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tutorial, we will call a \u003Cstrong\u003EREST API\u003C/strong\u003E to retrieve Formula 1 race results.\u003C/p\u003E\n","\u003Cp\u003EWhat is \u003Cstrong\u003EOpenflow\u003C/strong\u003E ?\nOpenflow is \u003Cstrong\u003ESnowflake’s new built-in ELT\u003C/strong\u003E (Extract, Load, Transform) tool. It allows you to visually design workflows that integrate with Snowflake, orchestrating data ingestion and transformation directly within your account.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA standalone Openflow workflow that interacts with your Snowflake account.\u003C/li\u003E\u003Cli\u003ERequired DDL objects that will be populated in the workflow (Stage, Tables).\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to build a complete ELT flow with Openflow without delay using components and SQL instructions.\u003C/li\u003E\u003Cli\u003EHow to fetch JSON data from an API, send it to a stage, and transform it into tables.\u003C/li\u003E\u003Cli\u003EHow to run a workflow manually or set up an automatic schedule.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn AWS account.\u003C/li\u003E\u003Cli\u003EOpenflow configured with:\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003EDeployment\u003C/strong\u003E is where your data flows execute.\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ERuntime\u003C/strong\u003E is where you build your flows.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExtract API Data\u003C/h2\u003E\n","\u003Cp\u003EWe will be working with race results linked to drivers, teams, and the circuits where the races took place.\u003C/p\u003E\n","\u003Cp\u003ELet's have a look at the data :\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/1_JSON.png\" alt=\"F1 race example image\"\u003E\u003C/p\u003E\n","\u003Cp\u003EHow to understand it ?\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample:\u003C/strong\u003E\u003Cbr\u003E\nIn 2025, the 14th race of the season was the Hungarian Grand Prix.\nIt took place at the Hungaroring circuit.\nAs for the results, the driver Lando Norris from the McLaren team won the Grand Prix, finishing in first position.\u003C/p\u003E\n","\u003Cp\u003EThe goal, for now, is to retrieve data through an API and store it in a stage.\u003C/p\u003E\n","\u003Cp\u003ETo do this, we'll need to navigate between OpenFLow (to manage our workflow) and a worksheet (to create the snowflake objects that will help us store and organize the data).\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create the Stage in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EOpen a Snowflake worksheet.\u003C/p\u003E\n","\u003Cp\u003EYou could set the default database and schema to the one on which the \u003Ccode\u003EOPENFLOW\u003C/code\u003E role has admin rights.\u003Cbr\u003E\nIn our case, it's \u003Ccode\u003EOPENFLOW.OPENFLOW_TEST\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EWe will then run the code that creates a stage to retrieve the file from the API :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE F1_API \n    DIRECTORY = ( ENABLE = true );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Launch Openflow\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ELaunch Openflow from Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/launch_openflow.png\" alt=\"Launch Openflow\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EConnect to Openflow.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/connect_to_openflow.png\" alt=\"Connect to Openflow\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to the \u003Cstrong\u003ERuntimes\u003C/strong\u003E tab next to Overview, and open the runtime dedicated to this tutorial.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/open_runtime.png\" alt=\"Open Runtime\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou should be on a white page like this one :\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/white_canvas.png\" alt=\"White Canvas\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 3: Fetch data from API\u003C/h3\u003E\n","\u003Cp\u003ETo build the workflow, we'll need Processors and Controller Services.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EProcessors\u003C/strong\u003E: The main components in Openflow for reading, transforming, and loading data.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EController Services\u003C/strong\u003E: Shared services that that will be used by processors in our case (e.g., Snowflake connection settings). We'll configure it once and reuse it whenever needed.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDrag and drop a processor from the toolbar. It's the first icon.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/toolbar.png\" alt=\"Toolbar\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESearch for \u003Ccode\u003EInvokeHTTP\u003C/code\u003E and click \u003Cstrong\u003EAdd\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/drag_and_drop_invokehttp.png\" alt=\"Drag and Drop InvokeHTTP\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETips:\u003C/strong\u003E\nYou could also find it by using tags. For example, typing "https" or "client".\nSnowflake processors are prefixed with a snowflake icon.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe yellow warning triangle means the processor is invalid, not runnable.\nThe status of your components is visible on the status bar under the toolbar.\nIn addition to invalid status, a processor could be running (green triangle), stopped (red square) or disabled (crossed-out lightning bolt).\u003C/p\u003E\n","\u003Cp\u003EWe need to configure the processor we just added :\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EDouble-click on it or Right-Click and select \u003Cstrong\u003EConfigure\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003ESettings\u003C/strong\u003E tab, you can \u003Cstrong\u003ERename\u003C/strong\u003E the processor to \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E for flow readability\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003EProperties\u003C/strong\u003E tab, set \u003Cstrong\u003EHTTP URL\u003C/strong\u003E to \u003Ccode\u003Ehttps://f1api.dev/api/current/last/race\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EThe only field we need to configure is the HTTP URL since the API doesn't require authentication.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/invokehttp_properties.png\" alt=\"InvokeHTTP Properties\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003ERelationships\u003C/strong\u003E tab, select \u003Cstrong\u003ETerminate for all except Response\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ERelationships determine what to do with the data output from a component. Since we’re calling an API, several relationship options are available.\nIn our case, we only want to keep the Response. We'll transfer it to another processor and ignore the rest.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/invokehttp_relationships.png\" alt=\"InvokeHTTP Relationships\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EApply\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003Escheduling\u003C/strong\u003E tab, you can define a scheduling strategy. That means, you can configure when the processor should be triggered, either using a fixed time interval or a CRON expression.\nHere, we'll keep the default configuration and run the processor manually.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/invokehttp_scheduling.png\" alt=\"InvokeHTTP Scheduling\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 4: Staging Processor\u003C/h3\u003E\n","\u003Cp\u003EThe component is now ready to call the API.\nThe next steps are to add a processor that will save the response into a snowflake stage and configure it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAdd a Staging processor and Link it to the \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E processor :\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EAdd a \u003Ccode\u003EPutSnowflakeInternalStageFile\u003C/code\u003E processor the same way we add the \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E processor\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELink it to \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E once the second processor is on the canvas\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EHover over the \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E processor until a blue arrow appears\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/PutSnowflakeInternalStageFile_drag.png\" alt=\"PutSnowflakeInternalStageFile drag\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick and drag a connection from \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E to the \u003Ccode\u003EPutSnowflakeInternalStageFile\u003C/code\u003E processor\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/PutSnowflakeInternalStageFile_drop.png\" alt=\"PutSnowflakeInternalStageFile drop\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA create connection window appears. Transfer the http response to the \u003Ccode\u003EPutSnowflakeInternalStageFile\u003C/code\u003E processor by selecting the \u003Cstrong\u003EResponse under relationship\u003C/strong\u003E and click \u003Cstrong\u003EAdd\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/PutSnowflakeInternalStageFile_http_connection.png\" alt=\"PutSnowflakeInternalStageFile http connection\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Two_processors_linked.png\" alt=\"Two processors linked\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfigure the Connection to Snowflake through a controller service :\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003EProperties\u003C/strong\u003E tab, Configure the \u003Cstrong\u003ESnowflake Connection Service\u003C/strong\u003E > Click on the three dots > \u003Cstrong\u003ECreate new service\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/snowflake_connection_service.png\" alt=\"snowflake connection service\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the Add Controller service window, search for \u003Cstrong\u003ESnowflakeConnectionService\u003C/strong\u003E and Add\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/add_controller_service.png\" alt=\"add controller service\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ETo remember, a Controller Service is a shared service that will provide information to processors. This way, we configure the connection to Snowflake once.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the three dots next to Snowflake Connection Service again but then choose \u003Cstrong\u003EGo to service\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is the Controller Services tab for this group of processors.\nSince we're working on the main canvas and not inside a process group (a named collection of processors), the controller services displayed here apply to the main canvas only.\u003C/p\u003E\n","\u003Cp\u003EYou can see the current context under Controller Services. In this case, we're working on the OpenFlow sheet.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Controller_Services_Overview.png\" alt=\"Controller Services Overview\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the three dots next to the service > Edit\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Eselect \u003Cstrong\u003EPassword\u003C/strong\u003E for the \u003Cstrong\u003EAuthentication Strategy\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFill in Account with your \u003Cstrong\u003EAccount Identifier\u003C/strong\u003E that you can find on your Snowflake Homepage > User Profile >Account > View account details > Account > Account Identifier\u003C/li\u003E\u003Cli\u003EFor User, Password > Openflow User & Password\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Edit_Controller_Service.png\" alt=\"Edit Controller Service\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFill in the following fields with information that Openflow have access to :\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Default_Openflow_Database_Access.png\" alt=\"Default Openflow Database Access\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EApply\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the three dots next to the service > \u003Cstrong\u003EEnable\u003C/strong\u003E to change the service state from \u003Cstrong\u003Edisabled\u003C/strong\u003E to \u003Cstrong\u003Eenable\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Enable_Controller_Service.png\" alt=\"Enable Controller Service\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EOn the right, under Referencing Components, you can see Processors and Controller services referencing to the Snowflake Connection\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder Scope > Select Service Only\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEnable\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Enable_Controller_Service_Bis.png\" alt=\"Enable Controller Service Bis\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on  \u003Cstrong\u003EBack to processor\u003C/strong\u003E on the upper left to finish the configuration.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfigure the rest of the staging processor :\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003ESettings\u003C/strong\u003E tab, you can \u003Cstrong\u003ERename\u003C/strong\u003E the processor to \u003Ccode\u003EPut_JSON_In_Internal_Stage\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003EProperties\u003C/strong\u003E tab, set :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENamed\u003C/strong\u003E : \u003Cstrong\u003EInternal Stage Type\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStage Property\u003C/strong\u003E : reuse the stage \u003Ccode\u003EF1_API\u003C/code\u003E we created in the first part\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFile Name\u003C/strong\u003E : we choose to prefix the current timestamp with \u003Ccode\u003ERACE\u003C/code\u003E to recognize our files in the stage :\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-java\"\u003ERACE_${now():toNumber():formatInstant("yyyy_MM_dd_HH_mm_ss", "CET")}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Put_JSON_In_Internal_Stage_Properties.png\" alt=\"Put_JSON_In_Internal_Stage Properties\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003ERelationships\u003C/strong\u003E tab, select \u003Cstrong\u003ETerminate\u003C/strong\u003E under \u003Cstrong\u003EFailure\u003C/strong\u003E and \u003Cstrong\u003ESuccess\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 5: Run and Check\u003C/h3\u003E\n","\u003Cp\u003EThe processors are now configured !\u003Cbr\u003E\nFor this tutorial, we recommend keeping the \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E processor in a Stopped state and triggering it manually when needed. The other processors should be set to Run.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERight-Click on the \u003Ccode\u003EPut_JSON_In_Internal_Stage\u003C/code\u003E component and select \u003Cstrong\u003EStart\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe are ready to trigger the workflow.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERight-Click on the \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E processor and select \u003Cstrong\u003ERun once\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESee how it works.\u003Cbr\u003E\nOne flowfile (piece of data) entered the \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E. It was processed, and the response was sent to the \u003Ccode\u003EPut_JSON_In_Internal_Stage\u003C/code\u003E processor.\u003Cbr\u003E\nThe whole operation completed in just a second once the code was compiled.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Run_Staging.png\" alt=\"Run Staging\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow let's check on snowsight if our file is in the stage.\u003C/p\u003E\n","\u003Cp\u003ERun this on your worksheet :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LIST the STAGE FILES to check if our race file is there\nLIST @OPENFLOW.OPENFLOW_TEST.F1_API pattern ='.*RACE.*';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Query_Stage.png\" alt=\"Query Stage\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen everithing works fine :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EGo back editing the last processor on Openflow.\u003C/li\u003E\u003Cli\u003EOn the \u003Cstrong\u003ERelationships\u003C/strong\u003E tab, unselect the box under \u003Cstrong\u003ESuccess\u003C/strong\u003E so it can be forwarded to the next processor.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad Data\u003C/h2\u003E\n","\u003Cp\u003EThe goal of this step is to transfer data from the internal stage into a Snowflake table called \u003Cstrong\u003ERAW_RACE\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create the Table and File Format\u003C/h3\u003E\n","\u003Cp\u003EThis table will include a VARIANT column, which is ideal for handling semi-structured formats like JSON.\nThe entire file will be stored in a single row.\u003C/p\u003E\n","\u003Cp\u003EIn addition, we'll also need to define a file_format so that the COPY INTO can correctly interpret JSON data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.RAW_RACE (\n    v VARIANT\n  , insert_date TIMESTAMP_LTZ\n);\n\nCREATE OR REPLACE FILE FORMAT json_format\n  TYPE = JSON\n  STRIP_OUTER_ARRAY = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Add the COPY INTO Processor\u003C/h3\u003E\n","\u003Cp\u003EOnce these objects are created in Snowflake, \u003Cstrong\u003Ego back to OpenFlow\u003C/strong\u003E and add a new processor to the canvas.\u003C/p\u003E\n","\u003Cp\u003EWe’ll use the \u003Cstrong\u003EExecuteSQLStatement\u003C/strong\u003E processor to run a \u003Cstrong\u003ECOPY INTO\u003C/strong\u003E command that loads the data from the stage into the RAW_RACE table.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EAdd an \u003Ccode\u003EExecuteSQLStatement\u003C/code\u003E processor to the canvas\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EConnect from \u003Ccode\u003EPut_JSON_In_Internal_Stage\u003C/code\u003E with \u003Cstrong\u003Esuccess\u003C/strong\u003E relationship > Add\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Add_ExecuteSQLStatement_Processor.png\" alt=\"Add ExecuteSQLStatement Processor\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003ESettings\u003C/strong\u003E tab, rename the \u003Ccode\u003EExecuteSQLStatement\u003C/code\u003E processor to \u003Ccode\u003ECopy_JSON_Into_Variant_Table\u003C/code\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003EProperties\u003C/strong\u003E tab :\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EReused the \u003Cstrong\u003ESnowflakeConnectionService\u003C/strong\u003E we created earlier as the value for \u003Cstrong\u003EConnection Pooling Service property\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn SQL, copy this :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECOPY INTO OPENFLOW.OPENFLOW_TEST.RAW_RACE (v, insert_date)\nFROM (\n    SELECT \n        PARSE_JSON($1),\n        CURRENT_TIMESTAMP(0)\n    FROM @OPENFLOW_TEST.F1_API\n)\nFILE_FORMAT = (FORMAT_NAME = 'json_format')\nPATTERN = '.*RACE.*';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EWe add the current_timestamp to have the time of the insert.\u003C/p\u003E\n","\u003Cp\u003EOption PATTERN will select every files that contains "RACE".\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/ExecuteSQLStatement_Properties.png\" alt=\"ExecuteSQLStatement Properties\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the \u003Cstrong\u003ERelationships\u003C/strong\u003E tab, Select \u003Cstrong\u003ETerminate\u003C/strong\u003E for \u003Cstrong\u003Efailure\u003C/strong\u003E and \u003Cstrong\u003Esuccess\u003C/strong\u003E and \u003Cstrong\u003Eapply\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 3: Run and Check\u003C/h3\u003E\n","\u003Cp\u003ETime to test !\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERight click on \u003Ccode\u003ECopy_JSON_Into_Variant_Table\u003C/code\u003E and select \u003Cstrong\u003EStart\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ERight click on \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E and select \u003Cstrong\u003ERun once\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EGo back to Snowsight and check whether the rows were successfully inserted after running the COPY INTO command.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see something like this :\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/RAW_RACE_Query.png\" alt=\"RAW_RACE Query\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen everithing works fine :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EGo back editing the last processor on Openflow.\u003C/li\u003E\u003Cli\u003EOn the \u003Cstrong\u003ERelationships\u003C/strong\u003E tab, unselect the box under \u003Cstrong\u003ESuccess\u003C/strong\u003E so it can be forwarded to the next processor.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAt this stage of the tutorial, we’ve successfully ingested JSON data from the API and stored it in a Snowflake table.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETransformation\u003C/h2\u003E\n","\u003Cp\u003ELast but not least, we'll add components to our flow to be able to split the JSON data stored in a variant into multiple tables.\u003C/p\u003E\n","\u003Cp\u003EBased on the JSON file content we explored on Step 1, we'll build a star model of this four tables :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ERace_Results\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EDrivers\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ETeams\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ECircuits\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe have three dimensions (drivers, teams, circuits) and a fact (race_results).\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Table Creation\u003C/h3\u003E\n","\u003Cp\u003ETo \u003Cstrong\u003Ecreate tables\u003C/strong\u003E and \u003Cstrong\u003Eidentify the fields\u003C/strong\u003E contained in the JSON file, we can run the following code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT REGEXP_REPLACE(f.path, '\\\\[[0-9]+\\\\]', '[]') AS "Path",\n  TYPEOF(f.value) AS "Type",\n  COUNT(*) AS "Count"\nFROM OPENFLOW.OPENFLOW_TEST.RAW_RACE,\nLATERAL FLATTEN(v, RECURSIVE=>true) f\nGROUP BY 1, 2 ORDER BY 1, 2;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBelow is an extract from the results:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/RAW_RACE_Fields.png\" alt=\"RAW_RACE fields\"\u003E\u003C/p\u003E\n","\u003Cp\u003EHere is a proposed model to build.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERun it in your worksheet\u003C/strong\u003E to create the associated tables :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- CIRCUIT\n\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.CIRCUITS (\n      circuit_Id string\n    , name string\n    , city string\n    , country string\n    , length string\n    , corners number\n    , first_Participation_Year number\n    , lap_Record string\n    , fastest_Lap_Driver_Id string\n    , fastest_Lap_Team_Id string\n    , fastest_Lap_Year number\n    , insert_date timestampltz(0)\n    , update_date timestampltz(0)\n);\n\n-- RACE_RESULTS\n\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.RACE_RESULTS (\n      race_Id string\n    , driver_Id string\n    , team_Id string\n    , circuit_Id string\n    , race_Name string\n    , race_Round integer\n    , race_Date date\n    , race_Time string\n    , driver_Race_Grid_Position string\n    , driver_Race_Final_Position string\n    , driver_Race_Points string\n    , driver_Race_Fast_Lap string\n    , driver_Race_Gap_With_Win_Time string\n    , insert_date timestampltz(0)\n    , update_date timestampltz(0)\n);\n\n-- DRIVERS\n\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.DRIVERS (\n      driver_Id string\n    , name string\n    , surname string\n    , shortName string\n    , nationality string\n    , birthday string\n    , number integer\n    , insert_date timestampltz(0)\n    , update_date timestampltz(0)\n);\n\n-- TEAMS\n\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.TEAMS (\n      team_Id string\n    , name string\n    , nationality string\n    , first_Appareance integer\n    , constructors_Championships integer\n    , drivers_Championships integer\n    , insert_date timestampltz(0)\n    , update_date timestampltz(0)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck in your database that the objects are created :\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Tables_Creation.png\" alt=\"Tables Creation\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 2: Workflow processors\u003C/h3\u003E\n","\u003Cp\u003EThen we'll add components in OpenFlow to manage the data in these tables.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EAdd four ExecuteSQLStatement processors\u003C/strong\u003E to the canvas\u003C/li\u003E\u003Cli\u003EConnect from \u003Ccode\u003ECopy_JSON_Into_Variant_Table\u003C/code\u003E to each new ExecuteSQLStatement processor with \u003Cstrong\u003Esuccess\u003C/strong\u003E as the relactionship > Add\u003C/li\u003E\u003Cli\u003ERight-click on each new ExecuteSQLStatement > Configure :\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder the \u003Cstrong\u003ESettings\u003C/strong\u003E tab, name each processor as follows (one at a time):\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EExtract_And_Transform_Race\u003C/li\u003E\u003Cli\u003EExtract_And_Transform_Drivers\u003C/li\u003E\u003Cli\u003EExtract_And_Transform_Teams\u003C/li\u003E\u003Cli\u003EExtract_And_Transform_Circuits\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003ERelationships\u003C/strong\u003E, Select \u003Cstrong\u003ETerminate\u003C/strong\u003E for both failure and \u003Cstrong\u003Esuccess\u003C/strong\u003E, as these are our final components\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Final_Tables_relationships.png\" alt=\"Final Tables relationships\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003Eproperties\u003C/strong\u003E :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReused the \u003Cstrong\u003ESnowflakeConnectionService\u003C/strong\u003E we created as the value for \u003Cstrong\u003EConnection Pooling Service property\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIn SQL, complete with the code provided below\u003C/strong\u003E > OK\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EApply\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EHere is the code you need to paste in the SQL value for the right appropriate target table :\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIt performs to an insert or update operation with the latest version of the data inserted into the RAW_RACE table .\u003C/p\u003E\n","\u003Cp\u003EThis way, the flow is autonomous, up to date ,and contains no duplicates.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ERACE_RESULTS :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EMERGE INTO OPENFLOW.OPENFLOW_TEST.RACE_RESULTS AS target\nUSING (\n    SELECT \n        v:races:raceId::string AS race_Id,\n        value:driver:driverId::string AS driver_Id,\n        value:team:teamId::string AS team_Id,\n        v:races:circuit:circuitId::string AS circuit_Id,\n        v:races:raceName::string AS race_Name,\n        v:races:round::integer AS race_Round,\n        v:races:date::date AS race_Date,\n        v:races:time::string AS race_Time,\n        value:grid::string AS driver_Race_Grid_Position,\n        value:position::string AS driver_Race_Final_Position,\n        value:points::number AS driver_Race_Points,\n        value:fastLap::string AS driver_Race_Fast_Lap,\n        value:time::string AS driver_Race_Gap_With_Win_Time,\n        insert_date\n    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,\n         LATERAL FLATTEN(input => r.V, path => 'races:results')\n    qualify row_number() over(partition by v:races:raceId::string, value:driver:driverId::string order by insert_date desc) = 1\n) AS source\nON target.race_Id = source.race_Id AND target.driver_Id = source.driver_Id\n\nWHEN MATCHED THEN\n    UPDATE SET\n        target.team_Id = source.team_Id,\n        target.circuit_Id = source.circuit_Id,\n        target.race_Name = source.race_Name,\n        target.race_Round = source.race_Round,\n        target.race_Date = source.race_Date,\n        target.race_Time = source.race_Time,\n        target.driver_Race_Grid_Position = source.driver_Race_Grid_Position,\n        target.driver_Race_Final_Position = source.driver_Race_Final_Position,\n        target.driver_Race_Points = source.driver_Race_Points,\n        target.driver_Race_Fast_Lap = source.driver_Race_Fast_Lap,\n        target.driver_Race_Gap_With_Win_Time = source.driver_Race_Gap_With_Win_Time,\n        target.update_date = current_timestamp\n\nWHEN NOT MATCHED THEN\n    INSERT (\n        race_Id,\n        driver_Id,\n        team_Id,\n        circuit_Id,\n        race_Name,\n        race_Round,\n        race_Date,\n        race_Time,\n        driver_Race_Grid_Position,\n        driver_Race_Final_Position,\n        driver_Race_Points,\n        driver_Race_Fast_Lap,\n        driver_Race_Gap_With_Win_Time,\n        insert_date\n    )\n    VALUES (\n        source.race_Id,\n        source.driver_Id,\n        source.team_Id,\n        source.circuit_Id,\n        source.race_Name,\n        source.race_Round,\n        source.race_Date,\n        source.race_Time,\n        source.driver_Race_Grid_Position,\n        source.driver_Race_Final_Position,\n        source.driver_Race_Points,\n        source.driver_Race_Fast_Lap,\n        source.driver_Race_Gap_With_Win_Time,\n        current_timestamp\n    );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDRIVERS :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EMERGE INTO OPENFLOW.OPENFLOW_TEST.DRIVERS AS target\nUSING (\n    SELECT \n        value:driver:driverId::string       AS driver_Id,\n        value:driver:name::string           AS name,\n        value:driver:surname::string        AS surname,\n        value:driver:shortName::string      AS shortName,\n        value:driver:nationality::string    AS nationality,\n        value:driver:birthday::string       AS birthday,\n        value:driver:number::number         AS number,\n        insert_date\n    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,\n         LATERAL FLATTEN(input => r.V, PATH => 'races:results')\n    QUALIFY ROW_NUMBER() OVER (PARTITION BY value:driver:driverId::string ORDER BY insert_date DESC) = 1\n) AS source\nON target.driver_Id = source.driver_Id\n\nWHEN MATCHED THEN\n    UPDATE SET\n        target.name         = source.name,\n        target.surname      = source.surname,\n        target.shortName   = source.shortName,\n        target.nationality  = source.nationality,\n        target.birthday     = source.birthday,\n        target.number       = source.number,\n        target.update_date  = CURRENT_TIMESTAMP\n\nWHEN NOT MATCHED THEN\n    INSERT (\n        driver_Id,\n        name,\n        surname,\n        shortName,\n        nationality,\n        birthday,\n        number,\n        insert_date\n    )\n    VALUES (\n        source.driver_Id,\n        source.name,\n        source.surname,\n        source.shortName,\n        source.nationality,\n        source.birthday,\n        source.number,\n        CURRENT_TIMESTAMP\n    );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETEAMS :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EMERGE INTO OPENFLOW.OPENFLOW_TEST.TEAMS AS target\nUSING (\n    SELECT \n        value:team:teamId::string                     AS team_id,\n        value:team:teamName::string                  AS name,\n        value:team:nationality::string               AS nationality,\n        value:team:firstAppareance::number           AS first_appareance,\n        value:team:constructorsChampionships::number AS constructors_championships,\n        value:team:driversChampionships::number      AS drivers_championships,\n        insert_date\n    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,\n         LATERAL FLATTEN(input => r.V, PATH => 'races:results')\n    QUALIFY ROW_NUMBER() OVER (\n        PARTITION BY value:team:teamId::string \n        ORDER BY insert_date DESC\n    ) = 1\n) AS source\nON target.team_id = source.team_id\n\nWHEN MATCHED THEN\n    UPDATE SET\n        target.name                      = source.name,\n        target.nationality               = source.nationality,\n        target.first_appareance          = source.first_appareance,\n        target.constructors_championships = source.constructors_championships,\n        target.drivers_championships     = source.drivers_championships,\n        target.update_date               = CURRENT_TIMESTAMP\n\nWHEN NOT MATCHED THEN\n    INSERT (\n        team_id,\n        name,\n        nationality,\n        first_appareance,\n        constructors_championships,\n        drivers_championships,\n        insert_date\n    )\n    VALUES (\n        source.team_id,\n        source.name,\n        source.nationality,\n        source.first_appareance,\n        source.constructors_championships,\n        source.drivers_championships,\n        CURRENT_TIMESTAMP\n    );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECIRCUITS :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EMERGE INTO OPENFLOW.OPENFLOW_TEST.CIRCUITS AS target\nUSING (\n    SELECT \n        V:races:circuit:circuitId::string                         AS circuit_Id,\n        V:races:circuit:circuitName::string                       AS name,\n        V:races:circuit:city::string                              AS city,\n        V:races:circuit:country::string                           AS country,\n        V:races:circuit:circuitLength::string                     AS length,\n        V:races:circuit:corners::number                           AS corners,\n        V:races:circuit:firstParticipationYear::number            AS first_Participation_Year,\n        V:races:circuit:lapRecord::string                         AS lap_Record,\n        V:races:circuit:fastestLapDriverId::string                AS fastest_Lap_Driver_Id,\n        V:races:circuit:fastestLapTeamId::string                  AS fastest_Lap_Team_Id,\n        V:races:circuit:fastestLapYear::number                    AS fastest_Lap_Year,\n        insert_date\n    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r\n    QUALIFY ROW_NUMBER() OVER (PARTITION BY V:races:circuit:circuitId::string ORDER BY insert_date DESC) = 1\n) AS source\nON target.circuit_Id = source.circuit_Id\n\nWHEN MATCHED THEN\n    UPDATE SET\n        target.name                      = source.name,\n        target.city                      = source.city,\n        target.country                   = source.country,\n        target.length                    = source.length,\n        target.corners                   = source.corners,\n        target.first_Participation_Year  = source.first_Participation_Year,\n        target.lap_Record                = source.lap_Record,\n        target.fastest_Lap_Driver_Id     = source.fastest_Lap_Driver_Id,\n        target.fastest_Lap_Team_Id       = source.fastest_Lap_Team_Id,\n        target.fastest_Lap_Year          = source.fastest_Lap_Year,\n        target.update_date               = CURRENT_TIMESTAMP\n\nWHEN NOT MATCHED THEN\n    INSERT (\n        circuit_Id,\n        name,\n        city,\n        country,\n        length,\n        corners,\n        first_Participation_Year,\n        lap_Record,\n        fastest_Lap_Driver_Id,\n        fastest_Lap_Team_Id,\n        fastest_Lap_Year,\n        insert_date\n    )\n    VALUES (\n        source.circuit_Id,\n        source.name,\n        source.city,\n        source.country,\n        source.length,\n        source.corners,\n        source.first_Participation_Year,\n        source.lap_Record,\n        source.fastest_Lap_Driver_Id,\n        source.fastest_Lap_Team_Id,\n        source.fastest_Lap_Year,\n        CURRENT_TIMESTAMP\n    );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Final_Tables_Properties.png\" alt=\"Final Tables Properties\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 3: Run and check\u003C/h3\u003E\n","\u003Cp\u003EOnce it's all set up, right-click on each of the four processors and set them to \u003Cstrong\u003EStart\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EYou should get something similar to that :\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/All_processor_view.png\" alt=\"All processor view\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's perform the final run. Right-Click on the \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E processor and select \u003Cstrong\u003ERun Once\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EGo back to your worksheet. We'll verify that the tables have been successfully populated.\u003C/p\u003E\n","\u003Cp\u003ECheck your databases and/or run this select :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check if the tables are filled\nselect * from OPENFLOW.OPENFLOW_TEST.CIRCUITS;\nselect * from OPENFLOW.OPENFLOW_TEST.RACE_RESULTS;\nselect * from OPENFLOW.OPENFLOW_TEST.DRIVERS;\nselect * from OPENFLOW.OPENFLOW_TEST.TEAMS;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScheduling\u003C/h2\u003E\n","\u003Cp\u003EUntil now, we have chosen to handle scheduling manually but there are other options.  The purpose of this is to retrieve race_results automatically, without any actions from us.\u003C/p\u003E\n","\u003Cp\u003EIn Openflow, if we right-click on the \u003Ccode\u003ECall_JSON_F1_API_Race\u003C/code\u003E processor, select configure, and then go to the scheduling tab, we should see two types of scheduling strategies :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETimer-driven\u003C/strong\u003E - Requires a number of seconds.\nFor example, if we choose 604 800 seconds, the processor will run every week from the moment the processor is started.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECRON-driven\u003C/strong\u003E - Can be more precise and is based on CRON expressions.\nRaces happen on Sundays, but not every Sunday. For instance, we could configure the schedule to 0 10 * * 1 if we want the processor to be triggered every Monday at 10 AM.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Scheduling_view.png\" alt=\"Scheduling view\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003ECongratulations ! You’ve successfully completed this quickstart on retrieving JSON data from an API and loading it into Snowflake.\nWe used Openflow, snowflake's ELT tool, to run an automated workflow that retrieves data from the latest F1 race every week through an F1 API.\nAfter calling the API endpoint, we staged the file in snowflake, loaded it in a table and finally transformed it into its final dimension and fact tables.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to build a complete ELT flow with Openflow without delay using components and SQL instructions\u003C/li\u003E\u003Cli\u003EHow to Fetch JSON data from an API, send it to a stage and transform it into tables\u003C/li\u003E\u003Cli\u003EHow to run a workflow manually or manage an automatic scheduling\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOpenflow Documentation\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-integration/openflow/about\"\u003EAbout Openflow\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-integration/openflow/setup-openflow\"\u003ESetting up Openflow\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-integration/openflow/processors/index\"\u003EList of Processors\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-integration/openflow/controllers/index\"\u003EList of Controller Services\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ca href=\"https://nifi.apache.org/docs/nifi-docs/html/user-guide.html\"\u003ENiFi Documentation\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://f1api.dev/fr/docs\"\u003EF1 API website\u003C/a\u003E 🏎️\u003C/p\u003E"],"description":"Automate ELT workflows with Snowflake Openflow for no-code data integration, transformation, and pipeline orchestration.","title":"Building an Automated ELT Workflow in Snowflake with Openflow",":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nThis guide will show you how to use **Openflow** to fetch external data into Snowflake.  \nIn this tutorial, we will call a **REST API** to retrieve Formula 1 race results.\n\nWhat is **Openflow** ?\nOpenflow is **Snowflake’s new built-in ELT** (Extract, Load, Transform) tool. It allows you to visually design workflows that integrate with Snowflake, orchestrating data ingestion and transformation directly within your account.\n\n### What You Will Build\n- A standalone Openflow workflow that interacts with your Snowflake account.\n- Required DDL objects that will be populated in the workflow (Stage, Tables).\n\n### What You Will Learn\n- How to build a complete ELT flow with Openflow without delay using components and SQL instructions.\n- How to fetch JSON data from an API, send it to a stage, and transform it into tables.\n- How to run a workflow manually or set up an automatic schedule.\n\n### Prerequisites\n- An AWS account.\n- Openflow configured with:\n  - A **Deployment** is where your data flows execute.\n  - A **Runtime** is where you build your flows.\n\n\u003C!-- ------------------------ --\u003E\n## Extract API Data\n\nWe will be working with race results linked to drivers, teams, and the circuits where the races took place.\n\nLet's have a look at the data :\n\n![F1 race example image](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/1_JSON.png)\n\nHow to understand it ?\n\n**Example:**  \nIn 2025, the 14th race of the season was the Hungarian Grand Prix.\nIt took place at the Hungaroring circuit.\nAs for the results, the driver Lando Norris from the McLaren team won the Grand Prix, finishing in first position.\n\nThe goal, for now, is to retrieve data through an API and store it in a stage.\n\nTo do this, we'll need to navigate between OpenFLow (to manage our workflow) and a worksheet (to create the snowflake objects that will help us store and organize the data).\n\n\n### Step 1: Create the Stage in Snowflake\n\nOpen a Snowflake worksheet.  \n\nYou could set the default database and schema to the one on which the `OPENFLOW` role has admin rights.  \nIn our case, it's `OPENFLOW.OPENFLOW_TEST`.\n\nWe will then run the code that creates a stage to retrieve the file from the API :\n\n```sql\nCREATE OR REPLACE STAGE F1_API \n    DIRECTORY = ( ENABLE = true );\n```\n\n\n### Step 2: Launch Openflow\n\n1. Launch Openflow from Snowflake.\n\n   ![Launch Openflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/launch_openflow.png)\n\n2. Connect to Openflow.\n\n   ![Connect to Openflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/connect_to_openflow.png)\n\n3. Navigate to the **Runtimes** tab next to Overview, and open the runtime dedicated to this tutorial.\n\n   ![Open Runtime](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/open_runtime.png)\n\nYou should be on a white page like this one :\n\n   ![White Canvas](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/white_canvas.png)\n\n\n### Step 3: Fetch data from API\n\nTo build the workflow, we'll need Processors and Controller Services.\n\n- **Processors**: The main components in Openflow for reading, transforming, and loading data.\n- **Controller Services**: Shared services that that will be used by processors in our case (e.g., Snowflake connection settings). We'll configure it once and reuse it whenever needed.\n\nDrag and drop a processor from the toolbar. It's the first icon.\n\n![Toolbar](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/toolbar.png)\n\nSearch for `InvokeHTTP` and click **Add**.\n\n![Drag and Drop InvokeHTTP](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/drag_and_drop_invokehttp.png)\n\n\u003E **Tips:**\n\u003E You could also find it by using tags. For example, typing \"https\" or \"client\".\n\u003E Snowflake processors are prefixed with a snowflake icon.\n\nThe yellow warning triangle means the processor is invalid, not runnable.\nThe status of your components is visible on the status bar under the toolbar.\nIn addition to invalid status, a processor could be running (green triangle), stopped (red square) or disabled (crossed-out lightning bolt).\n\nWe need to configure the processor we just added :\n\n1. Double-click on it or Right-Click and select **Configure**\n2. On the **Settings** tab, you can **Rename** the processor to `Call_JSON_F1_API_Race` for flow readability\n3. On the **Properties** tab, set **HTTP URL** to `https://f1api.dev/api/current/last/race`\n    \n    The only field we need to configure is the HTTP URL since the API doesn't require authentication.\n\n    ![InvokeHTTP Properties](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/invokehttp_properties.png)\n\n4. On the **Relationships** tab, select **Terminate for all except Response**\n\n    Relationships determine what to do with the data output from a component. Since we’re calling an API, several relationship options are available.\n    In our case, we only want to keep the Response. We'll transfer it to another processor and ignore the rest.\n\n    ![InvokeHTTP Relationships](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/invokehttp_relationships.png)\n\n5. Apply\n\nOn the **scheduling** tab, you can define a scheduling strategy. That means, you can configure when the processor should be triggered, either using a fixed time interval or a CRON expression.\nHere, we'll keep the default configuration and run the processor manually.\n\n![InvokeHTTP Scheduling](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/invokehttp_scheduling.png)\n\n\n### Step 4: Staging Processor\n\nThe component is now ready to call the API.\nThe next steps are to add a processor that will save the response into a snowflake stage and configure it.\n\n\n**Add a Staging processor and Link it to the `Call_JSON_F1_API_Race` processor :**\n\n1. Add a `PutSnowflakeInternalStageFile` processor the same way we add the `Call_JSON_F1_API_Race` processor\n\n2. Link it to `Call_JSON_F1_API_Race` once the second processor is on the canvas\n\n    1. Hover over the `Call_JSON_F1_API_Race` processor until a blue arrow appears\n    \n        ![PutSnowflakeInternalStageFile drag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/PutSnowflakeInternalStageFile_drag.png)\n    \n    2. Click and drag a connection from `Call_JSON_F1_API_Race` to the `PutSnowflakeInternalStageFile` processor\n    \n        ![PutSnowflakeInternalStageFile drop](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/PutSnowflakeInternalStageFile_drop.png)\n        \n    3. A create connection window appears. Transfer the http response to the `PutSnowflakeInternalStageFile` processor by selecting the **Response under relationship** and click **Add**\n    \n        ![PutSnowflakeInternalStageFile http connection](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/PutSnowflakeInternalStageFile_http_connection.png)  \n\n        ![Two processors linked](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Two_processors_linked.png)\n\n\n**Configure the Connection to Snowflake through a controller service :**\n\n1. On the **Properties** tab, Configure the **Snowflake Connection Service** \u003E Click on the three dots \u003E **Create new service**\n\n    ![snowflake connection service](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/snowflake_connection_service.png)\n\n2. On the Add Controller service window, search for **SnowflakeConnectionService** and Add\n\n    ![add controller service](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/add_controller_service.png)\n\n    \u003E To remember, a Controller Service is a shared service that will provide information to processors. This way, we configure the connection to Snowflake once.\n\n3. Select the three dots next to Snowflake Connection Service again but then choose **Go to service**\n  \n    This is the Controller Services tab for this group of processors.\n    Since we're working on the main canvas and not inside a process group (a named collection of processors), the controller services displayed here apply to the main canvas only.\n    \n    You can see the current context under Controller Services. In this case, we're working on the OpenFlow sheet.\n    \n    ![Controller Services Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Controller_Services_Overview.png)\n\n4. Select the three dots next to the service \u003E Edit\n    - select **Password** for the **Authentication Strategy**.\n    - Fill in Account with your **Account Identifier** that you can find on your Snowflake Homepage \u003E User Profile \u003EAccount \u003E View account details \u003E Account \u003E Account Identifier\n    - For User, Password \u003E Openflow User & Password\n\n    ![Edit Controller Service](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Edit_Controller_Service.png)\n\n    - Fill in the following fields with information that Openflow have access to :\n    \n    ![Default Openflow Database Access](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Default_Openflow_Database_Access.png)\n\n    - Apply\n\n5. Select the three dots next to the service \u003E **Enable** to change the service state from **disabled** to **enable**\n\n    ![Enable Controller Service](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Enable_Controller_Service.png)\n\n    \u003E On the right, under Referencing Components, you can see Processors and Controller services referencing to the Snowflake Connection\n  \n    1. Under Scope \u003E Select Service Only\n    2. Enable\n    \n        ![Enable Controller Service Bis](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Enable_Controller_Service_Bis.png)\n\n6. Click on  **Back to processor** on the upper left to finish the configuration.\n\n\n**Configure the rest of the staging processor :**\n\n1. On the **Settings** tab, you can **Rename** the processor to `Put_JSON_In_Internal_Stage`.\n\n2. On the **Properties** tab, set :\n    -  **Named** : **Internal Stage Type**\n    -  **Stage Property** : reuse the stage `F1_API` we created in the first part\n    -  **File Name** : we choose to prefix the current timestamp with `RACE` to recognize our files in the stage :\n    ```java\n    RACE_${now():toNumber():formatInstant(\"yyyy_MM_dd_HH_mm_ss\", \"CET\")}\n    ```\n\n    ![Put_JSON_In_Internal_Stage Properties](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Put_JSON_In_Internal_Stage_Properties.png)\n\n3. On the **Relationships** tab, select **Terminate** under **Failure** and **Success**.\n\n### Step 5: Run and Check\n\nThe processors are now configured !  \nFor this tutorial, we recommend keeping the `Call_JSON_F1_API_Race` processor in a Stopped state and triggering it manually when needed. The other processors should be set to Run.\n\n- Right-Click on the `Put_JSON_In_Internal_Stage` component and select **Start**.\n\nWe are ready to trigger the workflow.  \n\n- Right-Click on the `Call_JSON_F1_API_Race` processor and select **Run once**.\n\nSee how it works.  \nOne flowfile (piece of data) entered the `Call_JSON_F1_API_Race`. It was processed, and the response was sent to the `Put_JSON_In_Internal_Stage` processor.  \nThe whole operation completed in just a second once the code was compiled.\n\n![Run Staging](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Run_Staging.png)\n\nNow let's check on snowsight if our file is in the stage.\n\nRun this on your worksheet :\n\n```sql\n-- LIST the STAGE FILES to check if our race file is there\nLIST @OPENFLOW.OPENFLOW_TEST.F1_API pattern ='.*RACE.*';\n```\n![Query Stage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Query_Stage.png)\n\nWhen everithing works fine :  \n- Go back editing the last processor on Openflow.  \n- On the **Relationships** tab, unselect the box under **Success** so it can be forwarded to the next processor.\n\n\u003C!-- ------------------------ --\u003E\n## Load Data\n\nThe goal of this step is to transfer data from the internal stage into a Snowflake table called **RAW_RACE**.\n\n\n### Step 1: Create the Table and File Format\n\nThis table will include a VARIANT column, which is ideal for handling semi-structured formats like JSON.\nThe entire file will be stored in a single row.\n\nIn addition, we'll also need to define a file_format so that the COPY INTO can correctly interpret JSON data.\n\n```sql\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.RAW_RACE (\n    v VARIANT\n  , insert_date TIMESTAMP_LTZ\n);\n\nCREATE OR REPLACE FILE FORMAT json_format\n  TYPE = JSON\n  STRIP_OUTER_ARRAY = TRUE;\n```\n\n\n### Step 2: Add the COPY INTO Processor\n\nOnce these objects are created in Snowflake, **go back to OpenFlow** and add a new processor to the canvas.\n\nWe’ll use the **ExecuteSQLStatement** processor to run a **COPY INTO** command that loads the data from the stage into the RAW_RACE table.\n\n1. Add an `ExecuteSQLStatement` processor to the canvas\n\n2. Connect from `Put_JSON_In_Internal_Stage` with **success** relationship \u003E Add\n\n    ![Add ExecuteSQLStatement Processor](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Add_ExecuteSQLStatement_Processor.png)\n\n3. On the **Settings** tab, rename the `ExecuteSQLStatement` processor to `Copy_JSON_Into_Variant_Table`\n\n4. On the **Properties** tab :\n    1. Reused the **SnowflakeConnectionService** we created earlier as the value for **Connection Pooling Service property**\n  \n    2. In SQL, copy this :\n    \n        ```sql\n        COPY INTO OPENFLOW.OPENFLOW_TEST.RAW_RACE (v, insert_date)\n        FROM (\n            SELECT \n                PARSE_JSON($1),\n                CURRENT_TIMESTAMP(0)\n            FROM @OPENFLOW_TEST.F1_API\n        )\n        FILE_FORMAT = (FORMAT_NAME = 'json_format')\n        PATTERN = '.*RACE.*';\n        ```\n  \n        \u003E We add the current_timestamp to have the time of the insert.\n        \u003E\n        \u003E Option PATTERN will select every files that contains \"RACE\".\n\n        ![ExecuteSQLStatement Properties](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/ExecuteSQLStatement_Properties.png)\n\n5. On the **Relationships** tab, Select **Terminate** for **failure** and **success** and **apply**\n\n### Step 3: Run and Check\n\nTime to test !\n\n- Right click on `Copy_JSON_Into_Variant_Table` and select **Start**.\n- Right click on `Call_JSON_F1_API_Race` and select **Run once**.\n\nGo back to Snowsight and check whether the rows were successfully inserted after running the COPY INTO command.\n\n```sql\nSELECT * FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE;\n```\n\nYou should see something like this :\n\n![RAW_RACE Query](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/RAW_RACE_Query.png)\n\nWhen everithing works fine :  \n- Go back editing the last processor on Openflow.  \n- On the **Relationships** tab, unselect the box under **Success** so it can be forwarded to the next processor.\n\nAt this stage of the tutorial, we’ve successfully ingested JSON data from the API and stored it in a Snowflake table.\n\n\u003C!-- ------------------------ --\u003E\n## Transformation\n\nLast but not least, we'll add components to our flow to be able to split the JSON data stored in a variant into multiple tables.\n\nBased on the JSON file content we explored on Step 1, we'll build a star model of this four tables :\n  - `Race_Results`\n  - `Drivers`\n  - `Teams`\n  - `Circuits`\n\nWe have three dimensions (drivers, teams, circuits) and a fact (race_results).\n\n\n### Step 1: Table Creation\n\nTo **create tables** and **identify the fields** contained in the JSON file, we can run the following code:\n\n```sql\nSELECT REGEXP_REPLACE(f.path, '\\\\[[0-9]+\\\\]', '[]') AS \"Path\",\n  TYPEOF(f.value) AS \"Type\",\n  COUNT(*) AS \"Count\"\nFROM OPENFLOW.OPENFLOW_TEST.RAW_RACE,\nLATERAL FLATTEN(v, RECURSIVE=\u003Etrue) f\nGROUP BY 1, 2 ORDER BY 1, 2;\n```\n\nBelow is an extract from the results:\n\n![RAW_RACE fields](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/RAW_RACE_Fields.png)\n\nHere is a proposed model to build.\n\n**Run it in your worksheet** to create the associated tables :\n\n```sql\n-- CIRCUIT\n\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.CIRCUITS (\n      circuit_Id string\n    , name string\n    , city string\n    , country string\n    , length string\n    , corners number\n    , first_Participation_Year number\n    , lap_Record string\n    , fastest_Lap_Driver_Id string\n    , fastest_Lap_Team_Id string\n    , fastest_Lap_Year number\n    , insert_date timestampltz(0)\n    , update_date timestampltz(0)\n);\n\n-- RACE_RESULTS\n\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.RACE_RESULTS (\n      race_Id string\n    , driver_Id string\n    , team_Id string\n    , circuit_Id string\n    , race_Name string\n    , race_Round integer\n    , race_Date date\n    , race_Time string\n    , driver_Race_Grid_Position string\n    , driver_Race_Final_Position string\n    , driver_Race_Points string\n    , driver_Race_Fast_Lap string\n    , driver_Race_Gap_With_Win_Time string\n    , insert_date timestampltz(0)\n    , update_date timestampltz(0)\n);\n\n-- DRIVERS\n\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.DRIVERS (\n      driver_Id string\n    , name string\n    , surname string\n    , shortName string\n    , nationality string\n    , birthday string\n    , number integer\n    , insert_date timestampltz(0)\n    , update_date timestampltz(0)\n);\n\n-- TEAMS\n\nCREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.TEAMS (\n      team_Id string\n    , name string\n    , nationality string\n    , first_Appareance integer\n    , constructors_Championships integer\n    , drivers_Championships integer\n    , insert_date timestampltz(0)\n    , update_date timestampltz(0)\n);\n```\n\nCheck in your database that the objects are created :\n\n![Tables Creation](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Tables_Creation.png)\n\n\n### Step 2: Workflow processors\n\nThen we'll add components in OpenFlow to manage the data in these tables.\n\n1. **Add four ExecuteSQLStatement processors** to the canvas\n2. Connect from `Copy_JSON_Into_Variant_Table` to each new ExecuteSQLStatement processor with **success** as the relactionship \u003E Add\n3. Right-click on each new ExecuteSQLStatement \u003E Configure :\n    1. Under the **Settings** tab, name each processor as follows (one at a time):\n        - Extract_And_Transform_Race\n        - Extract_And_Transform_Drivers\n        - Extract_And_Transform_Teams\n        - Extract_And_Transform_Circuits\n    2. Under **Relationships**, Select **Terminate** for both failure and **success**, as these are our final components\n    \n        ![Final Tables relationships](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Final_Tables_relationships.png)\n    \n    3. Under **properties** :\n        - Reused the **SnowflakeConnectionService** we created as the value for **Connection Pooling Service property**\n        - **In SQL, complete with the code provided below** \u003E OK\n    4. Apply\n\nHere is the code you need to paste in the SQL value for the right appropriate target table :\n\n  \u003E\n  \u003E It performs to an insert or update operation with the latest version of the data inserted into the RAW_RACE table .\n  \u003E \n  \u003E This way, the flow is autonomous, up to date ,and contains no duplicates.\n  \u003E\n\nRACE_RESULTS :\n\n```sql\nMERGE INTO OPENFLOW.OPENFLOW_TEST.RACE_RESULTS AS target\nUSING (\n    SELECT \n        v:races:raceId::string AS race_Id,\n        value:driver:driverId::string AS driver_Id,\n        value:team:teamId::string AS team_Id,\n        v:races:circuit:circuitId::string AS circuit_Id,\n        v:races:raceName::string AS race_Name,\n        v:races:round::integer AS race_Round,\n        v:races:date::date AS race_Date,\n        v:races:time::string AS race_Time,\n        value:grid::string AS driver_Race_Grid_Position,\n        value:position::string AS driver_Race_Final_Position,\n        value:points::number AS driver_Race_Points,\n        value:fastLap::string AS driver_Race_Fast_Lap,\n        value:time::string AS driver_Race_Gap_With_Win_Time,\n        insert_date\n    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,\n         LATERAL FLATTEN(input =\u003E r.V, path =\u003E 'races:results')\n    qualify row_number() over(partition by v:races:raceId::string, value:driver:driverId::string order by insert_date desc) = 1\n) AS source\nON target.race_Id = source.race_Id AND target.driver_Id = source.driver_Id\n\nWHEN MATCHED THEN\n    UPDATE SET\n        target.team_Id = source.team_Id,\n        target.circuit_Id = source.circuit_Id,\n        target.race_Name = source.race_Name,\n        target.race_Round = source.race_Round,\n        target.race_Date = source.race_Date,\n        target.race_Time = source.race_Time,\n        target.driver_Race_Grid_Position = source.driver_Race_Grid_Position,\n        target.driver_Race_Final_Position = source.driver_Race_Final_Position,\n        target.driver_Race_Points = source.driver_Race_Points,\n        target.driver_Race_Fast_Lap = source.driver_Race_Fast_Lap,\n        target.driver_Race_Gap_With_Win_Time = source.driver_Race_Gap_With_Win_Time,\n        target.update_date = current_timestamp\n\nWHEN NOT MATCHED THEN\n    INSERT (\n        race_Id,\n        driver_Id,\n        team_Id,\n        circuit_Id,\n        race_Name,\n        race_Round,\n        race_Date,\n        race_Time,\n        driver_Race_Grid_Position,\n        driver_Race_Final_Position,\n        driver_Race_Points,\n        driver_Race_Fast_Lap,\n        driver_Race_Gap_With_Win_Time,\n        insert_date\n    )\n    VALUES (\n        source.race_Id,\n        source.driver_Id,\n        source.team_Id,\n        source.circuit_Id,\n        source.race_Name,\n        source.race_Round,\n        source.race_Date,\n        source.race_Time,\n        source.driver_Race_Grid_Position,\n        source.driver_Race_Final_Position,\n        source.driver_Race_Points,\n        source.driver_Race_Fast_Lap,\n        source.driver_Race_Gap_With_Win_Time,\n        current_timestamp\n    );\n```\n\nDRIVERS :\n\n```sql\nMERGE INTO OPENFLOW.OPENFLOW_TEST.DRIVERS AS target\nUSING (\n    SELECT \n        value:driver:driverId::string       AS driver_Id,\n        value:driver:name::string           AS name,\n        value:driver:surname::string        AS surname,\n        value:driver:shortName::string      AS shortName,\n        value:driver:nationality::string    AS nationality,\n        value:driver:birthday::string       AS birthday,\n        value:driver:number::number         AS number,\n        insert_date\n    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,\n         LATERAL FLATTEN(input =\u003E r.V, PATH =\u003E 'races:results')\n    QUALIFY ROW_NUMBER() OVER (PARTITION BY value:driver:driverId::string ORDER BY insert_date DESC) = 1\n) AS source\nON target.driver_Id = source.driver_Id\n\nWHEN MATCHED THEN\n    UPDATE SET\n        target.name         = source.name,\n        target.surname      = source.surname,\n        target.shortName   = source.shortName,\n        target.nationality  = source.nationality,\n        target.birthday     = source.birthday,\n        target.number       = source.number,\n        target.update_date  = CURRENT_TIMESTAMP\n\nWHEN NOT MATCHED THEN\n    INSERT (\n        driver_Id,\n        name,\n        surname,\n        shortName,\n        nationality,\n        birthday,\n        number,\n        insert_date\n    )\n    VALUES (\n        source.driver_Id,\n        source.name,\n        source.surname,\n        source.shortName,\n        source.nationality,\n        source.birthday,\n        source.number,\n        CURRENT_TIMESTAMP\n    );\n```\n\nTEAMS :\n\n```sql\nMERGE INTO OPENFLOW.OPENFLOW_TEST.TEAMS AS target\nUSING (\n    SELECT \n        value:team:teamId::string                     AS team_id,\n        value:team:teamName::string                  AS name,\n        value:team:nationality::string               AS nationality,\n        value:team:firstAppareance::number           AS first_appareance,\n        value:team:constructorsChampionships::number AS constructors_championships,\n        value:team:driversChampionships::number      AS drivers_championships,\n        insert_date\n    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,\n         LATERAL FLATTEN(input =\u003E r.V, PATH =\u003E 'races:results')\n    QUALIFY ROW_NUMBER() OVER (\n        PARTITION BY value:team:teamId::string \n        ORDER BY insert_date DESC\n    ) = 1\n) AS source\nON target.team_id = source.team_id\n\nWHEN MATCHED THEN\n    UPDATE SET\n        target.name                      = source.name,\n        target.nationality               = source.nationality,\n        target.first_appareance          = source.first_appareance,\n        target.constructors_championships = source.constructors_championships,\n        target.drivers_championships     = source.drivers_championships,\n        target.update_date               = CURRENT_TIMESTAMP\n\nWHEN NOT MATCHED THEN\n    INSERT (\n        team_id,\n        name,\n        nationality,\n        first_appareance,\n        constructors_championships,\n        drivers_championships,\n        insert_date\n    )\n    VALUES (\n        source.team_id,\n        source.name,\n        source.nationality,\n        source.first_appareance,\n        source.constructors_championships,\n        source.drivers_championships,\n        CURRENT_TIMESTAMP\n    );\n```\n\nCIRCUITS :\n\n```sql\nMERGE INTO OPENFLOW.OPENFLOW_TEST.CIRCUITS AS target\nUSING (\n    SELECT \n        V:races:circuit:circuitId::string                         AS circuit_Id,\n        V:races:circuit:circuitName::string                       AS name,\n        V:races:circuit:city::string                              AS city,\n        V:races:circuit:country::string                           AS country,\n        V:races:circuit:circuitLength::string                     AS length,\n        V:races:circuit:corners::number                           AS corners,\n        V:races:circuit:firstParticipationYear::number            AS first_Participation_Year,\n        V:races:circuit:lapRecord::string                         AS lap_Record,\n        V:races:circuit:fastestLapDriverId::string                AS fastest_Lap_Driver_Id,\n        V:races:circuit:fastestLapTeamId::string                  AS fastest_Lap_Team_Id,\n        V:races:circuit:fastestLapYear::number                    AS fastest_Lap_Year,\n        insert_date\n    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r\n    QUALIFY ROW_NUMBER() OVER (PARTITION BY V:races:circuit:circuitId::string ORDER BY insert_date DESC) = 1\n) AS source\nON target.circuit_Id = source.circuit_Id\n\nWHEN MATCHED THEN\n    UPDATE SET\n        target.name                      = source.name,\n        target.city                      = source.city,\n        target.country                   = source.country,\n        target.length                    = source.length,\n        target.corners                   = source.corners,\n        target.first_Participation_Year  = source.first_Participation_Year,\n        target.lap_Record                = source.lap_Record,\n        target.fastest_Lap_Driver_Id     = source.fastest_Lap_Driver_Id,\n        target.fastest_Lap_Team_Id       = source.fastest_Lap_Team_Id,\n        target.fastest_Lap_Year          = source.fastest_Lap_Year,\n        target.update_date               = CURRENT_TIMESTAMP\n\nWHEN NOT MATCHED THEN\n    INSERT (\n        circuit_Id,\n        name,\n        city,\n        country,\n        length,\n        corners,\n        first_Participation_Year,\n        lap_Record,\n        fastest_Lap_Driver_Id,\n        fastest_Lap_Team_Id,\n        fastest_Lap_Year,\n        insert_date\n    )\n    VALUES (\n        source.circuit_Id,\n        source.name,\n        source.city,\n        source.country,\n        source.length,\n        source.corners,\n        source.first_Participation_Year,\n        source.lap_Record,\n        source.fastest_Lap_Driver_Id,\n        source.fastest_Lap_Team_Id,\n        source.fastest_Lap_Year,\n        CURRENT_TIMESTAMP\n    );\n```\n\n![Final Tables Properties](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Final_Tables_Properties.png)\n\n\n### Step 3: Run and check\n\nOnce it's all set up, right-click on each of the four processors and set them to **Start**.\n\nYou should get something similar to that :\n\n![All processor view](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/All_processor_view.png)\n\nLet's perform the final run. Right-Click on the `Call_JSON_F1_API_Race` processor and select **Run Once**.\n\nGo back to your worksheet. We'll verify that the tables have been successfully populated.\n\nCheck your databases and/or run this select :\n\n```sql\n-- Check if the tables are filled\nselect * from OPENFLOW.OPENFLOW_TEST.CIRCUITS;\nselect * from OPENFLOW.OPENFLOW_TEST.RACE_RESULTS;\nselect * from OPENFLOW.OPENFLOW_TEST.DRIVERS;\nselect * from OPENFLOW.OPENFLOW_TEST.TEAMS;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Scheduling\n\nUntil now, we have chosen to handle scheduling manually but there are other options.  The purpose of this is to retrieve race_results automatically, without any actions from us.\n\nIn Openflow, if we right-click on the `Call_JSON_F1_API_Race` processor, select configure, and then go to the scheduling tab, we should see two types of scheduling strategies :\n  - **Timer-driven** - Requires a number of seconds.\n    For example, if we choose 604 800 seconds, the processor will run every week from the moment the processor is started.\n  \n  - **CRON-driven** - Can be more precise and is based on CRON expressions.\n    Races happen on Sundays, but not every Sunday. For instance, we could configure the schedule to 0 10 * * 1 if we want the processor to be triggered every Monday at 10 AM.\n\n![Scheduling view](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/building-an-automated-elt-workflow-on-snowflake-with-openflow/Scheduling_view.png)\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\n### Conclusion\n\nCongratulations ! You’ve successfully completed this quickstart on retrieving JSON data from an API and loading it into Snowflake.\nWe used Openflow, snowflake's ELT tool, to run an automated workflow that retrieves data from the latest F1 race every week through an F1 API.\nAfter calling the API endpoint, we staged the file in snowflake, loaded it in a table and finally transformed it into its final dimension and fact tables.\n\n### What You Learned\n\n  - How to build a complete ELT flow with Openflow without delay using components and SQL instructions\n  - How to Fetch JSON data from an API, send it to a stage and transform it into tables\n  - How to run a workflow manually or manage an automatic scheduling\n\n### Resources\n\n**Openflow Documentation**:\n  - [About Openflow](https://docs.snowflake.com/en/user-guide/data-integration/openflow/about)\n  - [Setting up Openflow](https://docs.snowflake.com/en/user-guide/data-integration/openflow/setup-openflow)\n  - [List of Processors](https://docs.snowflake.com/en/user-guide/data-integration/openflow/processors/index)\n  - [List of Controller Services](https://docs.snowflake.com/en/user-guide/data-integration/openflow/controllers/index)\n\n[NiFi Documentation](https://nifi.apache.org/docs/nifi-docs/html/user-guide.html)\n\n[F1 API website](https://f1api.dev/fr/docs) 🏎️\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-87efe25264","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-52c26782fc",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-f3859118f5","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-7d93d9852b","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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