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+authors: Jonathan Leclavier
+id: Building_An_Automated_ELT_Workflow_On_Snowflake_With_Openflow
+summary: Build an automated ELT workflow on Snowflake with Openflow
+categories: getting-started,data-engineering,partner-integrations,demos
+environments: web
+status: Published 
+feedback link: https://github.com/Snowflake-Labs/sfguides/issues
+tags: Getting Started, Data Engineering, Openflow, Data, ELT
+
+# Building an Automated ELT Workflow in Snowflake with Openflow
+<!-- ------------------------ -->
+## Overview
+
+This guide will show you how to use **Openflow** to fetch external data into Snowflake.  
+In this tutorial, we will call a **REST API** to retrieve Formula 1 race results.
+
+What is **Openflow** ?
+Openflow 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.
+
+### What You Will Build
+- A standalone Openflow workflow that interacts with your Snowflake account.
+- Required DDL objects that will be populated in the workflow (Stage, Tables).
+
+### What You Will Learn
+- How to build a complete ELT flow with Openflow without delay using components and SQL instructions.
+- How to fetch JSON data from an API, send it to a stage, and transform it into tables.
+- How to run a workflow manually or set up an automatic schedule.
+
+### Prerequisites
+- An AWS account.
+- Openflow configured with:
+  - A **Deployment** is where your data flows execute.
+  - A **Runtime** is where you build your flows.
+
+<!-- ------------------------ -->
+## Extract API Data
+
+We will be working with race results linked to drivers, teams, and the circuits where the races took place.
+
+Let's have a look at the data :
+
+![F1 race example image](assets/1_JSON.png)
+
+How to understand it ?
+
+**Example:**  
+In 2025, the 14th race of the season was the Hungarian Grand Prix.
+It took place at the Hungaroring circuit.
+As for the results, the driver Lando Norris from the McLaren team won the Grand Prix, finishing in first position.
+
+The goal, for now, is to retrieve data through an API and store it in a stage.
+
+To 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).
+
+
+### Step 1: Create the Stage in Snowflake
+
+Open a Snowflake worksheet.  
+
+You could set the default database and schema to the one on which the `OPENFLOW` role has admin rights.  
+In our case, it's `OPENFLOW.OPENFLOW_TEST`.
+
+We will then run the code that creates a stage to retrieve the file from the API :
+
+```sql
+CREATE OR REPLACE STAGE F1_API 
+    DIRECTORY = ( ENABLE = true );
+```
+
+
+### Step 2: Launch Openflow
+
+1. Launch Openflow from Snowflake.
+
+   ![Launch Openflow](assets/launch_openflow.png)
+
+2. Connect to Openflow.
+
+   ![Connect to Openflow](assets/connect_to_openflow.png)
+
+3. Navigate to the **Runtimes** tab next to Overview, and open the runtime dedicated to this tutorial.
+
+   ![Open Runtime](assets/open_runtime.png)
+
+You should be on a white page like this one :
+
+   ![White Canvas](assets/white_canvas.png)
+
+
+### Step 3: Fetch data from API
+
+To build the workflow, we'll need Processors and Controller Services.
+
+- **Processors**: The main components in Openflow for reading, transforming, and loading data.
+- **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.
+
+Drag and drop a processor from the toolbar. It's the first icon.
+
+![Toolbar](assets/toolbar.png)
+
+Search for `InvokeHTTP` and click **Add**.
+
+![Drag and Drop InvokeHTTP](assets/drag_and_drop_invokehttp.png)
+
+> **Tips:**
+> You could also find it by using tags. For example, typing "https" or "client".
+> Snowflake processors are prefixed with a snowflake icon.
+
+The yellow warning triangle means the processor is invalid, not runnable.
+The status of your components is visible on the status bar under the toolbar.
+In addition to invalid status, a processor could be running (green triangle), stopped (red square) or disabled (crossed-out lightning bolt).
+
+We need to configure the processor we just added :
+
+1. Double-click on it or Right-Click and select **Configure**
+2. On the **Settings** tab, you can **Rename** the processor to `Call_JSON_F1_API_Race` for flow readability
+3. On the **Properties** tab, set **HTTP URL** to `https://f1api.dev/api/current/last/race`
+    
+    The only field we need to configure is the HTTP URL since the API doesn't require authentication.
+
+    ![InvokeHTTP Properties](assets/invokehttp_properties.png)
+
+4. On the **Relationships** tab, select **Terminate for all except Response**
+
+    Relationships determine what to do with the data output from a component. Since we’re calling an API, several relationship options are available.
+    In our case, we only want to keep the Response. We'll transfer it to another processor and ignore the rest.
+
+    ![InvokeHTTP Relationships](assets/invokehttp_relationships.png)
+
+5. Apply
+
+On 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.
+Here, we'll keep the default configuration and run the processor manually.
+
+![InvokeHTTP Scheduling](assets/invokehttp_scheduling.png)
+
+
+### Step 4: Staging Processor
+
+The component is now ready to call the API.
+The next steps are to add a processor that will save the response into a snowflake stage and configure it.
+
+
+**Add a Staging processor and Link it to the `Call_JSON_F1_API_Race` processor :**
+
+1. Add a `PutSnowflakeInternalStageFile` processor the same way we add the `Call_JSON_F1_API_Race` processor
+
+2. Link it to `Call_JSON_F1_API_Race` once the second processor is on the canvas
+
+    1. Hover over the `Call_JSON_F1_API_Race` processor until a blue arrow appears
+    
+        ![PutSnowflakeInternalStageFile drag](assets/PutSnowflakeInternalStageFile_drag.png)
+    
+    2. Click and drag a connection from `Call_JSON_F1_API_Race` to the `PutSnowflakeInternalStageFile` processor
+    
+        ![PutSnowflakeInternalStageFile drop](assets/PutSnowflakeInternalStageFile_drop.png)
+        
+    3. A create connection window appears. Transfer the http response to the `PutSnowflakeInternalStageFile` processor by selecting the **Response under relationship** and click **Add**
+    
+        ![PutSnowflakeInternalStageFile http connection](assets/PutSnowflakeInternalStageFile_http_connection.png)  
+
+        ![Two processors linked](assets/Two_processors_linked.png)
+
+
+**Configure the Connection to Snowflake through a controller service :**
+
+1. On the **Properties** tab, Configure the **Snowflake Connection Service** > Click on the three dots > **Create new service**
+
+    ![snowflake connection service](assets/snowflake_connection_service.png)
+
+2. On the Add Controller service window, search for **SnowflakeConnectionService** and Add
+
+    ![add controller service](assets/add_controller_service.png)
+
+    > To remember, a Controller Service is a shared service that will provide information to processors. This way, we configure the connection to Snowflake once.
+
+3. Select the three dots next to Snowflake Connection Service again but then choose **Go to service**
+  
+    This is the Controller Services tab for this group of processors.
+    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.
+    
+    You can see the current context under Controller Services. In this case, we're working on the OpenFlow sheet.
+    
+    ![Controller Services Overview](assets/Controller_Services_Overview.png)
+
+4. Select the three dots next to the service > Edit
+    - select **Password** for the **Authentication Strategy**.
+    - Fill in Account with your **Account Identifier** that you can find on your Snowflake Homepage > User Profile >Account > View account details > Account > Account Identifier
+    - For User, Password > Openflow User & Password
+
+    ![Edit Controller Service](assets/Edit_Controller_Service.png)
+
+    - Fill in the following fields with information that Openflow have access to :
+    
+    ![Default Openflow Database Access](assets/Default_Openflow_Database_Access.png)
+
+    - Apply
+
+5. Select the three dots next to the service > **Enable** to change the service state from **disabled** to **enable**
+
+    ![Enable Controller Service](assets/Enable_Controller_Service.png)
+
+    > On the right, under Referencing Components, you can see Processors and Controller services referencing to the Snowflake Connection
+  
+    1. Under Scope > Select Service Only
+    2. Enable
+    
+        ![Enable Controller Service Bis](assets/Enable_Controller_Service_Bis.png)
+
+6. Click on  **Back to processor** on the upper left to finish the configuration.
+
+
+**Configure the rest of the staging processor :**
+
+1. On the **Settings** tab, you can **Rename** the processor to `Put_JSON_In_Internal_Stage`.
+
+2. On the **Properties** tab, set :
+    -  **Named** : **Internal Stage Type**
+    -  **Stage Property** : reuse the stage `F1_API` we created in the first part
+    -  **File Name** : we choose to prefix the current timestamp with `RACE` to recognize our files in the stage :
+    ```java
+    RACE_${now():toNumber():formatInstant("yyyy_MM_dd_HH_mm_ss", "CET")}
+    ```
+
+    ![Put_JSON_In_Internal_Stage Properties](assets/Put_JSON_In_Internal_Stage_Properties.png)
+
+3. On the **Relationships** tab, select **Terminate** under **Failure** and **Success**.
+
+### Step 5: Run and Check
+
+The processors are now configured !  
+For 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.
+
+- Right-Click on the `Put_JSON_In_Internal_Stage` component and select **Start**.
+
+We are ready to trigger the workflow.  
+
+- Right-Click on the `Call_JSON_F1_API_Race` processor and select **Run once**.
+
+See how it works.  
+One 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.  
+The whole operation completed in just a second once the code was compiled.
+
+![Run Staging](assets/Run_Staging.png)
+
+Now let's check on snowsight if our file is in the stage.
+
+Run this on your worksheet :
+
+```sql
+-- LIST the STAGE FILES to check if our race file is there
+LIST @OPENFLOW.OPENFLOW_TEST.F1_API pattern ='.*RACE.*';
+```
+![Query Stage](assets/Query_Stage.png)
+
+When everithing works fine :  
+- Go back editing the last processor on Openflow.  
+- On the **Relationships** tab, unselect the box under **Success** so it can be forwarded to the next processor.
+
+<!-- ------------------------ -->
+## Load Data
+
+The goal of this step is to transfer data from the internal stage into a Snowflake table called **RAW_RACE**.
+
+
+### Step 1: Create the Table and File Format
+
+This table will include a VARIANT column, which is ideal for handling semi-structured formats like JSON.
+The entire file will be stored in a single row.
+
+In addition, we'll also need to define a file_format so that the COPY INTO can correctly interpret JSON data.
+
+```sql
+CREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.RAW_RACE (
+    v VARIANT
+  , insert_date TIMESTAMP_LTZ
+);
+
+CREATE OR REPLACE FILE FORMAT json_format
+  TYPE = JSON
+  STRIP_OUTER_ARRAY = TRUE;
+```
+
+
+### Step 2: Add the COPY INTO Processor
+
+Once these objects are created in Snowflake, **go back to OpenFlow** and add a new processor to the canvas.
+
+We’ll use the **ExecuteSQLStatement** processor to run a **COPY INTO** command that loads the data from the stage into the RAW_RACE table.
+
+1. Add an `ExecuteSQLStatement` processor to the canvas
+
+2. Connect from `Put_JSON_In_Internal_Stage` with **success** relationship > Add
+
+    ![Add ExecuteSQLStatement Processor](assets/Add_ExecuteSQLStatement_Processor.png)
+
+3. On the **Settings** tab, rename the `ExecuteSQLStatement` processor to `Copy_JSON_Into_Variant_Table`
+
+4. On the **Properties** tab :
+    1. Reused the **SnowflakeConnectionService** we created earlier as the value for **Connection Pooling Service property**
+  
+    2. In SQL, copy this :
+    
+        ```sql
+        COPY INTO OPENFLOW.OPENFLOW_TEST.RAW_RACE (v, insert_date)
+        FROM (
+            SELECT 
+                PARSE_JSON($1),
+                CURRENT_TIMESTAMP(0)
+            FROM @OPENFLOW_TEST.F1_API
+        )
+        FILE_FORMAT = (FORMAT_NAME = 'json_format')
+        PATTERN = '.*RACE.*';
+        ```
+  
+        > We add the current_timestamp to have the time of the insert.
+        >
+        > Option PATTERN will select every files that contains "RACE".
+
+        ![ExecuteSQLStatement Properties](assets/ExecuteSQLStatement_Properties.png)
+
+5. On the **Relationships** tab, Select **Terminate** for **failure** and **success** and **apply**
+
+### Step 3: Run and Check
+
+Time to test !
+
+- Right click on `Copy_JSON_Into_Variant_Table` and select **Start**.
+- Right click on `Call_JSON_F1_API_Race` and select **Run once**.
+
+Go back to Snowsight and check whether the rows were successfully inserted after running the COPY INTO command.
+
+```sql
+SELECT * FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE;
+```
+
+You should see something like this :
+
+![RAW_RACE Query](assets/RAW_RACE_Query.png)
+
+When everithing works fine :  
+- Go back editing the last processor on Openflow.  
+- On the **Relationships** tab, unselect the box under **Success** so it can be forwarded to the next processor.
+
+At this stage of the tutorial, we’ve successfully ingested JSON data from the API and stored it in a Snowflake table.
+
+<!-- ------------------------ -->
+## Transformation
+
+Last 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.
+
+Based on the JSON file content we explored on Step 1, we'll build a star model of this four tables :
+  - `Race_Results`
+  - `Drivers`
+  - `Teams`
+  - `Circuits`
+
+We have three dimensions (drivers, teams, circuits) and a fact (race_results).
+
+
+### Step 1: Table Creation
+
+To **create tables** and **identify the fields** contained in the JSON file, we can run the following code:
+
+```sql
+SELECT REGEXP_REPLACE(f.path, '\\[[0-9]+\\]', '[]') AS "Path",
+  TYPEOF(f.value) AS "Type",
+  COUNT(*) AS "Count"
+FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE,
+LATERAL FLATTEN(v, RECURSIVE=>true) f
+GROUP BY 1, 2 ORDER BY 1, 2;
+```
+
+Below is an extract from the results:
+
+![RAW_RACE fields](assets/RAW_RACE_Fields.png)
+
+Here is a proposed model to build.
+
+**Run it in your worksheet** to create the associated tables :
+
+```sql
+-- CIRCUIT
+
+CREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.CIRCUITS (
+      circuit_Id string
+    , name string
+    , city string
+    , country string
+    , length string
+    , corners number
+    , first_Participation_Year number
+    , lap_Record string
+    , fastest_Lap_Driver_Id string
+    , fastest_Lap_Team_Id string
+    , fastest_Lap_Year number
+    , insert_date timestampltz(0)
+    , update_date timestampltz(0)
+);
+
+-- RACE_RESULTS
+
+CREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.RACE_RESULTS (
+      race_Id string
+    , driver_Id string
+    , team_Id string
+    , circuit_Id string
+    , race_Name string
+    , race_Round integer
+    , race_Date date
+    , race_Time string
+    , driver_Race_Grid_Position string
+    , driver_Race_Final_Position string
+    , driver_Race_Points string
+    , driver_Race_Fast_Lap string
+    , driver_Race_Gap_With_Win_Time string
+    , insert_date timestampltz(0)
+    , update_date timestampltz(0)
+);
+
+-- DRIVERS
+
+CREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.DRIVERS (
+      driver_Id string
+    , name string
+    , surname string
+    , shortName string
+    , nationality string
+    , birthday string
+    , number integer
+    , insert_date timestampltz(0)
+    , update_date timestampltz(0)
+);
+
+-- TEAMS
+
+CREATE OR REPLACE TABLE OPENFLOW.OPENFLOW_TEST.TEAMS (
+      team_Id string
+    , name string
+    , nationality string
+    , first_Appareance integer
+    , constructors_Championships integer
+    , drivers_Championships integer
+    , insert_date timestampltz(0)
+    , update_date timestampltz(0)
+);
+```
+
+Check in your database that the objects are created :
+
+![Tables Creation](assets/Tables_Creation.png)
+
+
+### Step 2: Workflow processors
+
+Then we'll add components in OpenFlow to manage the data in these tables.
+
+1. **Add four ExecuteSQLStatement processors** to the canvas
+2. Connect from `Copy_JSON_Into_Variant_Table` to each new ExecuteSQLStatement processor with **success** as the relactionship > Add
+3. Right-click on each new ExecuteSQLStatement > Configure :
+    1. Under the **Settings** tab, name each processor as follows (one at a time):
+        - Extract_And_Transform_Race
+        - Extract_And_Transform_Drivers
+        - Extract_And_Transform_Teams
+        - Extract_And_Transform_Circuits
+    2. Under **Relationships**, Select **Terminate** for both failure and **success**, as these are our final components
+    
+        ![Final Tables relationships](assets/Final_Tables_relationships.png)
+    
+    3. Under **properties** :
+        - Reused the **SnowflakeConnectionService** we created as the value for **Connection Pooling Service property**
+        - **In SQL, complete with the code provided below** > OK
+    4. Apply
+
+Here is the code you need to paste in the SQL value for the right appropriate target table :
+
+  >
+  > It performs to an insert or update operation with the latest version of the data inserted into the RAW_RACE table .
+  > 
+  > This way, the flow is autonomous, up to date ,and contains no duplicates.
+  >
+
+RACE_RESULTS :
+
+```sql
+MERGE INTO OPENFLOW.OPENFLOW_TEST.RACE_RESULTS AS target
+USING (
+    SELECT 
+        v:races:raceId::string AS race_Id,
+        value:driver:driverId::string AS driver_Id,
+        value:team:teamId::string AS team_Id,
+        v:races:circuit:circuitId::string AS circuit_Id,
+        v:races:raceName::string AS race_Name,
+        v:races:round::integer AS race_Round,
+        v:races:date::date AS race_Date,
+        v:races:time::string AS race_Time,
+        value:grid::string AS driver_Race_Grid_Position,
+        value:position::string AS driver_Race_Final_Position,
+        value:points::number AS driver_Race_Points,
+        value:fastLap::string AS driver_Race_Fast_Lap,
+        value:time::string AS driver_Race_Gap_With_Win_Time,
+        insert_date
+    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,
+         LATERAL FLATTEN(input => r.V, path => 'races:results')
+    qualify row_number() over(partition by v:races:raceId::string, value:driver:driverId::string order by insert_date desc) = 1
+) AS source
+ON target.race_Id = source.race_Id AND target.driver_Id = source.driver_Id
+
+WHEN MATCHED THEN
+    UPDATE SET
+        target.team_Id = source.team_Id,
+        target.circuit_Id = source.circuit_Id,
+        target.race_Name = source.race_Name,
+        target.race_Round = source.race_Round,
+        target.race_Date = source.race_Date,
+        target.race_Time = source.race_Time,
+        target.driver_Race_Grid_Position = source.driver_Race_Grid_Position,
+        target.driver_Race_Final_Position = source.driver_Race_Final_Position,
+        target.driver_Race_Points = source.driver_Race_Points,
+        target.driver_Race_Fast_Lap = source.driver_Race_Fast_Lap,
+        target.driver_Race_Gap_With_Win_Time = source.driver_Race_Gap_With_Win_Time,
+        target.update_date = current_timestamp
+
+WHEN NOT MATCHED THEN
+    INSERT (
+        race_Id,
+        driver_Id,
+        team_Id,
+        circuit_Id,
+        race_Name,
+        race_Round,
+        race_Date,
+        race_Time,
+        driver_Race_Grid_Position,
+        driver_Race_Final_Position,
+        driver_Race_Points,
+        driver_Race_Fast_Lap,
+        driver_Race_Gap_With_Win_Time,
+        insert_date
+    )
+    VALUES (
+        source.race_Id,
+        source.driver_Id,
+        source.team_Id,
+        source.circuit_Id,
+        source.race_Name,
+        source.race_Round,
+        source.race_Date,
+        source.race_Time,
+        source.driver_Race_Grid_Position,
+        source.driver_Race_Final_Position,
+        source.driver_Race_Points,
+        source.driver_Race_Fast_Lap,
+        source.driver_Race_Gap_With_Win_Time,
+        current_timestamp
+    );
+```
+
+DRIVERS :
+
+```sql
+MERGE INTO OPENFLOW.OPENFLOW_TEST.DRIVERS AS target
+USING (
+    SELECT 
+        value:driver:driverId::string       AS driver_Id,
+        value:driver:name::string           AS name,
+        value:driver:surname::string        AS surname,
+        value:driver:shortName::string      AS shortName,
+        value:driver:nationality::string    AS nationality,
+        value:driver:birthday::string       AS birthday,
+        value:driver:number::number         AS number,
+        insert_date
+    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,
+         LATERAL FLATTEN(input => r.V, PATH => 'races:results')
+    QUALIFY ROW_NUMBER() OVER (PARTITION BY value:driver:driverId::string ORDER BY insert_date DESC) = 1
+) AS source
+ON target.driver_Id = source.driver_Id
+
+WHEN MATCHED THEN
+    UPDATE SET
+        target.name         = source.name,
+        target.surname      = source.surname,
+        target.shortName   = source.shortName,
+        target.nationality  = source.nationality,
+        target.birthday     = source.birthday,
+        target.number       = source.number,
+        target.update_date  = CURRENT_TIMESTAMP
+
+WHEN NOT MATCHED THEN
+    INSERT (
+        driver_Id,
+        name,
+        surname,
+        shortName,
+        nationality,
+        birthday,
+        number,
+        insert_date
+    )
+    VALUES (
+        source.driver_Id,
+        source.name,
+        source.surname,
+        source.shortName,
+        source.nationality,
+        source.birthday,
+        source.number,
+        CURRENT_TIMESTAMP
+    );
+```
+
+TEAMS :
+
+```sql
+MERGE INTO OPENFLOW.OPENFLOW_TEST.TEAMS AS target
+USING (
+    SELECT 
+        value:team:teamId::string                     AS team_id,
+        value:team:teamName::string                  AS name,
+        value:team:nationality::string               AS nationality,
+        value:team:firstAppareance::number           AS first_appareance,
+        value:team:constructorsChampionships::number AS constructors_championships,
+        value:team:driversChampionships::number      AS drivers_championships,
+        insert_date
+    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r,
+         LATERAL FLATTEN(input => r.V, PATH => 'races:results')
+    QUALIFY ROW_NUMBER() OVER (
+        PARTITION BY value:team:teamId::string 
+        ORDER BY insert_date DESC
+    ) = 1
+) AS source
+ON target.team_id = source.team_id
+
+WHEN MATCHED THEN
+    UPDATE SET
+        target.name                      = source.name,
+        target.nationality               = source.nationality,
+        target.first_appareance          = source.first_appareance,
+        target.constructors_championships = source.constructors_championships,
+        target.drivers_championships     = source.drivers_championships,
+        target.update_date               = CURRENT_TIMESTAMP
+
+WHEN NOT MATCHED THEN
+    INSERT (
+        team_id,
+        name,
+        nationality,
+        first_appareance,
+        constructors_championships,
+        drivers_championships,
+        insert_date
+    )
+    VALUES (
+        source.team_id,
+        source.name,
+        source.nationality,
+        source.first_appareance,
+        source.constructors_championships,
+        source.drivers_championships,
+        CURRENT_TIMESTAMP
+    );
+```
+
+CIRCUITS :
+
+```sql
+MERGE INTO OPENFLOW.OPENFLOW_TEST.CIRCUITS AS target
+USING (
+    SELECT 
+        V:races:circuit:circuitId::string                         AS circuit_Id,
+        V:races:circuit:circuitName::string                       AS name,
+        V:races:circuit:city::string                              AS city,
+        V:races:circuit:country::string                           AS country,
+        V:races:circuit:circuitLength::string                     AS length,
+        V:races:circuit:corners::number                           AS corners,
+        V:races:circuit:firstParticipationYear::number            AS first_Participation_Year,
+        V:races:circuit:lapRecord::string                         AS lap_Record,
+        V:races:circuit:fastestLapDriverId::string                AS fastest_Lap_Driver_Id,
+        V:races:circuit:fastestLapTeamId::string                  AS fastest_Lap_Team_Id,
+        V:races:circuit:fastestLapYear::number                    AS fastest_Lap_Year,
+        insert_date
+    FROM OPENFLOW.OPENFLOW_TEST.RAW_RACE r
+    QUALIFY ROW_NUMBER() OVER (PARTITION BY V:races:circuit:circuitId::string ORDER BY insert_date DESC) = 1
+) AS source
+ON target.circuit_Id = source.circuit_Id
+
+WHEN MATCHED THEN
+    UPDATE SET
+        target.name                      = source.name,
+        target.city                      = source.city,
+        target.country                   = source.country,
+        target.length                    = source.length,
+        target.corners                   = source.corners,
+        target.first_Participation_Year  = source.first_Participation_Year,
+        target.lap_Record                = source.lap_Record,
+        target.fastest_Lap_Driver_Id     = source.fastest_Lap_Driver_Id,
+        target.fastest_Lap_Team_Id       = source.fastest_Lap_Team_Id,
+        target.fastest_Lap_Year          = source.fastest_Lap_Year,
+        target.update_date               = CURRENT_TIMESTAMP
+
+WHEN NOT MATCHED THEN
+    INSERT (
+        circuit_Id,
+        name,
+        city,
+        country,
+        length,
+        corners,
+        first_Participation_Year,
+        lap_Record,
+        fastest_Lap_Driver_Id,
+        fastest_Lap_Team_Id,
+        fastest_Lap_Year,
+        insert_date
+    )
+    VALUES (
+        source.circuit_Id,
+        source.name,
+        source.city,
+        source.country,
+        source.length,
+        source.corners,
+        source.first_Participation_Year,
+        source.lap_Record,
+        source.fastest_Lap_Driver_Id,
+        source.fastest_Lap_Team_Id,
+        source.fastest_Lap_Year,
+        CURRENT_TIMESTAMP
+    );
+```
+
+![Final Tables Properties](assets/Final_Tables_Properties.png)
+
+
+### Step 3: Run and check
+
+Once it's all set up, right-click on each of the four processors and set them to **Start**.
+
+You should get something similar to that :
+
+![All processor view](assets/All_processor_view.png)
+
+Let's perform the final run. Right-Click on the `Call_JSON_F1_API_Race` processor and select **Run Once**.
+
+Go back to your worksheet. We'll verify that the tables have been successfully populated.
+
+Check your databases and/or run this select :
+
+```sql
+-- Check if the tables are filled
+select * from OPENFLOW.OPENFLOW_TEST.CIRCUITS;
+select * from OPENFLOW.OPENFLOW_TEST.RACE_RESULTS;
+select * from OPENFLOW.OPENFLOW_TEST.DRIVERS;
+select * from OPENFLOW.OPENFLOW_TEST.TEAMS;
+```
+
+<!-- ------------------------ -->
+## Scheduling
+
+Until 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.
+
+In 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 :
+  - **Timer-driven** - Requires a number of seconds.
+    For example, if we choose 604 800 seconds, the processor will run every week from the moment the processor is started.
+  
+  - **CRON-driven** - Can be more precise and is based on CRON expressions.
+    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.
+
+![Scheduling view](assets/Scheduling_view.png)
+
+<!-- ------------------------ -->
+## Conclusion And Resources
+
+### Conclusion
+
+Congratulations ! You’ve successfully completed this quickstart on retrieving JSON data from an API and loading it into Snowflake.
+We 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.
+After 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.
+
+### What You Learned
+
+  - How to build a complete ELT flow with Openflow without delay using components and SQL instructions
+  - How to Fetch JSON data from an API, send it to a stage and transform it into tables
+  - How to run a workflow manually or manage an automatic scheduling
+
+### Resources
+
+**Openflow Documentation**:
+  - [About Openflow](https://docs.snowflake.com/en/user-guide/data-integration/openflow/about)
+  - [Setting up Openflow](https://docs.snowflake.com/en/user-guide/data-integration/openflow/setup-openflow)
+  - [List of Processors](https://docs.snowflake.com/en/user-guide/data-integration/openflow/processors/index)
+  - [List of Controller Services](https://docs.snowflake.com/en/user-guide/data-integration/openflow/controllers/index)
+
+[NiFi Documentation](https://nifi.apache.org/docs/nifi-docs/html/user-guide.html)
+
+[F1 API website](https://f1api.dev/fr/docs) 🏎️
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