003Esnowconvert-info@snowflake.com\u003C/a\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EValidation\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPre-Validation Checklist:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEnsure data stability during validation (no updates)\u003C/li\u003E\u003Cli\u003EComplete all migration steps before validation\u003C/li\u003E\u003Cli\u003EHave sufficient system resources available\u003C/li\u003E\u003Cli\u003EPlan validation during maintenance windows\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInterpreting Results:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFocus on FAIL status items first\u003C/li\u003E\u003Cli\u003EInvestigate WARNING items for business impact\u003C/li\u003E\u003Cli\u003EDocument any acceptable discrepancies\u003C/li\u003E\u003Cli\u003ERerun validation after fixes\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECommon Issues and Solutions:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EIssue\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECause\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESolution\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ERow count mismatch\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EIncomplete migration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ERe-run data migration\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EPrecision differences\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EData type conversion\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EVerify acceptable business impact\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDate format variations\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ETimezone or format changes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EStandardize date handling\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENull handling differences\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EPlatform-specific null behavior\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EUpdate conversion rules\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003E# Example validation check (conceptual)\ndef validate_table_metrics(source_table, target_table):\n    validations = {\n        'row_count': compare_row_counts(source_table, target_table),\n        'column_stats': compare_column_statistics(source_table, target_table),\n        'data_types': validate_schema_compatibility(source_table, target_table)\n    }\n    return validations\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConversion\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECode Preparation:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClean up source code before conversion\u003C/li\u003E\u003Cli\u003ERemove commented-out legacy code\u003C/li\u003E\u003Cli\u003EEnsure consistent encoding across all files\u003C/li\u003E\u003Cli\u003EDocument complex business logic\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EProject Organization:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse meaningful project names and descriptions\u003C/li\u003E\u003Cli\u003EOrganize source code in logical folder structures\u003C/li\u003E\u003Cli\u003EMaintain backup copies of original source code\u003C/li\u003E\u003Cli\u003EDocument custom conversion settings\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuality Assurance:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReview conversion reports thoroughly\u003C/li\u003E\u003Cli\u003ETest converted code in development environment\u003C/li\u003E\u003Cli\u003EAddress EWI errors before deployment\u003C/li\u003E\u003Cli\u003EValidate complex business logic manually\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EThis guide has walked you through the complete lifecycle of data warehouse migration using SnowConvert AI, from initial setup and configuration to advanced topics like SSIS replatforming and AI verification. You are now equipped to confidently accelerate your migration projects, ensuring accuracy and efficiency as you transition to Snowflake's powerful platform.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EInstallation and Setup:\u003C/strong\u003E Successfully installed SnowConvert AI across different operating systems\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAccess Management:\u003C/strong\u003E Obtained and managed access codes for tool activation and learned troubleshooting techniques for licensing issues\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEnd-to-End Migration:\u003C/strong\u003E Executed complete database migrations including extraction, conversion, deployment, data migration, and validation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBest Practices:\u003C/strong\u003E Applied security, performance, and quality assurance practices for successful enterprise-grade migrations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETroubleshooting:\u003C/strong\u003E Identified and resolved common issues related to installation, conversion, and deployment processes\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EArticles:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://training.snowflake.com/lmt/!clmsLink.dt?site=sf&region=us&lang=en-us&type=O&id=130596852\"\u003ESnowConvert AI for Conversion Training Course\u003C/a\u003E - Comprehensive hands-on training\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/fundamentals/what-is-data-migration/\"\u003EWhat is Data Migration? Types, Strategy & Best Practices\u003C/a\u003E - Learn what data migration is, including types, strategies, best practices and data migration solutions for smooth data transfer and management.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/blog/best-practices-for-migrating-historical-data-to-snowflake/\"\u003EBest Practices for Migrating Historical Data to Snowflake\u003C/a\u003E - Best practices for extracting, transferring, uploading, and validating data when migrating to Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/blog/best-practices-transitioning-on-premises-to-cloud/\"\u003E9 Best Practices for Transitioning From On-Premises to Cloud with Snowflake\u003C/a\u003E - This article provides nine best practices for a smooth and efficient transition from on-premises data systems to Snowflake's cloud data platform.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDocumentation:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/overview\"\u003ESnowConvert AI Documentation\u003C/a\u003E: Complete technical reference\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/migrations/snowconvert-docs/general/release-notes/release-notes/README\"\u003ESnowConvert AI Release Notes\u003C/a\u003E: Recent release notes\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n","\u003Cp\u003ENow that you have successfully migrated your data warehouse to Snowflake using SnowConvert AI, consider these next steps to fully leverage the power of the Snowflake Data Cloud:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EExplore Advanced Snowflake Features:\u003C/strong\u003E Dive deeper into Snowflake's capabilities, such as advanced analytics with \u003Ca href=\"/en/product/features/snowpark/\"\u003ESnowpark\u003C/a\u003E, data sharing with other organizations, and building data applications directly on Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIntegrate with BI Tools:\u003C/strong\u003E Connect your newly migrated Snowflake data to your existing or new \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ecosystem-bi\"\u003EBusiness Intelligence (BI) tools\u003C/a\u003E (e.g., Power BI, Tableau, Looker) to create insightful dashboards and reports.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOptimize Performance and Cost:\u003C/strong\u003E Continuously monitor your Snowflake usage to \u003Ca href=\"https://docs.snowflake.com/en/guides-overview-performance\"\u003Eoptimize performance and costs\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EImplement Data Governance and Security:\u003C/strong\u003E Establish robust \u003Ca href=\"https://docs.snowflake.com/en/guides-overview-govern\"\u003Edata governance\u003C/a\u003E policies and enhance security measures within Snowflake using features like row-access policies, column-level security, and data masking.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAutomate Data Pipelines:\u003C/strong\u003E Transition to automated data ingestion and transformation pipelines using Snowflake features like \u003Ca href=\"/en/product/features/openflow/\"\u003EOpenflow\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro\"\u003ESnowpipe\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELeverage AI/ML Capabilities:\u003C/strong\u003E Integrate Snowflake with other cloud services and tools for \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview\"\u003Emachine learning\u003C/a\u003E and data science.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EApplication development:\u003C/strong\u003E \u003Ca href=\"/en/product/features/native-apps/\"\u003ENative Apps\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit\u003C/a\u003E Apps for building interactive data apps.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStay Updated with Snowflake Innovations:\u003C/strong\u003E Regularly check Snowflake's release notes, documentation, and community forums to stay informed about new features and best practices.\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"How To Accelerate Data Warehouse Migrations With SnowConvert AI","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\n\nAt its core, data migration is the process of moving data between locations like on-premises servers, cloud platforms, or SaaS applications. This is typically driven by key business events like modernizing legacy infrastructure, centralizing data for analytics, or preparing datasets for AI training. However, an unsuccessful migration can lead to data loss, business disruptions and compliance issues.\n\nSnowConvert AI and Snowpark Migration Accelerator are both tools from Snowflake designed to assist in code migration, but they serve different purposes. \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_01.png)\n\nSnowConvert AI is an AI-powered solution for migrating entire data ecosystems, including code and objects, from a wide range of legacy data warehouses and BI tools (such as Teradata, Oracle, and Google BigQuery) to Snowflake. Its core function is to intelligently automate the conversion of SQL-based workloads, reducing manual effort. This guide provides a complete, end-to-end walkthrough of a SnowConvert AI migration project, from initial setup and extraction to code conversion, deployment, and final data validation.\n\nIn contrast, the [Snowpark Migration Accelerator](/en/data-cloud/snowpark/migration-accelerator/) is a specialized, free tool for migrations from Databricks or another Spark platform. Briefly, it converts existing Apache Spark codebases (in Python or Scala) to leverage the Snowpark API.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_03.png)\n\n### What You'll Learn\n\nThis guide will walk you through the complete end-to-end migration process, which is broken down into four key parts:\n\n**Part 1: Preparation and Setup**\n\n* Setup and Installation  \n* Understanding SnowConvert AI Projects\n\n**Part 2: Code Extraction and Conversion**\n\n* Database Object Extraction  \n* Code Conversion and Assessment  \n* AI Verification  \n* SSIS Replatform  \n* Power BI Repointing\n\n**Part 3: Migration Execution** \n\n* Deployment to Snowflake  \n* Data Migration  \n* Data Validation\n\n**Part 4: Post-Migration and Resources**\n\n* Troubleshooting  \n* Best Practices  \n* Conclusion and Resources  \n* Next Steps\n\n### What You'll Build\n\nBy the end of this quickstart guide, you will have a complete understanding of the SnowConvert AI migration workflow and will be able to execute end-to-end database migrations from legacy platforms to Snowflake, including data extraction, code conversion, deployment, data migration, and validation.\n\n### Prerequisites\n\n* Valid Snowflake account with appropriate permissions  \n  * Ensure your target role has the appropriate privileges. Specifically, the role needs:  \n    1. The `CREATE MIGRATION` privilege at the account level. You can grant this with the following command:  \n       ```SQL\n       GRANT CREATE MIGRATION ON ACCOUNT TO ROLE \u003Cyour_role\u003E;\n       ```\n    2. The ability to create databases and objects within them. \n\n* Access to source database system (Teradata, Oracle, SQL Server, etc.)  \n  * To prepare for the migration, verify that the role for your legacy data source has the necessary permissions. The role must have:  \n    1. **Read access** to all objects you plan to migrate.  \n    2. The ability to extract the **Data Definition Language (DDL)** code for these objects.  \n\n* Company domain email address for access code generation  \n* Windows 11+, macOS 13.3+, or Linux operating system  \n* Minimum 4GB RAM  \n* Internet connectivity for tool updates and licensing  \n* Multi-factor authentication (MFA) enabled for Snowflake account\n\n## Setup and Installation\n\n\n### System Requirements\n\nBefore installing [SnowConvert AI](/en/migrate-to-the-cloud/snowconvert-ai/), ensure your system meets the minimum requirements:\n\n**Windows:**\n\n* Windows 11 or newer  \n* Minimum 4GB RAM (8GB+ recommended)\n\n**macOS:**\n\n* macOS Ventura 13.3.1 or newer  \n* Minimum 4GB RAM\n\n**Linux:**\n\n* Any modern Linux distribution  \n* Minimum 4GB RAM\n\n### Obtaining SnowConvert AI\n\nSnowConvert AI can be downloaded from this [link.](/en/migrate-to-the-cloud/snowconvert-ai/) It's highly recommended to complete the free course \"[SnowConvert AI for Conversion](https://training.snowflake.com/lmt/clmsCatalogDetails.prMain?site=sf&in_offeringId=130596852)\" available at Snowflake Training, which provides both overview and hands-on technical training.\n\n\u003E IMPORTANT:\n\u003E - SnowConvert AI is free for all users  \n\u003E - Download Snowconvert AI from any Snowflake account via Snowsight by navigating to Ingestion/Migrations in the left-hand menu.   \n\u003E - Professional services are available for additional migration support. Learn more and contact a representative [here](/en/solutions/professional-services/). \n\n### Installing SnowConvert AI\n\n**Windows Installation**\n\n1. Download the `.exe` installer from the Snowflake portal  \n2. Double-click the downloaded file to start the installation  \n3. Follow the installation wizard prompts  \n4. Launch SnowConvert AI from the Applications menu\n\n**macOS Installation**\n\n1. Download the `.dmg` file from the Snowflake portal  \n2. Double-click the `.dmg` file to mount it  \n3. Drag SnowConvert AI to the Applications folder  \n4. Launch the application from Applications\n\n### Understanding Access Codes\n\nTo ensure quality, access codes are required to use SnowConvert AI and are valid for 3 months. Starting with version 1.2.0, the tool automatically generates access codes and supports all source platforms with a single code. For more information on access codes, visit the [SnowConvert AI - Download and Access](https://docs.snowflake.com/en/migrations/snowconvert-docs/general/getting-started/download-and-access) page.\n\n### Obtaining an Access Code\n\n**Method 1: Through the Application**\n\n1. Open SnowConvert AI  \n2. Click \"Get an Access Code\" link on the Project Creation page ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_04.png)![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_05.png)  \n\u003E Note:\n\u003E If you face connectivity issues due to restrictive firewall settings, please refer to this [document](https://docs.snowflake.com/en/migrations/snowconvert-docs/general/frequently-asked-questions-faq#why-i-am-not-receiving-an-access-code) for troubleshooting guidance \n4. Fill out the required information form  \n5. Submit and wait for email confirmation\n\n**Method 2: Help Menu**\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_06.png)\n\n1. Launch SnowConvert AI  \n2. Go to main menu bar  \n3. Click \"Get an access code\" option  \n4. Complete the form and submit\n\n## Understanding SnowConvert AI Projects\n\n\nThe process begins when a user creates a new project and assigns it a name. The user then selects the source code to be migrated; in the provided example, the source is SQL Server. To utilize SnowConvert, a free access code is required, which can be requested and is sent immediately via email. A local working folder must also be set up on the user's machine for the project. In addition to code conversion, the tool offers optional features to replatform ETLs, like Informatica or SSIS, and to repoint Power BI reports to the new Snowflake database.\n\n### What is a SnowConvert Project?\n\nA SnowConvert AI Project (`.snowct` file) is a local file that stores:\n\n* Source platform configuration  \n* Conversion settings  \n* Project status and progress  \n* Assessment and conversion results\n\nEach time you \"Save & Start Assessment\", SnowConvert AI creates a `.snowct` project file. The file is saved in the same folder as your input source code and it contains all project configurations and status.\n\nHere are some of the benefits of SnowConvert AI project files:\n\n* Resume work from where you left off  \n* Maintain consistent conversion settings  \n* Track progress across migration phases  \n* Share project configurations with team members\n\n### Project Setup\n\nTo initiate the migration process:\n\n1. Launch SnowConvert AI  \n2. Click \"Create New Project\"  \n3. Select source platform (Teradata, Oracle, SQL Server, etc.)  \n4. Choose input folder containing source code  \n5. Select output folder for converted code  \n6. Enter access code\n\nWhen resuming the migration process:\n\n* Double-click on the `.snowct` file. This will open SnowConvert AI.   \n* Use \"Open Project\" button in the application  \n* Access recent projects via File → Open Recents\n\n## Database Object Extraction\n\n\n### Extraction Overview\n\nAfter the project setup, the extraction phase begins. For a source like SQL Server, SnowConvert AI can connect directly to the database catalog to extract the Data Definition Language (DDL). As an alternative, code can be accessed from a local drive or a Git repository. During the extraction, SnowConvert AI connects to the source database and pulls the definitions for objects such as tables, views, and stored procedures. Once complete, it provides a summary of all the objects that were successfully extracted, such as the number of databases, schemas, and tables.\n\nSnowConvert AI provides direct database connectivity to extract objects from source systems, currently supporting SQL Server and Amazon Redshift.\n\nFor other sources, you will still need to extract your SQL code into .sql files. You can use database export scripts here: [https://github.com/Snowflake-Labs/SC.DDLExportScripts](https://github.com/Snowflake-Labs/SC.DDLExportScripts) \n\n### SQL Server Extraction\n\nSnowConvert AI offers flexible authentication options to seamlessly integrate with your existing SQL Server environments. The platform supports two primary methods, ensuring secure and efficient access to your databases:\n\n* **Standard SQL Server Authentication:** Uses a username and password.  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_07.png)  \n* **Windows Authentication (Integrated Security):** Leverages the Windows user's security credentials, often preferred in enterprise environments for seamless, secure integration with existing Windows security policies.  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_08.png)\n\n**Security Options:**\n\n* Trust Server Certificate (required if database uses trusted certificates)  \n* Encrypt Connection (required for encrypted connections)\n\n**Extractable Objects:**\n\n* Tables (structure and metadata)  \n* Views (definitions and dependencies)  \n* Functions (scalar and table-valued)  \n* Stored procedures (complete logic)\n\n### Amazon Redshift Extraction\n\n**Authentication Methods:**\n\n* IAM Provisioned Cluster  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_09.png)  \n* IAM Serverless  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_10.png)  \n* Standard authentication  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_11.png)\n\n**Extractable Objects:**\n\n* Tables  \n* Views  \n* Materialized views  \n* Stored procedures\n\n### Extraction Process![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_12.png)\n\n1. **Configure Connection:**  \n* Select authentication method  \n* Provide connection credentials  \n* Configure security settings if required  \n    \n2. **Connect to Database:**  \n* Click \"Connect to Database\"  \n* Wait for \"Connection established\" notification  \n* Browse available databases and schemas  \n    \n3. **Select Objects:**  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_13.png)  \n* Expand database and schema trees  \n* Select specific objects for extraction  \n* Review object dependencies  \n    \n4. **Execute Extraction:**  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_14.png)  \n* Click \"Extract Objects\"  \n* Monitor extraction progress  \n* Review extraction results  \n    \n5. **Validate Results:**  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_15.png)  \n* Click \"View Last Extraction Results\"  \n* Confirm all required objects extracted successfully  \n* Note any extraction failures or warnings\n\n```sql\n-- Example extracted object structure\n-- Original SQL Server procedure\nCREATE PROCEDURE GetCustomerOrders\n    @CustomerID INT\nAS\nBEGIN\n    SELECT OrderID, OrderDate, TotalAmount\n    FROM Orders\n    WHERE CustomerID = @CustomerID\nEND\n\n-- Will be prepared for conversion to Snowflake format\n```\n\n## Code Conversion and Assessment\n\n\n### Conversion Process Overview\n\nSnowConvert AI performs both assessment and conversion in a single unified process, generating comprehensive reports and converted code simultaneously.\n\n**Step 1: Conversion Settings**\n\n* **Encoding:** Select appropriate character encoding (UTF-8 default)  \n* **Custom Schema:** Specify target schema name if different from source  \n* **Database Name:** Set custom database name for converted objects  \n* **Target Language:** Choose SnowScript or JavaScript for procedures  \n* **Comments:** Enable commenting of nodes with missing dependencies\n\n**Step 2: Execute Conversion**\n\n1. Click \"Save & Start Assessment\"  \n2. Monitor conversion progress  \n3. Review completion status  \n4. Examine generated reports  \n   \n\n### Understanding Conversion Results\n\n**Code Completeness Metric:**  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_16.png)\n\n* Score below 100% indicates missing object references  \n* Review dependencies before deployment  \n* Address missing references in source code\n\n**Conversion Status Indicators:**\n\n* **Green:** Successfully converted, ready for deployment  \n* **Yellow:** Functional Data Model (FDM) warnings present  \n* **Red:** Equivalent Work Item (EWI) errors require attention\n\n**Assessment Reports:**\n\n* Conversion summary statistics  \n* Object-by-object conversion status  \n* Complexity analysis and recommendations  \n* Migration effort estimates\n\n```sql\n-- Example conversion result\n-- Original Teradata SQL\nSELECT \n    customer_id,\n    SUM(order_amount) as total_orders\nFROM orders\nWHERE order_date \u003E= ADD_MONTHS(CURRENT_DATE, -12)\nGROUP BY customer_id;\n\n-- Converted Snowflake SQL\nSELECT \n    customer_id,\n    SUM(order_amount) as total_orders\nFROM orders\nWHERE order_date \u003E= DATEADD(MONTH, -12, CURRENT_DATE)\nGROUP BY customer_id;\n```\n\n## AI Verification\n\n\n### Overview\n\nThe AI Verification step (currently in Public Preview) adds a layer of intelligence to migrations by using AI agents to automatically verify and resolve errors that occur after the deterministic conversion step (the Code Conversion step is executed by a transpiler). The AI fixes are grounded with tests over synthetically generated data. This improves accuracy, reduces manual effort, and makes migrations faster, more reliable, and more scalable. AI will attempt to execute the converted code and fix issues if it is not working.\n\n### Step-by-Step flow\n\nAfter the code conversion step customers can opt to use AI Verification to improve the quality of the converted code. If a customer is not allowed to use AI, this step can be skipped and continue to the Deployment of the converted objects ( as you can see in the image below, SKIP AI VERIFICATION button shows in the lower left corner of the screen). To continue with AI Verification we need to select which objects we want to verify.  Sometimes certain objects will be auto selected by SnowConvert AI because they are dependencies of the selected objects. So don't be scared if you select one procedure but the tool auto selects a few tables, views and even other procedures. This means all those objects are required to be able to execute the selected stored procedure.  \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_17.png)\n\nOnce objects are selected, the VERIFY CODE can be clicked, which will display a special screen with a set of disclaimers. It is important to read and understand these disclaimers before proceeding. The AI will be executed in the customer Snowflake account using a Cortex Complete function. The tool will use the same Snowflake connection that was used for Source Code Extraction and that will be used for Deployment and Data Migration.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_18.png)\n\nOnce the user accepts all of the disclaimers, they can proceed with the actual verification. This step may take a long time as AI needs to upload files to a Snowflake stage to complete the verification process. At the end of the process, you will get a result screen similar to the image below.  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_19.png)\n\nAt the top of your screen, you'll find a summary of the AI Results, followed by detailed information for each validated object. To gain a deeper understanding of how the AI reviewed a specific object, click \"SEE DETAILS\" for that object. This will display an explanation similar to the example shown in the following image.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_20.png)\n\nIt is important to understand that the migration engineer needs to review the AI results and merge them with the initial code conversion results.\n\n## SSIS Replatform\n\n\n### Overview\n\nSSIS Replatform is a specialized SnowConvert AI capability designed to convert SQL Server Integration Services (SSIS) `.dtsx` packages to Snowflake-native solutions. This feature transforms complex ETL workflows into modern data pipelines using Snowflake tasks, procedures, and dbt projects.\n\nFor optimal conversion results, ensure that your input includes valid `.dtsx` files and all dependent database objects (DDL scripts for tables, views, functions, and procedures referenced by the SSIS packages).\n\nThe replatform process separates SSIS packages into two components:\n\n1. **Control Flow Orchestration:** Converted to SQL scripts with Snowflake tasks and stored procedures.  \n2. **Data Flow Tasks:** Transformed into individual dbt projects for each data flow component.\n\n### Project Setup for SSIS Replatform\n\n1. Launch SnowConvert AI  \n2. Click \"Create New Project\"  \n3. **Important:** Select \"SQL Server\" as the source platform  \n4. In the extraction configuration section, select \"Replatform\" option  \n5. Enter the path to your SSIS `.dtsx` files\n\n### DTSX Conversion Process\n\n1. Proceed with the conversion process.  \n2. SnowConvert AI analyzes all `.dtsx` files and dependencies  \n3. Conversion proceeds following the standard SnowConvert AI workflow  \n4. Control flow tasks converted to Snowflake stored procedures and tasks  \n5. Data flow tasks converted to individual dbt projects\n\nThe converted ETL code will be available in the SnowConvert AI output folder under the `ETL` directory. An `etl_configuration` folder will also be created containing utilities and configuration files. Additionally, `ETL.Elements.NA.csv` and `ETL.Issues.NA.csv` files provide detailed information about the ETL conversion process and any issues encountered.\n\n### Output Structure\n\n```\noutput/\n└── ETL/\n    └── [same folder structure as input]/\n        └── [Package_Name]/\n            ├── Orchestration.sql\n            ├── Data Flow Task 1/\n            │   ├── dbt_project.yml\n            │   ├── models/\n            │   └── sources/\n            └── [Additional Data Flow Tasks]/\n```\n\n### Understanding Conversion Results\n\nReview the generated CSV files for detailed conversion information:\n\n* **ETL.Elements.NA.csv:** Details about converted ETL elements  \n* **ETL.Issues.NA.csv:** Issues encountered during conversion\n\n### Deployment Process\n\n1. Deploy Snowflake tasks and stored procedures  \n2. Deploy the dbt projects in your environment  \n3. Manually fix the failing components and validate model compilation and execution\n\n## Power BI Repointing\n\n\n### Overview\n\nPower BI repointing is a SnowConvert AI feature that allows users to redefine connections from different database sources to a Snowflake valid connector. Currently, the supported sources are:\n\n1. [SQL Server](https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/transact/etl-bi-repointing/power-bi-transact-repointing)  \n2. [Oracle](https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/oracle/etl-bi-repointing/power-bi-oracle-repointing)  \n3. [Teradata](https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/teradata/etl-bi-repointing/power-bi-teradata-repointing)  \n4. [Redshift](https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/redshift/etl-bi-repointing/power-bi-redshift-repointing)  \n5. [Azure Synapse](https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/transact/etl-bi-repointing/power-bi-transact-repointing)  \n6. [PostgreSQL](https://docs.snowflake.com/en/migrations/snowconvert-docs/translation-references/postgres/etl-bi-repointing/power-bi-postgres-repointing)\n\nIt is recommended that DDLs should be migrated with the Power BI repointing migration process, which would allow SnowConvert AI to identify objects such as tables and views.\n\n### Power BI Repointing Set Up\n\n1. Save your Power BI projects with .pbit extension.  \n2. Open SnowConvert AI.  \n3. Optionally, add your DDLs.  \n4. Select the source language that you are using in your Power BI reports, i.e. SQL Server.  \n5. Add your .pbit files on the Power BI repointing section.  \n6. Click on the “continue to conversion” button.\n\n### Power BI Repointing Results\n\n1. Open your repointed Power BI report.  \n2. Fill the Power BI parameters with your Snowflake account information: server link, warehouse name, and database name. (This is automatically added by SnowConvert AI).  \n3. Refresh your data in your reports.   \n4. Double-check results.  \n5. Save your repointed report with the format you need. i.e. .pbix. \n\n### Assessment\n\nTo review the overall status of the repointed reports, you can review the “ETLAndBiRepointing” assessment report. This report contains the report name and a summary of the changed connectors.\n\n## Deployment to Snowflake\n\n\n### Deployment Overview\n\nSnowConvert AI facilitates the direct deployment of converted code objects and schemas to your Snowflake environment. This process includes comprehensive dependency management and precise execution ordering, ensuring a smooth and accurate transition.\n\n### Authentication Methods\n\n**SSO Authentication:**\n\n* Integrates with the organization's Single Sign-On (SSO) provider for a seamless authentication experience.  \n* Supports enterprise identity management systems.\n\n**Standard Authentication:**\n\n* Username and password authentication  \n* Requires multi-factor authentication (MFA) enabled  \n* Account identifier format: `orgname-account-name`\n\n### Pre-Deployment Validation\n\n**Step 1: Review Conversion Status**\n\n* Examine status indicators for each object (e.g., successful, needs review).  \n* Review Converted Code: Examine the generated Snowflake SQL for key objects (e.g., procedures, functions) to confirm the logic is correctly translated before deployment.  \n* Resolve any EWI (Error with Impact) errors before deployment.  \n* Review FDM (Further Development Mandatory) warnings and decide if deployment is acceptable.  \n* Use \"Refresh Tree\" to update status after code changes  \n\n\u003E MANDATORY:\n\u003E Resolve any EWI (Error with Impact) errors before deployment. These errors represent logic that could not be reliably converted and will cause deployment failure or runtime errors if not corrected.\n\u003E \n\u003E **Handling EWI Errors:**   \n\u003E SnowConvert AI is designed to flag these high-impact errors, but cannot always fix them. To resolve an EWI, you must:  \n\u003E 1. **Examine the code snippet** in the SnowConvert AI interface that triggered the EWI or better yet in your preferred Source Code IDE. [Learn more](/en/developers/guides/end2endmigration/)  \n\u003E 2. **Manually modify the converted source code** to fix the issue..  \n\u003E 3. **Unit test** on the corrected file.  \n\u003E\n\u003E **Object Selection:**  \n\u003E * Select only successfully converted objects  \n\u003E * Consider deployment dependencies  \n\u003E * Group related objects for batch deployment\n\n**Step 2: Initiate Deployment**\n\n1. Select objects for deployment  \n2. Click \"Deploy\" button  \n3. Choose authentication method  \n4. Provide Snowflake credentials\n\n**Step 3: Authentication**\n\n```sql\n-- Example Snowflake connection details\nAccount: myorg-myaccount\nUsername: migration_user\nWarehouse: MIGRATION_WH\nDatabase: TARGET_DB\nSchema: PUBLIC\nRole: MIGRATION_ROLE\n```\n\n**Step 4: Execution Order** Deployment follows this automatic sequence:\n\n1. Databases (create container structure)  \n2. Schemas (organize object namespaces)  \n3. Tables (establish data structures)  \n4. Views (depend on table structures)  \n5. Functions (provide reusable logic)  \n6. Stored Procedures (may reference other objects)\n\n**Step 5: Monitor Progress**\n\n* Track deployment status for each object  \n* Review execution logs and results  \n* Address any deployment errors immediately\n\n### Deployment Results\n\n**Success Indicators:**\n\n* Object created successfully in Snowflake  \n* Proper dependency resolution  \n* Correct object permissions applied\n\n**Error Handling:**\n\n* Review detailed error messages  \n* Fix source code issues if needed  \n* Re-run conversion if necessary  \n* Retry deployment for failed objects\n\n```sql\n-- Example deployed object in Snowflake\nCREATE OR REPLACE PROCEDURE get_customer_orders(customer_id INTEGER)\nRETURNS STRING\nLANGUAGE SNOWSCRIPT\nAS\n$$\nBEGIN\n    LET result_cursor CURSOR FOR \n        SELECT order_id, order_date, total_amount\n        FROM orders\n        WHERE customer_id = :customer_id;\n    \n    RETURN 'Procedure executed successfully';\nEND;\n$$;\n```\n\n## Data Migration\n\n\n### Data Migration Overview\n\nSnowConvert AI provides automated data migration capabilities to transfer actual data from Teradata, Oracle, SQL Server, Amazon Redshift, Synapse, Sybase, BigQuery, Netezza, Postgres, Greenplum and Databricks SQL source tables to deployed Snowflake structures, with platform-specific optimization strategies.\n\nFor migrations from Databricks or another Spark platform, you’ll use [Snowpark Migration Accelerator](/en/data-cloud/snowpark/migration-accelerator/).\n\n### Amazon Redshift Data Migration\n\n**Migration Strategy:** Redshift data is migrated using S3 as an intermediary storage layer:\n\n1. Data unloaded from Redshift to PARQUET files in S3  \n2. Files copied directly from S3 to Snowflake tables  \n3. Temporary files cleaned up automatically\n\n**Prerequisites:**\n\n1. **S3 Bucket Configuration:**  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_21.png)\n     \n* Bucket in same region as Redshift cluster  \n* Empty bucket path (no existing files)  \n* Proper IAM permissions configured  \n    \n2. **IAM Role for Redshift:**\n\nThis JSON snippet defines an AWS Identity and Access Management (IAM) role specifically for Amazon Redshift, which is crucial for data migration, particularly when Redshift data is migrated using S3 as an intermediary storage layer.\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    {\n      \"Effect\": \"Allow\",\n      \"Action\": [\n        \"s3:PutObject\",\n        \"s3:GetObject\",\n        \"s3:GetBucketLocation\",\n        \"s3:ListBucket\"\n      ],\n      \"Resource\": [\n        \"arn:aws:s3:::your-bucket-name/*\",\n        \"arn:aws:s3:::your-bucket-name\"\n      ]\n    }\n  ]\n}\n```\n\n3. **IAM User for S3 Access:**\n\nThis JSON snippet defines an AWS Identity and Access Management (IAM) user policy for S3 Access. This policy outlines the permissions granted to an IAM user to interact with a specific Amazon S3 bucket.\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    {\n      \"Effect\": \"Allow\",\n      \"Action\": [\n        \"s3:PutObject\",\n        \"s3:GetObject\",\n        \"s3:GetObjectVersion\",\n        \"s3:DeleteObject\",\n        \"s3:DeleteObjectVersion\",\n        \"s3:ListBucket\",\n        \"s3:GetBucketLocation\"\n      ],\n      \"Resource\": [\n        \"arn:aws:s3:::your-bucket-name/*\",\n        \"arn:aws:s3:::your-bucket-name\"\n      ]\n    }\n  ]\n}\n```\n\n**Migration Process:**\n\nThe migration process outlines the essential steps for efficiently transferring data from Redshift to Snowflake, ensuring data integrity and performance.\n\n1. **Configure S3 Settings:**  \n     \n   - S3 Bucket URL (must end with \"/\")  \n   - IAM Role ARN for data unloading  \n   - Access Key ID and Secret Access Key\n\n   \n\n2. **Select Tables:**  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_22.png)  \n* Choose tables for data migration  \n* Review table sizes and complexity  \n* Consider migration batch sizing  \n    \n3. **Execute Migration:**\n\n* Monitor unload progress from Redshift  \n* Track S3 file creation and validation  \n* Observe copy operations to Snowflake  \n    \n4. **Validate Results:**  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_23.png)  \n* Compare row counts between source and target  \n* Verify data integrity and completeness  \n* Review migration timing and performance\n\n### SQL Server Data Migration\n\n**Migration Strategy:** Direct data transfer using optimized connection protocols:\n\n* Bulk data extraction from SQL Server  \n* Streaming transfer to Snowflake tables  \n* Real-time progress monitoring\n\n**Prerequisites:**\n\n* Valid SQL Server connection with read permissions  \n* Network connectivity to both systems  \n* Sufficient bandwidth for data transfer\n\n**Migration Process:**\n\n1. **Verify Connections:**  \n* Test source SQL Server connectivity  \n* Confirm Snowflake authentication  \n* Validate table structures match  \n    \n2. **Select Tables:**  \n* Choose tables for migration  \n* Review dependencies and foreign keys  \n* Plan migration order if needed\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_24.png)\n\n3. **Execute Transfer:**  \n* Start data migration process  \n* Monitor transfer progress and speed  \n* Handle any connection or timeout issues  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_25.png)\n\n\n4. **Validate Migration:**  \n* Compare row counts automatically  \n* Review data type conversions  \n* Verify constraint compliance  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_26.png)\n\n## Data Validation\n\n\n### Validation Overview\n\nSnowConvert AI provides comprehensive data validation to ensure migration accuracy through schema validation and metrics validation, giving you confidence in your migrated data.\n\n### Validation Prerequisites\n\n**Python Requirements:**\n\n- Python version ≥ 3.9 and \u003C 3.13  \n- Python available in system PATH  \n- Virtual environment support\n\n```shell\n# Verify Python installation\npython --version\n\n# Expected output format: Python 3.x.x\n```\n\n### Validation Modes\n\n**Schema Validations:** Verifies structural integrity of migrated tables:\n\n- Table names match exactly  \n- Column names preserved correctly  \n- Ordinal positions maintained  \n- Data types converted appropriately  \n- Character lengths preserved  \n- Numeric precision and scale maintained  \n- Row counts match between source and target\n\n**Metrics Validations:** Compares aggregate data metrics between source and target:\n\n- Minimum and maximum values  \n- Average calculations  \n- Null value counts  \n- Distinct value counts  \n- Standard deviation  \n- Variance calculations  \n- Custom business metrics\n\n### Validation Process\n\n**Prerequisites:** Data validation requires Python ≥ 3.10 or \u003C 3.13 to be installed and available in your PATH. To verify that Python is available in your PATH, run  `python --version` in your terminal (or Command Prompt on Windows). Download Python [here](https://www.python.org/downloads/). \n\n**Step 1: Environment Setup**\n\n1. Data migration completion required  \n2. Python environment validation  \n3. Virtual environment creation  \n4. Dependency installation\n\n**Step 2: Validation Execution**  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_27.png)\n\n1. Click \"Validate Data\" in the migration interface  \n2. System checks Python availability  \n3. Sets up isolated validation environment  \n4. Executes both schema and metrics validations\n\n**Step 3: Results Analysis** Validation results are categorized into three levels:\n\n| Category | Description | Action Required |\n| :---- | :---- | :---- |\n| **Pass** | Values match exactly | No action needed |\n| **Warning** | Minor differences (e.g., higher precision) | Reconcile data by: Applying transformation  Changing the ingestion process |\n| **Fail** | Values don't match | Investigation required |\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/how-to-accelerate-data-warehouse-migrations-with-snowconvert-ai/image_28.png)\n\n### Validation Reports\n\n**Summary Table:**\n\n* High-level validation status per table  \n* Quick identification of issues  \n* Overall migration success metrics\n\n**Detailed CSV Reports:**\n\n* Column-by-column validation results  \n* Specific discrepancy details  \n* Sharable with stakeholders  \n* Suitable for audit trails\n\n**Report Structure:**\n\n```\ntable_name,column_name,validation_type,source_value,target_value,status,notes\ncustomers,customer_id,row_count,10000,10000,PASS,Counts match\norders,order_total,avg,156.78,156.78,PASS,Averages match\nproducts,price,max,999.99,999.99,PASS,Maximum values match\n```\n\n## Troubleshooting\n\n\n### Common Installation Issues\n\n**Permission Problems:**\n\n- **macOS .config issues:** Ensure .config is a directory, not a file  \n- **Windows AppData access:** Verify Snowflake Inc folder permissions  \n- **Linux installation:** May require sudo for system-wide installation\n\n**Network Connectivity:**\n\n- **Firewall blocking updates:** Whitelist `sctoolsartifacts.blob.core.windows.net`  \n- **Access code generation:** Ensure access to `https://snowconvert.snowflake.com/licensing`  \n- **License validation:** Check connectivity to licensing APIs\n\n**Version Management:**\n\n- SnowConvert AI updates automatically when you launch it with an internet connection, but you can manually check for updates through the application's menu. If an update is available, the system will prompt you to download and install it. To verify the update is complete, you can check again in the menu to see the new version.   \n- If facing any issues during the update, please uninstall previous versions and relaunch.\n\n### Performance Optimization\n\n**Large Dataset Migration:**\n\n- Plan migrations during off-peak hours  \n- Use incremental migration strategies for very large tables  \n- Monitor system resources during migration  \n- Consider table partitioning for optimal performance\n\n**Resource Management:**\n\n- Ensure adequate memory (8GB+ recommended for large projects)  \n- Monitor disk space for temporary files  \n- Use SSD storage for better I/O performance  \n- Close unnecessary applications during migration\n\n### Security Considerations\n\n**Access Control:**\n\n- Use principle of least privilege for database connections  \n- Enable MFA on all Snowflake accounts  \n- Regularly rotate access codes and credentials  \n- Audit migration activities and access logs\n\n**Data Protection:**\n\n- Encrypt sensitive data during transit  \n- Use secure connection protocols (SSL/TLS)  \n- Implement proper backup strategies  \n- Maintain audit trails for compliance\n\n```sql\n-- Example security configuration\n-- Grant minimal required permissions\nGRANT USAGE ON WAREHOUSE migration_wh TO ROLE migration_role;\nGRANT CREATE TABLE ON SCHEMA target_db.public TO ROLE migration_role;\nGRANT INSERT, SELECT ON ALL TABLES IN SCHEMA target_db.public TO ROLE migration_role;\n```\n\n### Monitoring and Alerting\n\n**Migration Monitoring:**\n\n- Track conversion success rates by object type  \n- Monitor data migration progress and timing  \n- Set up alerts for migration failures  \n- Document migration performance baselines\n\n**Post-Migration Monitoring:**\n\n- Validate application connectivity to new environment  \n- Monitor query performance in Snowflake  \n- Track user adoption and satisfaction  \n- Plan for ongoing maintenance and optimization\n\n\u003E IMPORTANT:\n\u003E - Always test conversions in development environments first  \n\u003E - Maintain detailed migration documentation for future reference  \n\u003E - Plan rollback strategies for critical production migrations  \n\u003E - Engage Snowflake Professional Services for complex migrations\n\n## Best Practices\n\n\nAdhering to best practices is crucial for a successful and efficient migration to Snowflake. This section outlines key considerations across SSIS replatforming, general migration strategies, data validation, and code conversion, providing guidance to optimize performance, ensure data integrity, and streamline the overall process.\n\n### SSIS Replatform\n\n* Document SSIS package dependencies and custom components.  \n* Review `ETL.Elements.NA.csv` and `ETL.Issues.NA.csv` for conversion details.\n\n### Migration\n\n**Performance Optimization:**\n\n- Migrate large tables during off-peak hours  \n- Use parallel migration for multiple small tables  \n- Monitor network bandwidth utilization  \n- Consider table partitioning for very large datasets\n\n**Error Handling:**\n\n- Implement retry logic for transient failures  \n- Log detailed error information for troubleshooting  \n- Maintain rollback capabilities where possible  \n- Validate partial migrations before continuing\n\n**Data Integrity Checks:**\n\n```sql\n-- Example validation query\n-- Compare row counts between source and target\nSELECT \n    'Source Count' as source,\n    COUNT(*) as row_count \nFROM source_database.schema.table_name\nUNION ALL\nSELECT \n    'Target Count' as source,\n    COUNT(*) as row_count \nFROM target_database.schema.table_name;\n```\n\u003E NOTE:\n\u003E Should you require additional help resolving errors and ensuring data integrity, please email [snowconvert-info@snowflake.com](mailto:snowconvert-info@snowflake.com)\n\n### Validation\n\n**Pre-Validation Checklist:**\n\n* Ensure data stability during validation (no updates)  \n* Complete all migration steps before validation  \n* Have sufficient system resources available  \n* Plan validation during maintenance windows\n\n**Interpreting Results:**\n\n* Focus on FAIL status items first  \n* Investigate WARNING items for business impact  \n* Document any acceptable discrepancies  \n* Rerun validation after fixes\n\n**Common Issues and Solutions:**\n\n| Issue | Cause | Solution |\n| :---- | :---- | :---- |\n| Row count mismatch | Incomplete migration | Re-run data migration |\n| Precision differences | Data type conversion | Verify acceptable business impact |\n| Date format variations | Timezone or format changes | Standardize date handling |\n| Null handling differences | Platform-specific null behavior | Update conversion rules |\n\n```py\n# Example validation check (conceptual)\ndef validate_table_metrics(source_table, target_table):\n    validations = {\n        'row_count': compare_row_counts(source_table, target_table),\n        'column_stats': compare_column_statistics(source_table, target_table),\n        'data_types': validate_schema_compatibility(source_table, target_table)\n    }\n    return validations\n```\n\n### Conversion\n\n**Code Preparation:**\n\n- Clean up source code before conversion  \n- Remove commented-out legacy code  \n- Ensure consistent encoding across all files  \n- Document complex business logic\n\n**Project Organization:**\n\n- Use meaningful project names and descriptions  \n- Organize source code in logical folder structures  \n- Maintain backup copies of original source code  \n- Document custom conversion settings\n\n**Quality Assurance:**\n\n- Review conversion reports thoroughly  \n- Test converted code in development environment  \n- Address EWI errors before deployment  \n- Validate complex business logic manually\n\n## Conclusion And Resources\n\n\nThis guide has walked you through the complete lifecycle of data warehouse migration using SnowConvert AI, from initial setup and configuration to advanced topics like SSIS replatforming and AI verification. You are now equipped to confidently accelerate your migration projects, ensuring accuracy and efficiency as you transition to Snowflake's powerful platform.\n\n### What You Learned\n\n* **Installation and Setup:** Successfully installed SnowConvert AI across different operating systems  \n* **Access Management:** Obtained and managed access codes for tool activation and learned troubleshooting techniques for licensing issues  \n* **End-to-End Migration:** Executed complete database migrations including extraction, conversion, deployment, data migration, and validation  \n* **Best Practices:** Applied security, performance, and quality assurance practices for successful enterprise-grade migrations  \n* **Troubleshooting:** Identified and resolved common issues related to installation, conversion, and deployment processes\n\n### Related Resources\n\n**Articles:**\n\n* [SnowConvert AI for Conversion Training Course](https://training.snowflake.com/lmt/!clmsLink.dt?site=sf&region=us&lang=en-us&type=O&id=130596852) - Comprehensive hands-on training  \n* [What is Data Migration? Types, Strategy & Best Practices](/en/fundamentals/what-is-data-migration/) - Learn what data migration is, including types, strategies, best practices and data migration solutions for smooth data transfer and management.  \n* [Best Practices for Migrating Historical Data to Snowflake](/en/blog/best-practices-for-migrating-historical-data-to-snowflake/) - Best practices for extracting, transferring, uploading, and validating data when migrating to Snowflake.  \n* [9 Best Practices for Transitioning From On-Premises to Cloud with Snowflake](/en/blog/best-practices-transitioning-on-premises-to-cloud/) - This article provides nine best practices for a smooth and efficient transition from on-premises data systems to Snowflake's cloud data platform.\n\n**Documentation:**\n\n- [SnowConvert AI Documentation](https://docs.snowflake.com/en/migrations/snowconvert-docs/overview): Complete technical reference  \n- [SnowConvert AI Release Notes](https://docs.snowflake.com/en/migrations/snowconvert-docs/general/release-notes/release-notes/README): Recent release notes\n\n### Next Steps\n\n\nNow that you have successfully migrated your data warehouse to Snowflake using SnowConvert AI, consider these next steps to fully leverage the power of the Snowflake Data Cloud:\n\n* **Explore Advanced Snowflake Features:** Dive deeper into Snowflake's capabilities, such as advanced analytics with [Snowpark](/en/product/features/snowpark/), data sharing with other organizations, and building data applications directly on Snowflake.  \n* **Integrate with BI Tools:** Connect your newly migrated Snowflake data to your existing or new [Business Intelligence (BI) tools](https://docs.snowflake.com/en/user-guide/ecosystem-bi) (e.g., Power BI, Tableau, Looker) to create insightful dashboards and reports.  \n* **Optimize Performance and Cost:** Continuously monitor your Snowflake usage to [optimize performance and costs](https://docs.snowflake.com/en/guides-overview-performance).  \n* **Implement Data Governance and Security:** Establish robust [data governance](https://docs.snowflake.com/en/guides-overview-govern) policies and enhance security measures within Snowflake using features like row-access policies, column-level security, and data masking.  \n* **Automate Data Pipelines:** Transition to automated data ingestion and transformation pipelines using Snowflake features like [Openflow](/en/product/features/openflow/) and [Snowpipe](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro).  \n* **Leverage AI/ML Capabilities:** Integrate Snowflake with other cloud services and tools for [machine learning](https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview) and data science.   \n* **Application development:** [Native Apps](/en/product/features/native-apps/) and [Streamlit](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit) Apps for building interactive data apps.   \n* **Stay Updated with Snowflake Innovations:** Regularly check Snowflake's release notes, documentation, and community forums to stay informed about new features and best practices.","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-dba2587ed2","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-d54d24c612",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-f9df81e7c5","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-03-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-cd86954982","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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