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------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EAzure Data Factory serves as a central orchestrator for managing data tasks, especially in ETL processes. It empowers businesses to seamlessly manage data movement through a code-free UI for intuitive authoring and single-pane-of-glass monitoring and management. By streamlining these workflows, Azure Data Factory enables organizations to harness their data assets effectively, driving informed decision-making and operational efficiency in today's data-centric landscape.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart you will build an architecture that demonstrates how to use Azure Data Factory to orchestrate data ingestion from an Azure SQL transactional database into Snowflake to generate powerful analytical insights.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake\u003C/a\u003E and a Snowflake account\u003C/li\u003E\u003Cli\u003EFamiliarity with Azure and an Azure account, an ADF workspace, and an Azure SQL with Adventure works sample data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EYou'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to deploy an Azure SQL with Adventure Works sample data\u003C/li\u003E\u003Cli\u003ESetup an Azure Data Factory pipeline with linked services and datasets\u003C/li\u003E\u003Cli\u003EUtilize Copy Data, Data Flow, and Script activities using both SQL and SnowPark Python within ADF\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA free \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake Account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://azure.microsoft.com/en-us/free/search/?ef_id=_k_2ba2be3ad9791e57964fda0c62ccd55c_k_&OCID=AIDcmm5edswduu_SEM_k_2ba2be3ad9791e57964fda0c62ccd55c_k_&msclkid=2ba2be3ad9791e57964fda0c62ccd55c\"\u003EAzure Account\u003C/a\u003E with ADF, storage container, and Azure SQL\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n","\u003Cp\u003EYou will build an orchestrated data pipeline from Azure SQL to Snowflake, learning how to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDeploy an Azure SQL with Adventure Works\u003C/li\u003E\u003Cli\u003EDeploy and ADF Workspace\u003C/li\u003E\u003Cli\u003EBuild an ADF pipeline\u003C/li\u003E\u003Cli\u003ETrigger the data pipeline\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe end-to-end workflow will look like this:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/ADFsql.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse Case\u003C/h2\u003E\n","\u003Cp\u003EAs a retail analyst, imagine effortlessly unraveling your company's sales performance with Azure Data Factory and Snowflake. Your sales data, stored in Azure SQL, seamlessly flows into Snowflake, where you can conduct comprehensive analyses with unprecedented speed and scalability. A combination of GUI and scripting-based interfaces allows anyone, no matter their skillset or preferences, to easily orchestrate data pipelines. With pushdown compute capabilities, choose between SQL and Python to effortlessly sift through the data within Snowflake, revealing invaluable insights.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Snowflake Environment\u003C/h2\u003E\n","\u003Cp\u003EThe first thing to do is create a database and warehouse in your Snowflake environment. Run the below code in a Snowflake worksheet. You are using the AccountAdmin role here for demo purposes, but in production you will likely use a different role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a new database (if not already created)\nCREATE DATABASE IF NOT EXISTS ADFdemo;\nUSE DATABASE ADFdemo;\n-- Create a new virtual warehouse (if not already created)\nCREATE WAREHOUSE IF NOT EXISTS ADFdemo WITH WAREHOUSE_SIZE='X-SMALL';\nCREATE SCHEMA IF NOT EXISTS Raw;\nCREATE SCHEMA IF NOT EXISTS Analytics;\n\n\nCREATE OR REPLACE TABLE Raw.Customer(\n\tCustomerID int,\n\tNameStyle STRING ,\n\tTitle STRING NULL,\n\tFirstName STRING ,\n\tMiddleName STRING NULL,\n\tLastName STRING ,\n\tSuffix STRING NULL,\n\tCompanyName STRING NULL,\n\tSalesPerson STRING NULL,\n\tEmailAddress STRING NULL,\n\tPhone STRING NULL,\n\tPasswordHash STRING ,\n\tPasswordSalt STRING ,\n\trowguid STRING ,\n\tModifiedDate datetime \n);\n\n\nCREATE OR REPLACE TABLE Raw.Product(\n\tProductID int  ,\n\tName STRING ,\n\tProductNumber STRING ,\n\tColor STRING NULL,\n\tStandardCost decimal ,\n\tListPrice decimal ,\n\tSize STRING NULL,\n\tWeight decimal(8, 2) NULL,\n\tProductCategoryID int NULL,\n\tProductModelID int NULL,\n\tSellStartDate datetime ,\n\tSellEndDate datetime NULL,\n\tDiscontinuedDate datetime NULL,\n\tThumbNailPhoto STRING NULL,\n\tThumbnailPhotoFileName STRING NULL,\n\trowguid STRING ,\n\tModifiedDate datetime \n); \n\nCREATE OR REPLACE TABLE Raw.ProductCategory(\n\tProductCategoryID int  ,\n\tParentProductCategoryID int NULL,\n\tName STRING ,\n\trowguid STRING ,\n\tModifiedDate datetime \n);\n\n\nCREATE OR REPLACE TABLE Raw.SalesOrder(\n\tSalesOrderID int ,\n\tSalesOrderDetailID int ,\n\tOrderQty int ,\n\tProductID int ,\n\tUnitPrice DECIMAL ,\n\tUnitPriceDiscount DECIMAL ,\n\tLineTotal  DECIMAL,\n\trowguid STRING ,\n\tModifiedDate datetime ,\n\tRevisionNumber int ,\n\tOrderDate datetime ,\n\tDueDate datetime ,\n\tShipDate datetime NULL,\n\tStatus int ,\n\tSalesOrderNumber  STRING,\n\tPurchaseOrderNumber STRING NULL,\n\tAccountNumber STRING NULL,\n\tCustomerID int ,\n\tShipToAddressID int NULL,\n\tBillToAddressID int NULL,\n\tShipMethod STRING ,\n\tCreditCardApprovalCode STRING NULL,\n\tSubTotal DECIMAL ,\n\tTaxAmt DECIMAL ,\n\tFreight DECIMAL ,\n\tTotalDue  DECIMAL,\n\tComment STRING NULL\n);\n\nCREATE TABLE IF NOT EXISTS Analytics.CustomerOrders (\nSalesOrderID int,\norderdate datetime,\nDueDate datetime,\nShipDate datetime,\nPurchaseOrderNumber STRING,\nAccountNumber STRING,\nTotalDue DECIMAL,\nrowguid STRING,\nTitle STRING,\nFirstName STRING,\nLastName STRING,\nCompanyName STRING,\nEmailAddress STRING,\nLineTotal DECIMAL,\nProductname STRING,\nProductNumber STRING,\nProductCategoryName STRING\n);\n\nCREATE OR REPLACE PROCEDURE Analytics.InsertCustomerOrders()\nRETURNS VARCHAR\nLANGUAGE SQL\nAS\n$$\nBEGIN\n\nINSERT INTO Analytics.CustomerOrders\nSELECT\n    so.SalesOrderID,\n    so.orderdate,\n    so.DueDate,\n    so.ShipDate,\n    so.PurchaseOrderNumber,\n    so.AccountNumber,\n    so.TotalDue,\nso.rowguid,\nc.Title,\nc.FirstName,\nc.LastName,\nc.CompanyName,\nc.EmailAddress,\nso.LineTotal,\np.name,\np.ProductNumber,\npc.Name\n  FROM raw.SalesOrder so\n    join raw.Customer c on c.CustomerID=so.CustomerID\n    join raw.Product p on p.ProductID=so.ProductID\n    join raw.ProductCategory pc on pc.ProductCategoryID=p.ProductCategoryID\nLEFT JOIN Analytics.CustomerOrders co on co.SalesOrderID=so.SalesOrderID \nWHERE co.rowguid is null; \n\n  RETURN 'Success';\nEND;\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis SQL has set up the raw tables for us to land data into, as well as some stored procedures you’ll use later in the quickstart.\u003C/p\u003E\n","\u003Cp\u003EThe result should look like this screenshot, with tables, stored procedure, and Schemas:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/SnowflakeSchema.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Azure Environment\u003C/h2\u003E\n","\u003Ch3\u003ESet Up Azure SQL with AdventureWorks Data\u003C/h3\u003E\n","\u003Cp\u003ENow you need to set up your Azure SQL instance.  When you create it you can include a sample Adventure Works dataset, this will represent your transactional sales database.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect create > Search Martkerplace for Azure SQL > Click Create > Under SQL Databases select Single Database and click Create\n\u003Cul\u003E\u003Cli\u003EDatabase Name: ADFdemo\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EServer (Select create new): Give a unique server name, enable SQL authentication for this quickstart and enter an admin login and password.\n\u003Cul\u003E\u003Cli\u003EGo to Networking, for Connectivity method select Public Endpoint, select yes for both Allow Azure Services and Add Current client IP.\u003C/li\u003E\u003Cli\u003ENo changes in Networking or Security\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EUnder Additional Settings, Use Existing Data: Select Sample\n\u003Cul\u003E\u003Cli\u003ESelect Create\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/SQL.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESet Up ADF Workspace\u003C/h3\u003E\n","\u003Cp\u003EHead back to the Azure Marketplace and search for Data Factory.\u003Cbr\u003E\nSelect Create > Enter a Name, your preferred region, and V2 for the Instance Details.\u003Cbr\u003E\nCreate with the default settings.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/ManagedID.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESet up Container for Staging\u003C/h3\u003E\n","\u003Cp\u003EBehind the scenes, ADF will use Blob Storage to stage data as it is moved.  So you need to have a Blob Storage container available.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFrom the Marketplace, search for Storage Account and create.\n\u003Cul\u003E\u003Cli\u003EGive the Account a unique name\u003C/li\u003E\u003Cli\u003EKeep all the default settings the same and Create\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E(Note: you are leaving the Networking open to public access for this Quickstart, be sure to follow your organization's security best practices for non-demo projects)\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EOnce created select Go to Resource\n\u003Cul\u003E\u003Cli\u003EOn the left select Containers\u003C/li\u003E\u003Cli\u003EAdd a Container named adfdemo\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Containers.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EYou’ll need to generate a SAS token for ADF to access the container.\n\u003Cul\u003E\u003Cli\u003EOn the left panel select Shared Access Signature\u003C/li\u003E\u003Cli\u003EEnsure all resource types are available\u003C/li\u003E\u003Cli\u003EMake the expiration End Date long enough to finish the Quickstart\u003C/li\u003E\u003Cli\u003EGenerate SAS and connection string, then copy out the SAS token\n(Note: Be sure to save it or leave this tab open, you won’t be able to get back to the token)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/SAS.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfigure the Linked Services\u003C/h2\u003E\n","\u003Cp\u003ENow you want to get into the ADF Studio.  Go to the ADF resource you created and click Launch Studio from the Overview tab.  This will open up the Data Factory Studio.\u003C/p\u003E\n","\u003Cp\u003EOn the Left panel, click on the Manage tab, and then linked services.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/LinkedServices.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELinked Services act as the connection strings to any data sources or destinations you want to interact with. In this case you want to set up services for Azure SQL, Snowflake, and Blob Storage.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EClick Create Linked Services > Select Azure SQL Database > Continue\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAdd a Name, select all the subscription information for your Azure SQL database\u003C/li\u003E\u003Cli\u003EEnter your SQL authentication username and password setup previously\u003C/li\u003E\u003Cli\u003ETest connection and create\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick Create Linked Services > Select Azure Blob Storage> Continue\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESet Authentication type to SAS URI and enter the information created in previous steps\u003C/li\u003E\u003Cli\u003EThe URL should be just the base URL: such as AccountName.blob.core.windows.net\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick Create Linked Services > Select Snowflake > Continue\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFill out the details for your account, note the format of the account name\u003C/li\u003E\u003Cli\u003EThe database and warehouse are both ADFDEMO\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/LinkedServices2.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Source & Sink Datasets\u003C/h2\u003E\n","\u003Cp\u003ENext you need to create datasets that map the data inside the linked services.  In this case you'll need to set up 5 source datasets for the Azure SQL tables, and 5 sink datasets for the Snowflake tables.\u003C/p\u003E\n","\u003Cp\u003ENavigate to Author on the side menu, under the three dot menu on Datasets select New Dataset.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect Azure SQL database, name it AzureSQL_Product and enter your linked server to azure SQL\u003C/li\u003E\u003Cli\u003EThen set the table name to SalesLT.Product\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ERepeat this process for the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAzureSQL_ProductCategory\u003C/li\u003E\u003Cli\u003EAzureSQL_Customer\u003C/li\u003E\u003Cli\u003EAzureSQL_SalesOrderHeader\u003C/li\u003E\u003Cli\u003EAzureSQL_SalesOrderDetail\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENow you want to set up the Sink datasets in Snowflake.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a new dataset, but this time select Snowflake\u003C/li\u003E\u003Cli\u003EName the dataset Snowflake_Product\u003C/li\u003E\u003Cli\u003ESet the linked service to the Snowflake one you created.  And set the tablename to Raw.Product\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAgain repeat this process for the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake_ProductCategory\u003C/li\u003E\u003Cli\u003ESnowflake_Customer\u003C/li\u003E\u003Cli\u003ESnowflake_SalesOrder\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/DataSets.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate the Pipeline\u003C/h2\u003E\n","\u003Cp\u003ENow it’s time to set up the pipeline that will control the flow of the data.\u003C/p\u003E\n","\u003Ch3\u003ECopy Data\u003C/h3\u003E\n","\u003Cp\u003EYou'll start with a Copy Data Activity to directly copy the data from source to sink, with no transformation.  Transformations can be done later in Snowflake, which is an ELT process.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder Move and Transform, select a CopyData activity.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EName the CopyData Activity as Product.\u003C/li\u003E\u003Cli\u003EUnder the Source tab, select AzureSql_Product.\u003C/li\u003E\u003Cli\u003EUnder the Sink tab, select Snowflake_Product. In the pre-copy script enter “Truncate table Raw.Product;”. This SQL will clear what's in the raw staging in Snowflake each time data is loaded.\u003C/li\u003E\u003Cli\u003EUnder the Settings tab, toggle Enable Staging and set the Staging account to the Blob Storage setup previously.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou can copy and paste the Activity to create two more CopyData activities.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFor the first, update the name to ProductCategory, and update the source and sink to the product category tables. Update the Sink truncate command to Raw.ProductCategory.\u003C/li\u003E\u003Cli\u003EFor the second, do the same with Customer.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Pipelines.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EData Flow\u003C/h3\u003E\n","\u003Cp\u003ENow add a Data Flow Activity. Data Flows are a traditional ETL task. You can perform transformations on the data in the middle of the data movement.  If you are familiar with SSIS, data flows have similar functionality. In this case you are going to merge the Sales Order Header and Detail into a single table in Snowflake.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EName the activity SalesOrders.\u003C/li\u003E\u003Cli\u003EUnder Settings select New Data flow.\u003C/li\u003E\u003Cli\u003EUnder the Properties set the Name to SalesOrder DataFlow.\u003C/li\u003E\u003Cli\u003EAt the top, select Data Flow Debug, and OK.  This will create a Spark cluster for debugging.\u003C/li\u003E\u003Cli\u003EAdd a Source.  Name the output stream SalesOrderHeader and Select the dataset as AzureSQL_SalesOrderHeader.  Under projection click Import projection to get the field metadata.\u003C/li\u003E\u003Cli\u003EAdd a second source. Name the output stream SalesOrderDetail and Select the dataset as AzureSQL_SalesOrderDetail.  Under projection click Import projection to get the field names.\u003C/li\u003E\u003Cli\u003ESelect the small + to the right of the SalesOrderHeader task, and add Join.\u003C/li\u003E\u003Cli\u003ESet the left stream to SalesOrderHeader, and the right to SalesOrderDetail.\u003C/li\u003E\u003Cli\u003EUnder join conditions set both the left and right to SalesOrderID.\u003C/li\u003E\u003Cli\u003EClick the + and add in a select task.  Since there are some columns in both tables with the same name, the skip duplicate input columns will just use the SalesOrderHeader columns.\u003C/li\u003E\u003Cli\u003ESelect + and add a sink.  Name it Snowflake, set the dataset to Snowflake_SalesOrder. Under mapping uncheck Auto Mapping so it uses the fields from the select step. You may need to open the Dataset and Import schema to refresh the metadata if the mapping does not look correct.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/DataFlows.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EScript Tasks\u003C/h3\u003E\n","\u003Cp\u003EIn order to push compute to Snowflake, you can add in script activities to call queries or stored procedures. In this step you can transform the data however you need to, using SQL or python via Snowpark.\u003C/p\u003E\n","\u003Cp\u003EBack in the pipeline, under the General Activities, add in a Script Activity.\u003C/p\u003E\n","\u003Cp\u003EName the activity Update CustomerOrders, under Settings add in a query for\n\u003Ccode\u003ECALL Analytics.InsertCustomerOrders();\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003ENow orchestrate the order of execution by connecting activities with the Success connector.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Debug.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBy clicking the Validate and Debug you can perform a test run of the entire pipeline.\u003C/p\u003E\n","\u003Cp\u003EThen go into your Snowflake instance and validate that the tables have data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETriggers and Monitors\u003C/h2\u003E\n","\u003Cp\u003EADF is designed to be an automated pipeline orchestrator.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAt the top of the pipeline click Add Trigger, and New/Edit.\u003C/li\u003E\u003Cli\u003ESet Recurrence to Every 1 Day.\u003C/li\u003E\u003Cli\u003ESet the time to whatever works best for your testing.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Triggers.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow publish all objects created by clicking on Publish all at the top.\u003C/p\u003E\n","\u003Cp\u003EOnce the trigger has activated and the pipeline is running, you can monitor the progress of all runs in the Monitor tab on the left menu.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Monitor.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully set up a data pipeline ingesting data from Azure SQL to Snowflake, using CopyData, DataFlows, and Script activities. You now have bulk data ingestion occuring at a scheduled interval, and your AdventureWorks retail data is prepared for analytics, machine learning, and data applications.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to deploy an Azure SQL with Adventure Works sample data\u003C/li\u003E\u003Cli\u003ESet up an Azure Data Factory pipeline with linked services and datasets\u003C/li\u003E\u003Cli\u003EUtilize Copy Data, Data Flow, and Script activities using both SQL and SnowPark Python within ADF\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EThis Quickstart is just that, a quick way to get you started with ADF and Snowflake. You will want to consider the additional items below for enterprise-grade workloads:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://learn.microsoft.com/en-us/azure/data-factory/connector-snowflake?tabs=data-factory\"\u003EOfficial Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://learn.microsoft.com/en-us/azure/data-factory/connector-troubleshoot-snowflake\"\u003ECommon Troubleshooting\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://community.snowflake.com/s/article/How-to-set-up-a-managed-private-endpoint-from-Azure-Data-Factory-or-Synapse-to-Snowflake\"\u003EUsing Private Endpoints\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/azure-data-factory-connecting-to-snowflake-using-keypair-authentication-906000506345\"\u003EKey-Pair authentication\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build enterprise data pipelines with Azure Data Factory and Snowflake for orchestrated ETL, CDC, and data integration workflows.","title":"Getting Started with Azure Data Factory and Snowflake","elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nAzure Data Factory serves as a central orchestrator for managing data tasks, especially in ETL processes. It empowers businesses to seamlessly manage data movement through a code-free UI for intuitive authoring and single-pane-of-glass monitoring and management. By streamlining these workflows, Azure Data Factory enables organizations to harness their data assets effectively, driving informed decision-making and operational efficiency in today's data-centric landscape.\n\nIn this quickstart you will build an architecture that demonstrates how to use Azure Data Factory to orchestrate data ingestion from an Azure SQL transactional database into Snowflake to generate powerful analytical insights.\n\n\n### Prerequisites\n- Familiarity with [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) and a Snowflake account\n- Familiarity with Azure and an Azure account, an ADF workspace, and an Azure SQL with Adventure works sample data\n\n### You'll Learn\n- How to deploy an Azure SQL with Adventure Works sample data\n- Setup an Azure Data Factory pipeline with linked services and datasets\n- Utilize Copy Data, Data Flow, and Script activities using both SQL and SnowPark Python within ADF\n\n### What You’ll Need \n- A free [Snowflake Account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n- [Azure Account](https://azure.microsoft.com/en-us/free/search/?ef_id=_k_2ba2be3ad9791e57964fda0c62ccd55c_k_&OCID=AIDcmm5edswduu_SEM_k_2ba2be3ad9791e57964fda0c62ccd55c_k_&msclkid=2ba2be3ad9791e57964fda0c62ccd55c) with ADF, storage container, and Azure SQL\n\n### What You’ll Build \nYou will build an orchestrated data pipeline from Azure SQL to Snowflake, learning how to:\n- Deploy an Azure SQL with Adventure Works \n- Deploy and ADF Workspace\n- Build an ADF pipeline\n- Trigger the data pipeline\n\nThe end-to-end workflow will look like this:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/ADFsql.png)\n\n\u003C!-- ------------------------ --\u003E\n## Use Case\n\nAs a retail analyst, imagine effortlessly unraveling your company's sales performance with Azure Data Factory and Snowflake. Your sales data, stored in Azure SQL, seamlessly flows into Snowflake, where you can conduct comprehensive analyses with unprecedented speed and scalability. A combination of GUI and scripting-based interfaces allows anyone, no matter their skillset or preferences, to easily orchestrate data pipelines. With pushdown compute capabilities, choose between SQL and Python to effortlessly sift through the data within Snowflake, revealing invaluable insights. \n\n\n\u003C!-- ------------------------ --\u003E\n## Set Up Snowflake Environment\n\nThe first thing to do is create a database and warehouse in your Snowflake environment. Run the below code in a Snowflake worksheet. You are using the AccountAdmin role here for demo purposes, but in production you will likely use a different role.\n\n```sql\n-- Create a new database (if not already created)\nCREATE DATABASE IF NOT EXISTS ADFdemo;\nUSE DATABASE ADFdemo;\n-- Create a new virtual warehouse (if not already created)\nCREATE WAREHOUSE IF NOT EXISTS ADFdemo WITH WAREHOUSE_SIZE='X-SMALL';\nCREATE SCHEMA IF NOT EXISTS Raw;\nCREATE SCHEMA IF NOT EXISTS Analytics;\n\n\nCREATE OR REPLACE TABLE Raw.Customer(\n\tCustomerID int,\n\tNameStyle STRING ,\n\tTitle STRING NULL,\n\tFirstName STRING ,\n\tMiddleName STRING NULL,\n\tLastName STRING ,\n\tSuffix STRING NULL,\n\tCompanyName STRING NULL,\n\tSalesPerson STRING NULL,\n\tEmailAddress STRING NULL,\n\tPhone STRING NULL,\n\tPasswordHash STRING ,\n\tPasswordSalt STRING ,\n\trowguid STRING ,\n\tModifiedDate datetime \n);\n\n\nCREATE OR REPLACE TABLE Raw.Product(\n\tProductID int  ,\n\tName STRING ,\n\tProductNumber STRING ,\n\tColor STRING NULL,\n\tStandardCost decimal ,\n\tListPrice decimal ,\n\tSize STRING NULL,\n\tWeight decimal(8, 2) NULL,\n\tProductCategoryID int NULL,\n\tProductModelID int NULL,\n\tSellStartDate datetime ,\n\tSellEndDate datetime NULL,\n\tDiscontinuedDate datetime NULL,\n\tThumbNailPhoto STRING NULL,\n\tThumbnailPhotoFileName STRING NULL,\n\trowguid STRING ,\n\tModifiedDate datetime \n); \n\nCREATE OR REPLACE TABLE Raw.ProductCategory(\n\tProductCategoryID int  ,\n\tParentProductCategoryID int NULL,\n\tName STRING ,\n\trowguid STRING ,\n\tModifiedDate datetime \n);\n\n\nCREATE OR REPLACE TABLE Raw.SalesOrder(\n\tSalesOrderID int ,\n\tSalesOrderDetailID int ,\n\tOrderQty int ,\n\tProductID int ,\n\tUnitPrice DECIMAL ,\n\tUnitPriceDiscount DECIMAL ,\n\tLineTotal  DECIMAL,\n\trowguid STRING ,\n\tModifiedDate datetime ,\n\tRevisionNumber int ,\n\tOrderDate datetime ,\n\tDueDate datetime ,\n\tShipDate datetime NULL,\n\tStatus int ,\n\tSalesOrderNumber  STRING,\n\tPurchaseOrderNumber STRING NULL,\n\tAccountNumber STRING NULL,\n\tCustomerID int ,\n\tShipToAddressID int NULL,\n\tBillToAddressID int NULL,\n\tShipMethod STRING ,\n\tCreditCardApprovalCode STRING NULL,\n\tSubTotal DECIMAL ,\n\tTaxAmt DECIMAL ,\n\tFreight DECIMAL ,\n\tTotalDue  DECIMAL,\n\tComment STRING NULL\n);\n\nCREATE TABLE IF NOT EXISTS Analytics.CustomerOrders (\nSalesOrderID int,\norderdate datetime,\nDueDate datetime,\nShipDate datetime,\nPurchaseOrderNumber STRING,\nAccountNumber STRING,\nTotalDue DECIMAL,\nrowguid STRING,\nTitle STRING,\nFirstName STRING,\nLastName STRING,\nCompanyName STRING,\nEmailAddress STRING,\nLineTotal DECIMAL,\nProductname STRING,\nProductNumber STRING,\nProductCategoryName STRING\n);\n\nCREATE OR REPLACE PROCEDURE Analytics.InsertCustomerOrders()\nRETURNS VARCHAR\nLANGUAGE SQL\nAS\n$$\nBEGIN\n\nINSERT INTO Analytics.CustomerOrders\nSELECT\n    so.SalesOrderID,\n    so.orderdate,\n    so.DueDate,\n    so.ShipDate,\n    so.PurchaseOrderNumber,\n    so.AccountNumber,\n    so.TotalDue,\nso.rowguid,\nc.Title,\nc.FirstName,\nc.LastName,\nc.CompanyName,\nc.EmailAddress,\nso.LineTotal,\np.name,\np.ProductNumber,\npc.Name\n  FROM raw.SalesOrder so\n    join raw.Customer c on c.CustomerID=so.CustomerID\n    join raw.Product p on p.ProductID=so.ProductID\n    join raw.ProductCategory pc on pc.ProductCategoryID=p.ProductCategoryID\nLEFT JOIN Analytics.CustomerOrders co on co.SalesOrderID=so.SalesOrderID \nWHERE co.rowguid is null; \n\n  RETURN 'Success';\nEND;\n$$;\n```\nThis SQL has set up the raw tables for us to land data into, as well as some stored procedures you’ll use later in the quickstart.\n\nThe result should look like this screenshot, with tables, stored procedure, and Schemas: \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/SnowflakeSchema.png)\n\n\u003C!-- ------------------------ --\u003E\n## Set Up Azure Environment\n\n### Set Up Azure SQL with AdventureWorks Data\n\nNow you need to set up your Azure SQL instance.  When you create it you can include a sample Adventure Works dataset, this will represent your transactional sales database.\n\n1. Select create \u003E Search Martkerplace for Azure SQL \u003E Click Create \u003E Under SQL Databases select Single Database and click Create\n   * Database Name: ADFdemo\n2. Server (Select create new): Give a unique server name, enable SQL authentication for this quickstart and enter an admin login and password. \n   * Go to Networking, for Connectivity method select Public Endpoint, select yes for both Allow Azure Services and Add Current client IP.\n   * No changes in Networking or Security\n3. Under Additional Settings, Use Existing Data: Select Sample\n   * Select Create\n\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/SQL.png)\n\n\n\n\n\n### Set Up ADF Workspace\n\nHead back to the Azure Marketplace and search for Data Factory.  \nSelect Create \u003E Enter a Name, your preferred region, and V2 for the Instance Details.  \nCreate with the default settings.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/ManagedID.png)\n\n### Set up Container for Staging\n\nBehind the scenes, ADF will use Blob Storage to stage data as it is moved.  So you need to have a Blob Storage container available.\n\n1. From the Marketplace, search for Storage Account and create.\n   * Give the Account a unique name\n   * Keep all the default settings the same and Create\n\n(Note: you are leaving the Networking open to public access for this Quickstart, be sure to follow your organization's security best practices for non-demo projects)\n\n2. Once created select Go to Resource\n   * On the left select Containers\n   * Add a Container named adfdemo\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Containers.png)\n\n3. You’ll need to generate a SAS token for ADF to access the container.\n   * On the left panel select Shared Access Signature\n   * Ensure all resource types are available\n   * Make the expiration End Date long enough to finish the Quickstart\n   * Generate SAS and connection string, then copy out the SAS token\n(Note: Be sure to save it or leave this tab open, you won’t be able to get back to the token)\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/SAS.png)\n\n\u003C!-- ------------------------ --\u003E\n## Configure the Linked Services\n\nNow you want to get into the ADF Studio.  Go to the ADF resource you created and click Launch Studio from the Overview tab.  This will open up the Data Factory Studio.\n\nOn the Left panel, click on the Manage tab, and then linked services.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/LinkedServices.png)\n\n\nLinked Services act as the connection strings to any data sources or destinations you want to interact with. In this case you want to set up services for Azure SQL, Snowflake, and Blob Storage.\n\n1. Click Create Linked Services \u003E Select Azure SQL Database \u003E Continue\n   * Add a Name, select all the subscription information for your Azure SQL database\n   * Enter your SQL authentication username and password setup previously\n   * Test connection and create\n\n2. Click Create Linked Services \u003E Select Azure Blob Storage\u003E Continue\n   * Set Authentication type to SAS URI and enter the information created in previous steps\n   * The URL should be just the base URL: such as AccountName.blob.core.windows.net\n\n3. Click Create Linked Services \u003E Select Snowflake \u003E Continue\n   * Fill out the details for your account, note the format of the account name\n   * The database and warehouse are both ADFDEMO\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/LinkedServices2.png)\n\n\u003C!-- ------------------------ --\u003E\n## Create Source & Sink Datasets\n\nNext you need to create datasets that map the data inside the linked services.  In this case you'll need to set up 5 source datasets for the Azure SQL tables, and 5 sink datasets for the Snowflake tables.\n\nNavigate to Author on the side menu, under the three dot menu on Datasets select New Dataset. \n   * Select Azure SQL database, name it AzureSQL_Product and enter your linked server to azure SQL\n   * Then set the table name to SalesLT.Product\n\nRepeat this process for the following:\n   * AzureSQL_ProductCategory\n   * AzureSQL_Customer\n   * AzureSQL_SalesOrderHeader\n   * AzureSQL_SalesOrderDetail\n\nNow you want to set up the Sink datasets in Snowflake.\n   * Create a new dataset, but this time select Snowflake\n   * Name the dataset Snowflake_Product\n   * Set the linked service to the Snowflake one you created.  And set the tablename to Raw.Product\n\nAgain repeat this process for the following:\n   * Snowflake_ProductCategory\n   * Snowflake_Customer\n   * Snowflake_SalesOrder\n\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/DataSets.png)\n\n\u003C!-- ------------------------ --\u003E\n## Create the Pipeline\n\nNow it’s time to set up the pipeline that will control the flow of the data.\n\n### Copy Data\n\nYou'll start with a Copy Data Activity to directly copy the data from source to sink, with no transformation.  Transformations can be done later in Snowflake, which is an ELT process.\n\n1. Under Move and Transform, select a CopyData activity.\n   * Name the CopyData Activity as Product. \n   * Under the Source tab, select AzureSql_Product.\n   * Under the Sink tab, select Snowflake_Product. In the pre-copy script enter “Truncate table Raw.Product;”. This SQL will clear what's in the raw staging in Snowflake each time data is loaded.\n   * Under the Settings tab, toggle Enable Staging and set the Staging account to the Blob Storage setup previously.\n\n2. You can copy and paste the Activity to create two more CopyData activities.\n   * For the first, update the name to ProductCategory, and update the source and sink to the product category tables. Update the Sink truncate command to Raw.ProductCategory.\n   * For the second, do the same with Customer.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Pipelines.png)\n\n\n### Data Flow\n\nNow add a Data Flow Activity. Data Flows are a traditional ETL task. You can perform transformations on the data in the middle of the data movement.  If you are familiar with SSIS, data flows have similar functionality. In this case you are going to merge the Sales Order Header and Detail into a single table in Snowflake.\n\n- Name the activity SalesOrders.\n- Under Settings select New Data flow.\n- Under the Properties set the Name to SalesOrder DataFlow.\n- At the top, select Data Flow Debug, and OK.  This will create a Spark cluster for debugging.\n- Add a Source.  Name the output stream SalesOrderHeader and Select the dataset as AzureSQL_SalesOrderHeader.  Under projection click Import projection to get the field metadata.\n- Add a second source. Name the output stream SalesOrderDetail and Select the dataset as AzureSQL_SalesOrderDetail.  Under projection click Import projection to get the field names.\n- Select the small + to the right of the SalesOrderHeader task, and add Join.\n- Set the left stream to SalesOrderHeader, and the right to SalesOrderDetail.\n- Under join conditions set both the left and right to SalesOrderID.\n- Click the + and add in a select task.  Since there are some columns in both tables with the same name, the skip duplicate input columns will just use the SalesOrderHeader columns.\n- Select + and add a sink.  Name it Snowflake, set the dataset to Snowflake_SalesOrder. Under mapping uncheck Auto Mapping so it uses the fields from the select step. You may need to open the Dataset and Import schema to refresh the metadata if the mapping does not look correct.\n\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/DataFlows.png)\n\n\n### Script Tasks\n\nIn order to push compute to Snowflake, you can add in script activities to call queries or stored procedures. In this step you can transform the data however you need to, using SQL or python via Snowpark.\n\nBack in the pipeline, under the General Activities, add in a Script Activity. \n\n Name the activity Update CustomerOrders, under Settings add in a query for \n    ```CALL Analytics.InsertCustomerOrders();\n    ```\n\nNow orchestrate the order of execution by connecting activities with the Success connector.\n\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Debug.png)\n\n\nBy clicking the Validate and Debug you can perform a test run of the entire pipeline.\n\nThen go into your Snowflake instance and validate that the tables have data.\n\n\u003C!-- ------------------------ --\u003E\n## Triggers and Monitors\n\nADF is designed to be an automated pipeline orchestrator. \n\n* At the top of the pipeline click Add Trigger, and New/Edit.\n* Set Recurrence to Every 1 Day.\n* Set the time to whatever works best for your testing.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Triggers.png)\n\nNow publish all objects created by clicking on Publish all at the top.\n\nOnce the trigger has activated and the pipeline is running, you can monitor the progress of all runs in the Monitor tab on the left menu.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-azure-data-factory-and-snowflake/Monitor.png)\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You've successfully set up a data pipeline ingesting data from Azure SQL to Snowflake, using CopyData, DataFlows, and Script activities. You now have bulk data ingestion occuring at a scheduled interval, and your AdventureWorks retail data is prepared for analytics, machine learning, and data applications.\n\n### What You Learned\n- How to deploy an Azure SQL with Adventure Works sample data\n- Set up an Azure Data Factory pipeline with linked services and datasets\n- Utilize Copy Data, Data Flow, and Script activities using both SQL and SnowPark Python within ADF\n\n### Related Resources\n\nThis Quickstart is just that, a quick way to get you started with ADF and Snowflake. You will want to consider the additional items below for enterprise-grade workloads:\n- [Official Documentation](https://learn.microsoft.com/en-us/azure/data-factory/connector-snowflake?tabs=data-factory)\n- [Common Troubleshooting](https://learn.microsoft.com/en-us/azure/data-factory/connector-troubleshoot-snowflake)\n- [Using Private Endpoints](https://community.snowflake.com/s/article/How-to-set-up-a-managed-private-endpoint-from-Azure-Data-Factory-or-Synapse-to-Snowflake)\n- [Key-Pair authentication](https://medium.com/snowflake/azure-data-factory-connecting-to-snowflake-using-keypair-authentication-906000506345)\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-99d7533ea2","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-06e9a75a6a","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-10a33b73c1","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-0cf2c16a87","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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