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------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis guide will take you through a scenario of using Snowflake's Tasks and Streams capabilities to ingest a stream of data and prepare for analytics.\u003C/p\u003E\n","\u003Cp\u003EStreams provides a change tracking mechanism for your tables and views, enabling and ensuring "exactly once" semantics for new or changed data.\u003C/p\u003E\n","\u003Cp\u003ETasks are Snowflake objects to execute a single command, which could be simple SQL command or calling an extensive stored procedure.  Tasks can be scheduled or run on-demand, either within a Snowflake Virtual warehouse or serverless.\u003C/p\u003E\n","\u003Cp\u003EThis Lab will also construct a Directed Acyclic Graph (DAG), which is a series of tasks composed of a single root task and additional tasks, organized by their dependencies. Tasks will be combined with table streams to create data pipelines, continuous ELT workflows to process recently received or changed table rows.\u003C/p\u003E\n","\u003Cp\u003EA simulated streaming datafeed will be used for this exercise, using a Snowpark-based Stored Procedure, to simplify your setup and focus on these two capabilities.  The simulation will be high-volume, at 1 million transactions a minute (exceeding 15k/second), of credit card purchases and returns.\nThis prerequisite streaming ingestion was modeled to mirror one created from Snowflake's Kafka Connector, without the distraction of having to setup a running Kafka instance.\u003Cbr\u003E\nWhile not covered in this exercise, one can use these building blocks to further enrich your data with Snowflake Marketplace data, train and deploy machine learning models, perform fraud detection, and other use cases by combining these skills with other Snowflake virtual hands-on labs.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowflake, basic SQL knowledge, Snowsight UI and Snowflake objects\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003Ehow to create a Snowflake Stream\u003C/li\u003E\u003Cli\u003Ehow to create and schedule a Snowflake Task\u003C/li\u003E\u003Cli\u003Ehow to orchestrate tasks into data pipelines\u003C/li\u003E\u003Cli\u003Ehow Snowpark can be used to build new types of user-defined functions and stored procedures\u003C/li\u003E\u003Cli\u003Emultiple ways to manage and monitor your Snowflake tasks and data pipelines\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n","\u003Cp\u003ETo participate in the virtual hands-on lab, attendees need the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&lab=getStartedWithStreamsAndTasks&utm_cta=developer-guides\"\u003ESnowflake Enterprise Account on preferred AWS region\u003C/a\u003E with \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E access\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake database that contains all data and objects built in this lab\u003C/li\u003E\u003Cli\u003EA Stage and Staging table to initially land your incoming data stream\u003C/li\u003E\u003Cli\u003EAn Analytics-Ready Table\u003C/li\u003E\u003Cli\u003EA Stream to track recently-received credit card transactions\u003C/li\u003E\u003Cli\u003ETasks orchestrated two ways to process those new transactions\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting up Snowflake\u003C/h2\u003E\n","\u003Ch3\u003ELogin and Setup Lab\u003C/h3\u003E\n","\u003Cp\u003ELog into your Snowflake account or \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&lab=getStartedWithStreamsAndTasks&utm_cta=developer-guides\"\u003Esignup for a free trial\u003C/a\u003E. You can access the SQL commands we will execute throughout this lab directly in your Snowflake account by setting up your environment below:\u003C/p\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://app.snowflake.com/resources/labs/getStartedWithStreamsAndTasks\"\u003ESetup Lab Environment\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Cp\u003EThis will create worksheets containing the lab SQL that can be executed as we step through this lab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/setup_lab.png\" alt=\"setup_lab\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce the lab has been setup, it can be continued by revisiting the \u003Ca href=\"https://app.snowflake.com/resources/labs/getStartedWithStreamsAndTasks\"\u003Elab details page\u003C/a\u003E and clicking \u003Ccode\u003EContinue Lab\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/continue_lab.png\" alt=\"continue_lab\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eor by navigating to Worksheets and selecting the \u003Ccode\u003EGetting Started with Streams & Tasks\u003C/code\u003E folder.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/worksheets.png\" alt=\"worksheets\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESet your Role\u003C/h3\u003E\n","\u003Cp\u003EFinally, switch to the ACCOUNTADMIN role.  If you just created an evaluation account to go through this Lab, this should be easy.  However, if you are using an established account and find this role missing from your list, you may need assistance to complete the next few steps.  Creating a Role, Database, Stages, Tasks, and monitoring tasks executed by 'System' requires higher-level permissions.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image2-4.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBegin Construction\u003C/h2\u003E\n","\u003Cp\u003ECreate Foundational Snowflake Objects for this Hands-on Lab\u003C/p\u003E\n","\u003Ch3\u003Ea)  Create a new role for this Lab and grant permissions\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role ACCOUNTADMIN;\nset myname = current_user();\ncreate role if not exists VHOL;\ngrant role VHOL to user identifier($myname);\ngrant create database on account to role VHOL;\ngrant EXECUTE TASK, EXECUTE MANAGED TASK on ACCOUNT to role VHOL;\ngrant IMPORTED PRIVILEGES on DATABASE SNOWFLAKE to role VHOL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eb)  Create a Dedicated Virtual Compute Warehouse\u003C/h3\u003E\n","\u003Cp\u003ESize XS, dedicated for this Hands-on Lab\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace warehouse VHOL_WH WAREHOUSE_SIZE = XSMALL, AUTO_SUSPEND = 5, AUTO_RESUME= TRUE;\ngrant all privileges on warehouse VHOL_WH to role VHOL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ec)  Create Database used throughout this Lab\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role VHOL;\ncreate or replace database VHOL_ST;\ngrant all privileges on database VHOL_ST to role VHOL;\nuse database VHOL_ST;\nuse schema PUBLIC;\nuse warehouse VHOL_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ed)  Create an internal Stage\u003C/h3\u003E\n","\u003Cp\u003EDedicated for incoming streaming files (Typically real-time stream consumption would be automated from Kafka using Snowflake Kafka Connector, but for this exercise we are simulating data and focusing on Task and Stream usage.).  Incoming data will JSON format, with many transactions within each file.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace stage VHOL_STAGE\nFILE_FORMAT = ( TYPE=JSON,STRIP_OUTER_ARRAY=TRUE );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ed)  Create a Staging/Landing Table\u003C/h3\u003E\n","\u003Cp\u003EWhere all incoming data will land initially.  Each row will contain a transaction, but JSON will be stored as a VARIANT datatype within Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace table CC_TRANS_STAGING (RECORD_CONTENT variant);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESimulated Stream Source\u003C/h2\u003E\n","\u003Cp\u003ECreate Simulation Data Generation Stored Procedure (Using Snowpark Java).  We kept this as a separate step, as it is necessary for setup, but deep interrogation of how this Stored Procedure works to use Snowflake as a streaming ingestion process is not the focus of this Lab.\nJust copy, paste and run this to create the Stored Procedure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace procedure SIMULATE_KAFKA_STREAM(mystage STRING,prefix STRING,numlines INTEGER)\n  RETURNS STRING\n  LANGUAGE JAVA\n  PACKAGES = ('com.snowflake:snowpark:latest')\n  HANDLER = 'StreamDemo.run'\n  AS\n  $$\n    import com.snowflake.snowpark_java.Session;\n    import java.io.*;\n    import java.util.HashMap;\n    public class StreamDemo {\n      public String run(Session session, String mystage,String prefix,int numlines) {\n        SampleData SD=new SampleData();\n        BufferedWriter bw = null;\n        File f=null;\n        try {\n            f = File.createTempFile(prefix, ".json");\n            FileWriter fw = new FileWriter(f);\n\t        bw = new BufferedWriter(fw);\n            boolean first=true;\n            bw.write("[");\n            for(int i=1;i<=numlines;i++){\n                if (first) first = false;\n                else {bw.write(",");bw.newLine();}\n                bw.write(SD.getDataLine(i));\n            }\n            bw.write("]");\n            bw.close();\n            return session.file().put(f.getAbsolutePath(),mystage,options)[0].getStatus();\n        }\n        catch (Exception ex){\n            return ex.getMessage();\n        }\n        finally {\n            try{\n\t            if(bw!=null) bw.close();\n                if(f!=null && f.exists()) f.delete();\n\t        }\n            catch(Exception ex){\n\t            return ("Error in closing:  "+ex);\n\t        }\n        }\n      }\n      \n      private static final HashMap<String,String> options = new HashMap<String, String>() {\n        { put("AUTO_COMPRESS", "TRUE"); }\n      };\n      \n      // sample data generator (credit card transactions)\n    public static class SampleData {\n      private static final java.util.Random R=new java.util.Random();\n      private static final java.text.NumberFormat NF_AMT = java.text.NumberFormat.getInstance();\n      String[] transactionType={"PURCHASE","PURCHASE","PURCHASE","PURCHASE","PURCHASE","PURCHASE","PURCHASE","PURCHASE","PURCHASE","PURCHASE","REFUND"};\n      String[] approved={"true","true","true","true","true","true","true","true","true","true","false"};\n      static {\n        NF_AMT.setMinimumFractionDigits(2);\n        NF_AMT.setMaximumFractionDigits(2);\n        NF_AMT.setGroupingUsed(false);\n      }\n      \n      private static int randomQty(int low, int high){\n        return R.nextInt(high-low) + low;\n      }\n      \n      private static double randomAmount(int low, int high){\n        return R.nextDouble()*(high-low) + low;\n      }\n      \n      private String getDataLine(int rownum){\n        StringBuilder sb = new StringBuilder()\n            .append("{")\n            .append("\\"element\\":"+rownum+",")\n            .append("\\"object\\":\\"basic-card\\",")\n            .append("\\"transaction\\":{")\n            .append("\\"id\\":"+(1000000000 + R.nextInt(900000000))+",")\n            .append("\\"type\\":"+"\\""+transactionType[R.nextInt(transactionType.length)]+"\\",")\n            .append("\\"amount\\":"+NF_AMT.format(randomAmount(1,5000)) +",")\n            .append("\\"currency\\":"+"\\"USD\\",")\n            .append("\\"timestamp\\":\\""+java.time.Instant.now()+"\\",")\n            .append("\\"approved\\":"+approved[R.nextInt(approved.length)]+"")\n            .append("},")\n            .append("\\"card\\":{")\n                .append("\\"number\\":"+ java.lang.Math.abs(R.nextLong()) +"")\n            .append("},")\n            .append("\\"merchant\\":{")\n            .append("\\"id\\":"+(100000000 + R.nextInt(90000000))+"")\n            .append("}")\n            .append("}");\n        return sb.toString();\n      }\n    }\n}\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhich will return:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image4-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EDevelop and Testing\u003C/h2\u003E\n","\u003Ch3\u003Ea)  Call SP to generate the compressed JSON load file\u003C/h3\u003E\n","\u003Cp\u003ELater, this will be setup to run repetitively on a schedule to simulate a real-time stream ingestion process.  First, we run the stored procedure on-demand using:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecall SIMULATE_KAFKA_STREAM('@VHOL_STAGE','SNOW_',1000000);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhich returns:\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003Eb)  Verify file was created in the internal stage\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Elist @VHOL_STAGE PATTERN='.*SNOW_.*';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis file looks similar to this in your Stage:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003Ec)  Load file into Staging Table  (about 100Mb raw json data per file).  Later, this will be setup to run every x minutes.\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecopy into CC_TRANS_STAGING from @VHOL_STAGE PATTERN='.*SNOW_.*';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhich will return this:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003Ed)  Now, there should be raw source JSON data in our Landing/Staging Table, but now a VARIANT datatype.\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from CC_TRANS_STAGING;\nselect * from CC_TRANS_STAGING limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESelect one of the rows, and you will see a better view of the contents, which will be similar to:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-4.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003Ee) Run Test Queries.  Now that it is a VARIANT datatype, the contents of the JSON is now understood.\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect RECORD_CONTENT:card:number as card_id from CC_TRANS_STAGING limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect\nRECORD_CONTENT:card:number::varchar,\nRECORD_CONTENT:merchant:id::varchar,\nRECORD_CONTENT:transaction:id::varchar,\nRECORD_CONTENT:transaction:amount::float,\nRECORD_CONTENT:transaction:currency::varchar,\nRECORD_CONTENT:transaction:approved::boolean,\nRECORD_CONTENT:transaction:type::varchar,\nRECORD_CONTENT:transaction:timestamp::datetime\nfrom CC_TRANS_STAGING\nwhere RECORD_CONTENT:transaction:amount::float < 600 limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhile the raw data was JSON, Snowflake has converted that during the COPY INTO step, creating VARIANT column values, making semi-structured data much easier\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-5.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003Ef)  Create a View of Staging Table for real-time operational queries (normalize VARIANT column to a full tabular representation)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace view CC_TRANS_STAGING_VIEW (card_id, merchant_id, transaction_id, amount, currency, approved, type, timestamp ) as (\nselect\nRECORD_CONTENT:card:number::varchar card_id,\nRECORD_CONTENT:merchant:id::varchar merchant_id,\nRECORD_CONTENT:transaction:id::varchar transaction_id,\nRECORD_CONTENT:transaction:amount::float amount,\nRECORD_CONTENT:transaction:currency::varchar currency,\nRECORD_CONTENT:transaction:approved::boolean approved,\nRECORD_CONTENT:transaction:type::varchar type,\nRECORD_CONTENT:transaction:timestamp::datetime timestamp\nfrom CC_TRANS_STAGING);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eg) We will be creating a stream, so we need to enable change tracking\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter table CC_TRANS_STAGING set CHANGE_TRACKING = true;\nalter view CC_TRANS_STAGING_VIEW set CHANGE_TRACKING = true;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eh) Preview your View\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from CC_TRANS_STAGING_VIEW limit 10;\nselect count(*) from CC_TRANS_STAGING_VIEW limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhile data in staging table is JSON, one can now see traditional tablular view of the data\u003C/p\u003E\n","\u003Ch3\u003Ei) Create a Stream on the operational view\u003C/h3\u003E\n","\u003Cp\u003ENote:  While we could put a stream on the staging table, by using the view one can use the normalized view\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace stream CC_TRANS_STAGING_VIEW_STREAM on view CC_TRANS_STAGING_VIEW SHOW_INITIAL_ROWS=true;\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\nselect * from CC_TRANS_STAGING_VIEW_STREAM limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote:  We are populating the Stream with the all current rows in the table.  Querying the Stream does not consume it.  But, something that is in a transaction (auto or manual committed) will consume the records in the stream for exactly-once change pattern.\u003C/p\u003E\n","\u003Ch3\u003Ej) Create Analytical table (normalized) for transformation (ELT) and easier user consumption (Staging table for landing can then be purged after X days)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace table CC_TRANS_ALL (\ncard_id varchar,\nmerchant_id varchar,\ntransaction_id varchar,\namount float,\ncurrency varchar,\napproved boolean,\ntype varchar,\ntimestamp datetime);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECreate Data Pipeline #1\u003C/h2\u003E\n","\u003Cp\u003ECreate Tasks to orchestrate Processing for this Hands-on Lab.  Each Task will be independent and separately scheduled.\u003C/p\u003E\n","\u003Cp\u003ESee diagram:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/pipeline1.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFirst task will create an incoming JSON file containing credit card transactions and store this file in the Stage.\u003C/li\u003E\u003Cli\u003ESecond task will perform a COPY INTO operation, reading file(s) within the Stage and load into staging/landing table\u003C/li\u003E\u003Cli\u003EThird task will perform an ELT transformation operation, processing recently-added records to the staging/landing table and load into the "analytics-ready" table.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003Ea)  Create Task\u003C/h3\u003E\n","\u003Cp\u003ETask be our real-time kafka streaming source (calling Stored Procedure to simulate incoming Kafka-provided credit card transactions).  This task will be scheduled to run every 60 seconds, very similar to how Snowflake's Kafka Connector bundles and ingests data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace task GENERATE_TASK\nWAREHOUSE=VHOL_WH\nSCHEDULE = '1 minute'\nCOMMENT = 'Generates simulated real-time data for ingestion'\nas\ncall SIMULATE_KAFKA_STREAM('@VHOL_STAGE','SNOW_',1000000);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eb)  View Definition of Task\u003C/h3\u003E\n","\u003Cp\u003EHere are the details of your newly-created Task\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Edescribe task GENERATE_TASK;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ec)  Manually Run Task\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eexecute task GENERATE_TASK;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ed) Monitor our Activities\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ERight-Click on "House icon" at upper right and open in a new browser tab:\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-1.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003Eand then\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-1_1.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFrom that new tab, select Activity" on the left menu, then "Query History":\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-2.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERemove filters at the top of this table, including your username, as later scheduled tasks will run as "System":\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-3.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick "Filter", and add filter option 'Queries executed by user tasks' and click "Apply Filters":\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-4.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow, you can see all executed SQL commands, including those executed by your running tasks:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-5.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you don't see your tasks, make sure you are ACCOUNTADMIN in this tab/window too (or same role if not ACCOUNTADMIN but have rights to see SYSTEM-executed tasks).\nFinally, click on your Worksheet tab leaving your Query History tab available as you will want to return and jump between these two Snowsight views.\u003C/p\u003E\n","\u003Ch3\u003Ee)  Enable Task to Run on its Schedule\u003C/h3\u003E\n","\u003Cp\u003ETask scheduled to run every 1 minute\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task GENERATE_TASK RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ef)  List Files in Stage\u003C/h3\u003E\n","\u003Cp\u003EList Files in Stage now ready to be copied into Snowflake.\nNOTE:  Task will not run instantly, it will wait a minute before running\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Elist @VHOL_STAGE PATTERN='.*SNOW_.*';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eg)  Wait\u003C/h3\u003E\n","\u003Cp\u003EWait a couple minutes and then you should see that the Task is regularly generating and adding a new file to the Stage.\nReminder:  Remember to turn these Tasks off at end of lab or, if you take a break, jump to the last step of this section for the commands to suspend your tasks.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Elist @VHOL_STAGE PATTERN='.*SNOW_.*';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eh)  Create a Second Task\u003C/h3\u003E\n","\u003Cp\u003EThis Task will run every 3 minutes and utilizes the Staging Stream that identifies newly added records to the Staging file and loads into our analytical table.  As there will be multiple files ready for ingestion, note each will be loaded independently and in parallel.\u003C/p\u003E\n","\u003Cp\u003EFor this Task, we will utilize the option to run with a serverless compute, as a full compute warehouse is not really necessary to have running for these small continuous micro-batch tasks.  In an actual implementation, the incoming simulated data stream will be external.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace task PROCESS_FILES_TASK\nUSER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE = 'XSMALL'\nSCHEDULE = '3 minute'\nCOMMENT = 'Ingests Incoming Staging Datafiles into Staging Table'\nas\ncopy into CC_TRANS_STAGING from @VHOL_STAGE PATTERN='.*SNOW_.*';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote:  Snowflake does track what files in a stage it has already processed for a period of time, but, if you get thousands and thousands of files in your stage, you don't want this process to sort through them for each run of the Task. Using "PURGE" is a way to delete them during the COPY INTO step, or you can create a separate step to archive or delete these files on your preferred schedule.\u003C/p\u003E\n","\u003Ch3\u003Ei)  Check Staging Table, View, and Stream\u003C/h3\u003E\n","\u003Cp\u003EQuery Staging Table and its Stream tracking changes, before running Process Task.  Note before and after row counts.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from CC_TRANS_STAGING;\nselect * from CC_TRANS_STAGING limit 10;\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\nselect * from CC_TRANS_STAGING_VIEW_STREAM limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ej)  Execute Task Manually\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eexecute task PROCESS_FILES_TASK;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWait here for processing (Can monitor from your Query History tab)\u003C/p\u003E\n","\u003Ch3\u003Ek)  Query Staging View and its Stream\u003C/h3\u003E\n","\u003Cp\u003EStreams make it so easy to track changes, after running Process Task:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from CC_TRANS_STAGING_VIEW;\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003El)  Begin task to run on its schedule\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task PROCESS_FILES_TASK resume;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Em)  Create Third Task\u003C/h3\u003E\n","\u003Cp\u003EThis task will run every 4 minutes, leveraging the Staging Stream to process new records and load into the Analytical Table.\nNote that this first checks if there are records in the stream and then will process and load records if the stream has records.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace task REFINE_TASK\nUSER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE = 'XSMALL'\nSCHEDULE = '4 minute'\nCOMMENT = '2.  ELT Process New Transactions in Landing/Staging Table into a more Normalized/Refined Table (flattens JSON payloads)'\nwhen\nSYSTEM$STREAM_HAS_DATA('CC_TRANS_STAGING_VIEW_STREAM')\nas\ninsert into CC_TRANS_ALL (select                     \ncard_id, merchant_id, transaction_id, amount, currency, approved, type, timestamp\nfrom CC_TRANS_STAGING_VIEW_STREAM);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EStaging tables are typically periodically purged.  This Table will be permanent, storing all transactions for their useful lifecycle.  This ELT process would also typically include more transformations, validations, and refine/enrichment however that is not the focus of this hands-on Lab.\u003C/p\u003E\n","\u003Ch3\u003En)  Query Analytical Table\u003C/h3\u003E\n","\u003Cp\u003EYou should see there are no records in this empty table\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from CC_TRANS_ALL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eo)  Query Staging Table's Stream\u003C/h3\u003E\n","\u003Cp\u003EStream is showing there are records and has not yet been flushed/consumed.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ep)  Run Task Manually\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eexecute task REFINE_TASK;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWait here for task to complete, check Query History view to monitor.\u003C/p\u003E\n","\u003Ch3\u003Eq)  Query Staging Table's Stream\u003C/h3\u003E\n","\u003Cp\u003EStream now showing stream after it has been flushed/consumed\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from CC_TRANS_ALL;\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote: The Stream will first show just the initial records (1,000,000).  Once those are consumed for the initial rows, then the stream will show the rows added by the scheduled tasks.\u003C/p\u003E\n","\u003Ch3\u003Er)  Schedule Task\u003C/h3\u003E\n","\u003Cp\u003EResume Task so it will run on its schedule.  Note it is fully independent of the other tasks we have running.  You can also configure tasks to run at a certain time, using CRON-based syntax option.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task REFINE_TASK resume;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Es)  Reporting\u003C/h3\u003E\n","\u003Cp\u003EOne can report on LOAD metadata generated by Snowflake.  This is available in two views:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFor no latency, but only 14 days of retention:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from VHOL_ST.INFORMATION_SCHEMA.LOAD_HISTORY where SCHEMA_NAME=current_schema() and TABLE_NAME='CC_TRANS_STAGING';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EUp to 90 minutes latency with this view, but 365 days of retention:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from SNOWFLAKE.ACCOUNT_USAGE.LOAD_HISTORY where SCHEMA_NAME=current_schema() and TABLE_NAME='CC_TRANS_STAGING';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Et)  Stop Tasks\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task REFINE_TASK SUSPEND;\nalter task PROCESS_FILES_TASK SUSPEND;\nalter task GENERATE_TASK SUSPEND;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eu)  See how many transactions we have fully processed so far\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from CC_TRANS_ALL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECreate Data Pipeline #2\u003C/h2\u003E\n","\u003Cp\u003EThis section's Data Pipeline will be very similar to the first, except will orchestrate tasks with dependencies, rather than being independently scheduled and executed.\u003C/p\u003E\n","\u003Cp\u003ESee diagram:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/pipeline2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003Ea)  Create Two Tasks\u003C/h3\u003E\n","\u003Cp\u003ESimilar to the tasks created in the previous section, but ones that will become subtasks (rather than independent scheduling).\nNote: Define all of these to run within the same warehouse, but that is not a requirement.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFirst Task\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace task REFINE_TASK2\nWAREHOUSE=VHOL_WH\nas\ninsert into CC_TRANS_ALL (select                     \ncard_id, merchant_id, transaction_id, amount, currency, approved, type, timestamp\nfrom CC_TRANS_STAGING_VIEW_STREAM);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ESecond Task\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace task PROCESS_FILES_TASK2\nWAREHOUSE=VHOL_WH\nas\ncopy into CC_TRANS_STAGING from @VHOL_STAGE PATTERN='.*SNOW_.*';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eb)  Create Root Task\u003C/h3\u003E\n","\u003Cp\u003EThis is the Task where all others will be subprocesses of.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace task LOAD_TASK\nWAREHOUSE=VHOL_WH\nSCHEDULE = '1 minute'\nCOMMENT = 'Full Sequential Orchestration'\nas\ncall SIMULATE_KAFKA_STREAM('@VHOL_STAGE','SNOW_',1000000);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ec)  First Predecessor\u003C/h3\u003E\n","\u003Cp\u003E-Have task REFINE_TASK2 be a predecessor of PROCESS_FILES_TASK2\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task REFINE_TASK2 add after PROCESS_FILES_TASK2;\nalter task REFINE_TASK2 RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E-Have task LOAD_TASK be a predecessor of PROCESS_FILES_TASK2\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task PROCESS_FILES_TASK2 add after LOAD_TASK;\nalter task PROCESS_FILES_TASK2 RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote:  One can also use the 'after' within the create task command\u003C/p\u003E\n","\u003Ch3\u003Ed)  View DAG Orchestration\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EIn a new tab (use right-click on "home icon" at upper left), Navigate in Snowsight to:\u003Cbr\u003E\n\u003Cstrong\u003EData>Databases>VHOL_ST>PUBLIC>Tasks>LOAD_TASK\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EReview "Task Details" tab:\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image7-1.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EReview "Graph" to see graphical representation of our simple flow:\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image7-2.png\" alt=\"\"\u003E\nRemember your warehouse is 'VHOL_WH'\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EReview "Run History" to view Task Executions:\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image7-3.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENote:  Graph and Task History are recently added previews, so may need to ask on how to enable in your account if these menu options are missing.\u003C/p\u003E\n","\u003Ch3\u003Ee)  Start LOAD Task\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task LOAD_TASK RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWait, as task will run after one minute.  Then, see a file created in Stage\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Elist @VHOL_STAGE PATTERN='.*SNOW_.*';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGo back to Activity tab, reviewing Query History and Task's Run History (refresh)\u003C/p\u003E\n","\u003Ch3\u003Ef)  Tasks in Parallel\u003C/h3\u003E\n","\u003Cp\u003ETasks do not have to be sequential, they can also run in parallel.  Let's create a simple task to demonstrate this.  To add to the root task @ creation, you have to first suspend:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task LOAD_TASK SUSPEND;\ncreate or replace task WAIT_TASK\n  WAREHOUSE=VHOL_WH\n  after PROCESS_FILES_TASK2\nas\n  call SYSTEM$wait(1);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eg)  Run Load Task\u003C/h3\u003E\n","\u003Cp\u003EAfter this addition, resume/enable both tasks. Note we utilized the "AFTER" option during creation rather than using "ALTER" afterward to create this dependency to the DAG process.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task WAIT_TASK RESUME;\nalter task LOAD_TASK RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eh)  Monitor\u003C/h3\u003E\n","\u003Cp\u003EGo back to your Task tab, refreshing your Graph view and task's Run History.\u003C/p\u003E\n","\u003Ch3\u003Ei)  Task Dependencies\u003C/h3\u003E\n","\u003Cp\u003ECan also see task dependencies via SQL.  As this is available via query, this can be collected for your data catalog and other analysis/reporting purposes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from table(information_schema.task_dependents(task_name => 'LOAD_TASK', recursive => true));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ej)  Section is Complete\u003C/h3\u003E\n","\u003Cp\u003ESuspend your Tasks for this Section\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ealter task LOAD_TASK SUSPEND;\nalter task REFINE_TASK2 SUSPEND;\nalter task PROCESS_FILES_TASK2 SUSPEND;\nalter task WAIT_TASK SUSPEND;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EFinal Steps & Cleanup\u003C/h2\u003E\n","\u003Ch3\u003Ea)  See how many transactions we have processed\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from CC_TRANS_ALL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eb)  Look at our newest record now in your Analytical table:\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from CC_TRANS_ALL order by TIMESTAMP desc limit 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ec)  See all tasks we have created, and to confirm their state is all "Suspended"\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Eshow tasks;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E(suspend any still running)\u003C/p\u003E\n","\u003Ch3\u003Ed)    Drop Database, removing all objects created by this Hands-on Lab (Optional)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Edrop database VHOL_ST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ee)    Drop Warehouse (Optional)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E\nuse role ACCOUNTADMIN;\ndrop warehouse VHOL_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ee)    Drop Role (Optional)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role ACCOUNTADMIN;\ndrop role VHOL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ECongratulations, you have completed this Lab!\u003C/p\u003E\n","\u003Ch3\u003EWhat We Covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreated a Snowflake Stream\u003C/li\u003E\u003Cli\u003ECreated and Scheduled a Snowflake Task\u003C/li\u003E\u003Cli\u003EAssembled tasks into Data Pipelines\u003C/li\u003E\u003Cli\u003ESnowpark can be used to build new types of user-defined functions and stored procedures\u003C/li\u003E\u003Cli\u003EManaged and Monitored your Snowflake Tasks and Data Pipelines\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/streams-intro.html\"\u003EStreams\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro.html\"\u003ETasks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/execute-task.html\"\u003EExecute Tasks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-errors.html\"\u003EHandling Task Errors\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe.html\"\u003ESnowpipe\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/kafka-connector.html\"\u003EKafka Connector\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Learn how to use streams and tasks with Snowflake.","title":"Getting Started with Streams & Tasks","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nThis guide will take you through a scenario of using Snowflake's Tasks and Streams capabilities to ingest a stream of data and prepare for analytics.  \n\nStreams provides a change tracking mechanism for your tables and views, enabling and ensuring \"exactly once\" semantics for new or changed data.\n\nTasks are Snowflake objects to execute a single command, which could be simple SQL command or calling an extensive stored procedure.  Tasks can be scheduled or run on-demand, either within a Snowflake Virtual warehouse or serverless.\n\nThis Lab will also construct a Directed Acyclic Graph (DAG), which is a series of tasks composed of a single root task and additional tasks, organized by their dependencies. Tasks will be combined with table streams to create data pipelines, continuous ELT workflows to process recently received or changed table rows. \n\nA simulated streaming datafeed will be used for this exercise, using a Snowpark-based Stored Procedure, to simplify your setup and focus on these two capabilities.  The simulation will be high-volume, at 1 million transactions a minute (exceeding 15k/second), of credit card purchases and returns.\nThis prerequisite streaming ingestion was modeled to mirror one created from Snowflake's Kafka Connector, without the distraction of having to setup a running Kafka instance.  \nWhile not covered in this exercise, one can use these building blocks to further enrich your data with Snowflake Marketplace data, train and deploy machine learning models, perform fraud detection, and other use cases by combining these skills with other Snowflake virtual hands-on labs.\n\n\n### Prerequisites\n- Familiarity with Snowflake, basic SQL knowledge, Snowsight UI and Snowflake objects\n\n### What You’ll Learn \n- how to create a Snowflake Stream\n- how to create and schedule a Snowflake Task \n- how to orchestrate tasks into data pipelines\n- how Snowpark can be used to build new types of user-defined functions and stored procedures \n- multiple ways to manage and monitor your Snowflake tasks and data pipelines\n\n### What You’ll Need \nTo participate in the virtual hands-on lab, attendees need the following:\n\n- A [Snowflake Enterprise Account on preferred AWS region](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&lab=getStartedWithStreamsAndTasks&utm_cta=developer-guides) with **ACCOUNTADMIN** access\n\n### What You’ll Build \n- A Snowflake database that contains all data and objects built in this lab\n- A Stage and Staging table to initially land your incoming data stream\n- An Analytics-Ready Table\n- A Stream to track recently-received credit card transactions\n- Tasks orchestrated two ways to process those new transactions\n\n\u003C!-- ------------------------ --\u003E\n## Setting up Snowflake\n\n### Login and Setup Lab\nLog into your Snowflake account or [signup for a free trial](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&lab=getStartedWithStreamsAndTasks&utm_cta=developer-guides). You can access the SQL commands we will execute throughout this lab directly in your Snowflake account by setting up your environment below:\n\n\u003Cbutton\u003E[Setup Lab Environment](https://app.snowflake.com/resources/labs/getStartedWithStreamsAndTasks)\u003C/button\u003E\n\nThis will create worksheets containing the lab SQL that can be executed as we step through this lab.\n\n![setup_lab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/setup_lab.png)\n\nOnce the lab has been setup, it can be continued by revisiting the [lab details page](https://app.snowflake.com/resources/labs/getStartedWithStreamsAndTasks) and clicking `Continue Lab`\n\n![continue_lab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/continue_lab.png)\n\nor by navigating to Worksheets and selecting the `Getting Started with Streams & Tasks` folder.\n\n![worksheets](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/worksheets.png)\n\n### Set your Role\nFinally, switch to the ACCOUNTADMIN role.  If you just created an evaluation account to go through this Lab, this should be easy.  However, if you are using an established account and find this role missing from your list, you may need assistance to complete the next few steps.  Creating a Role, Database, Stages, Tasks, and monitoring tasks executed by 'System' requires higher-level permissions.    \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image2-4.png)\n\n\n\u003C!-- ------------------------ --\u003E\n## Begin Construction\n\nCreate Foundational Snowflake Objects for this Hands-on Lab\n### a)  Create a new role for this Lab and grant permissions\n```\nuse role ACCOUNTADMIN;\nset myname = current_user();\ncreate role if not exists VHOL;\ngrant role VHOL to user identifier($myname);\ngrant create database on account to role VHOL;\ngrant EXECUTE TASK, EXECUTE MANAGED TASK on ACCOUNT to role VHOL;\ngrant IMPORTED PRIVILEGES on DATABASE SNOWFLAKE to role VHOL;\n```\n### b)  Create a Dedicated Virtual Compute Warehouse\nSize XS, dedicated for this Hands-on Lab\n```\ncreate or replace warehouse VHOL_WH WAREHOUSE_SIZE = XSMALL, AUTO_SUSPEND = 5, AUTO_RESUME= TRUE;\ngrant all privileges on warehouse VHOL_WH to role VHOL;\n```\n\n### c)  Create Database used throughout this Lab\n```\nuse role VHOL;\ncreate or replace database VHOL_ST;\ngrant all privileges on database VHOL_ST to role VHOL;\nuse database VHOL_ST;\nuse schema PUBLIC;\nuse warehouse VHOL_WH;\n```\n\n### d)  Create an internal Stage\nDedicated for incoming streaming files (Typically real-time stream consumption would be automated from Kafka using Snowflake Kafka Connector, but for this exercise we are simulating data and focusing on Task and Stream usage.).  Incoming data will JSON format, with many transactions within each file.\n```\ncreate or replace stage VHOL_STAGE\nFILE_FORMAT = ( TYPE=JSON,STRIP_OUTER_ARRAY=TRUE );\n```\n### d)  Create a Staging/Landing Table\nWhere all incoming data will land initially.  Each row will contain a transaction, but JSON will be stored as a VARIANT datatype within Snowflake.\n```\ncreate or replace table CC_TRANS_STAGING (RECORD_CONTENT variant);\n```\n\n## Simulated Stream Source\n\nCreate Simulation Data Generation Stored Procedure (Using Snowpark Java).  We kept this as a separate step, as it is necessary for setup, but deep interrogation of how this Stored Procedure works to use Snowflake as a streaming ingestion process is not the focus of this Lab.\nJust copy, paste and run this to create the Stored Procedure:\n```\ncreate or replace procedure SIMULATE_KAFKA_STREAM(mystage STRING,prefix STRING,numlines INTEGER)\n  RETURNS STRING\n  LANGUAGE JAVA\n  PACKAGES = ('com.snowflake:snowpark:latest')\n  HANDLER = 'StreamDemo.run'\n  AS\n  $$\n    import com.snowflake.snowpark_java.Session;\n    import java.io.*;\n    import java.util.HashMap;\n    public class StreamDemo {\n      public String run(Session session, String mystage,String prefix,int numlines) {\n        SampleData SD=new SampleData();\n        BufferedWriter bw = null;\n        File f=null;\n        try {\n            f = File.createTempFile(prefix, \".json\");\n            FileWriter fw = new FileWriter(f);\n\t        bw = new BufferedWriter(fw);\n            boolean first=true;\n            bw.write(\"[\");\n            for(int i=1;i\u003C=numlines;i++){\n                if (first) first = false;\n                else {bw.write(\",\");bw.newLine();}\n                bw.write(SD.getDataLine(i));\n            }\n            bw.write(\"]\");\n            bw.close();\n            return session.file().put(f.getAbsolutePath(),mystage,options)[0].getStatus();\n        }\n        catch (Exception ex){\n            return ex.getMessage();\n        }\n        finally {\n            try{\n\t            if(bw!=null) bw.close();\n                if(f!=null && f.exists()) f.delete();\n\t        }\n            catch(Exception ex){\n\t            return (\"Error in closing:  \"+ex);\n\t        }\n        }\n      }\n      \n      private static final HashMap\u003CString,String\u003E options = new HashMap\u003CString, String\u003E() {\n        { put(\"AUTO_COMPRESS\", \"TRUE\"); }\n      };\n      \n      // sample data generator (credit card transactions)\n    public static class SampleData {\n      private static final java.util.Random R=new java.util.Random();\n      private static final java.text.NumberFormat NF_AMT = java.text.NumberFormat.getInstance();\n      String[] transactionType={\"PURCHASE\",\"PURCHASE\",\"PURCHASE\",\"PURCHASE\",\"PURCHASE\",\"PURCHASE\",\"PURCHASE\",\"PURCHASE\",\"PURCHASE\",\"PURCHASE\",\"REFUND\"};\n      String[] approved={\"true\",\"true\",\"true\",\"true\",\"true\",\"true\",\"true\",\"true\",\"true\",\"true\",\"false\"};\n      static {\n        NF_AMT.setMinimumFractionDigits(2);\n        NF_AMT.setMaximumFractionDigits(2);\n        NF_AMT.setGroupingUsed(false);\n      }\n      \n      private static int randomQty(int low, int high){\n        return R.nextInt(high-low) + low;\n      }\n      \n      private static double randomAmount(int low, int high){\n        return R.nextDouble()*(high-low) + low;\n      }\n      \n      private String getDataLine(int rownum){\n        StringBuilder sb = new StringBuilder()\n            .append(\"{\")\n            .append(\"\\\"element\\\":\"+rownum+\",\")\n            .append(\"\\\"object\\\":\\\"basic-card\\\",\")\n            .append(\"\\\"transaction\\\":{\")\n            .append(\"\\\"id\\\":\"+(1000000000 + R.nextInt(900000000))+\",\")\n            .append(\"\\\"type\\\":\"+\"\\\"\"+transactionType[R.nextInt(transactionType.length)]+\"\\\",\")\n            .append(\"\\\"amount\\\":\"+NF_AMT.format(randomAmount(1,5000)) +\",\")\n            .append(\"\\\"currency\\\":\"+\"\\\"USD\\\",\")\n            .append(\"\\\"timestamp\\\":\\\"\"+java.time.Instant.now()+\"\\\",\")\n            .append(\"\\\"approved\\\":\"+approved[R.nextInt(approved.length)]+\"\")\n            .append(\"},\")\n            .append(\"\\\"card\\\":{\")\n                .append(\"\\\"number\\\":\"+ java.lang.Math.abs(R.nextLong()) +\"\")\n            .append(\"},\")\n            .append(\"\\\"merchant\\\":{\")\n            .append(\"\\\"id\\\":\"+(100000000 + R.nextInt(90000000))+\"\")\n            .append(\"}\")\n            .append(\"}\");\n        return sb.toString();\n      }\n    }\n}\n$$;\n```\nWhich will return:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image4-1.png)\n\n\n## Develop and Testing\n\n### a)  Call SP to generate the compressed JSON load file\nLater, this will be setup to run repetitively on a schedule to simulate a real-time stream ingestion process.  First, we run the stored procedure on-demand using:  \n```\ncall SIMULATE_KAFKA_STREAM('@VHOL_STAGE','SNOW_',1000000);\n```\nWhich returns:  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-1.png)\n\n### b)  Verify file was created in the internal stage\n```\nlist @VHOL_STAGE PATTERN='.*SNOW_.*';\n```\nThis file looks similar to this in your Stage:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-2.png)\n\n### c)  Load file into Staging Table  (about 100Mb raw json data per file).  Later, this will be setup to run every x minutes.\n```\ncopy into CC_TRANS_STAGING from @VHOL_STAGE PATTERN='.*SNOW_.*';\n```\nWhich will return this:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-3.png)\n\n### d)  Now, there should be raw source JSON data in our Landing/Staging Table, but now a VARIANT datatype.\n```\nselect count(*) from CC_TRANS_STAGING;\nselect * from CC_TRANS_STAGING limit 10;\n```\nSelect one of the rows, and you will see a better view of the contents, which will be similar to:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-4.png)\n\n### e) Run Test Queries.  Now that it is a VARIANT datatype, the contents of the JSON is now understood.\n```\nselect RECORD_CONTENT:card:number as card_id from CC_TRANS_STAGING limit 10;\n```\n\n```\nselect\nRECORD_CONTENT:card:number::varchar,\nRECORD_CONTENT:merchant:id::varchar,\nRECORD_CONTENT:transaction:id::varchar,\nRECORD_CONTENT:transaction:amount::float,\nRECORD_CONTENT:transaction:currency::varchar,\nRECORD_CONTENT:transaction:approved::boolean,\nRECORD_CONTENT:transaction:type::varchar,\nRECORD_CONTENT:transaction:timestamp::datetime\nfrom CC_TRANS_STAGING\nwhere RECORD_CONTENT:transaction:amount::float \u003C 600 limit 10;\n```\nWhile the raw data was JSON, Snowflake has converted that during the COPY INTO step, creating VARIANT column values, making semi-structured data much easier\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image5-5.png)\n\n\n### f)  Create a View of Staging Table for real-time operational queries (normalize VARIANT column to a full tabular representation)\n```\ncreate or replace view CC_TRANS_STAGING_VIEW (card_id, merchant_id, transaction_id, amount, currency, approved, type, timestamp ) as (\nselect\nRECORD_CONTENT:card:number::varchar card_id,\nRECORD_CONTENT:merchant:id::varchar merchant_id,\nRECORD_CONTENT:transaction:id::varchar transaction_id,\nRECORD_CONTENT:transaction:amount::float amount,\nRECORD_CONTENT:transaction:currency::varchar currency,\nRECORD_CONTENT:transaction:approved::boolean approved,\nRECORD_CONTENT:transaction:type::varchar type,\nRECORD_CONTENT:transaction:timestamp::datetime timestamp\nfrom CC_TRANS_STAGING);\n```\n### g) We will be creating a stream, so we need to enable change tracking\n```\nalter table CC_TRANS_STAGING set CHANGE_TRACKING = true;\nalter view CC_TRANS_STAGING_VIEW set CHANGE_TRACKING = true;\n```\n### h) Preview your View\n```\nselect * from CC_TRANS_STAGING_VIEW limit 10;\nselect count(*) from CC_TRANS_STAGING_VIEW limit 10;\n```\nWhile data in staging table is JSON, one can now see traditional tablular view of the data\n\n### i) Create a Stream on the operational view\nNote:  While we could put a stream on the staging table, by using the view one can use the normalized view\n```\ncreate or replace stream CC_TRANS_STAGING_VIEW_STREAM on view CC_TRANS_STAGING_VIEW SHOW_INITIAL_ROWS=true;\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\nselect * from CC_TRANS_STAGING_VIEW_STREAM limit 10;\n```\nNote:  We are populating the Stream with the all current rows in the table.  Querying the Stream does not consume it.  But, something that is in a transaction (auto or manual committed) will consume the records in the stream for exactly-once change pattern.\n\n### j) Create Analytical table (normalized) for transformation (ELT) and easier user consumption (Staging table for landing can then be purged after X days)\n```\ncreate or replace table CC_TRANS_ALL (\ncard_id varchar,\nmerchant_id varchar,\ntransaction_id varchar,\namount float,\ncurrency varchar,\napproved boolean,\ntype varchar,\ntimestamp datetime);\n```\n## Create Data Pipeline #1\n\nCreate Tasks to orchestrate Processing for this Hands-on Lab.  Each Task will be independent and separately scheduled.\n\nSee diagram:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/pipeline1.png)\n- First task will create an incoming JSON file containing credit card transactions and store this file in the Stage.\n- Second task will perform a COPY INTO operation, reading file(s) within the Stage and load into staging/landing table\n- Third task will perform an ELT transformation operation, processing recently-added records to the staging/landing table and load into the \"analytics-ready\" table.\n\n### a)  Create Task\nTask be our real-time kafka streaming source (calling Stored Procedure to simulate incoming Kafka-provided credit card transactions).  This task will be scheduled to run every 60 seconds, very similar to how Snowflake's Kafka Connector bundles and ingests data.\n```\ncreate or replace task GENERATE_TASK\nWAREHOUSE=VHOL_WH\nSCHEDULE = '1 minute'\nCOMMENT = 'Generates simulated real-time data for ingestion'\nas\ncall SIMULATE_KAFKA_STREAM('@VHOL_STAGE','SNOW_',1000000);\n```\n\n### b)  View Definition of Task\nHere are the details of your newly-created Task\n```\ndescribe task GENERATE_TASK;\n```\n\n### c)  Manually Run Task  \n```\nexecute task GENERATE_TASK;\n```\n\n### d) Monitor our Activities\n\n- Right-Click on \"House icon\" at upper right and open in a new browser tab:    \n ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-1.png)  \n\n- and then    \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-1_1.png)\n -\n- From that new tab, select Activity\" on the left menu, then \"Query History\":     \n ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-2.png)\n\n- Remove filters at the top of this table, including your username, as later scheduled tasks will run as \"System\":\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-3.png)\n\n- Click \"Filter\", and add filter option 'Queries executed by user tasks' and click \"Apply Filters\":\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-4.png)\n\n- Now, you can see all executed SQL commands, including those executed by your running tasks:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image6-5.png)\n\nIf you don't see your tasks, make sure you are ACCOUNTADMIN in this tab/window too (or same role if not ACCOUNTADMIN but have rights to see SYSTEM-executed tasks).\nFinally, click on your Worksheet tab leaving your Query History tab available as you will want to return and jump between these two Snowsight views.\n\n\n### e)  Enable Task to Run on its Schedule\nTask scheduled to run every 1 minute\n```\nalter task GENERATE_TASK RESUME;\n```\n\n### f)  List Files in Stage\nList Files in Stage now ready to be copied into Snowflake.\nNOTE:  Task will not run instantly, it will wait a minute before running\n```\nlist @VHOL_STAGE PATTERN='.*SNOW_.*';\n```\n\n### g)  Wait\nWait a couple minutes and then you should see that the Task is regularly generating and adding a new file to the Stage.\nReminder:  Remember to turn these Tasks off at end of lab or, if you take a break, jump to the last step of this section for the commands to suspend your tasks.\n```\nlist @VHOL_STAGE PATTERN='.*SNOW_.*';\n```\n\n### h)  Create a Second Task\nThis Task will run every 3 minutes and utilizes the Staging Stream that identifies newly added records to the Staging file and loads into our analytical table.  As there will be multiple files ready for ingestion, note each will be loaded independently and in parallel.\n\nFor this Task, we will utilize the option to run with a serverless compute, as a full compute warehouse is not really necessary to have running for these small continuous micro-batch tasks.  In an actual implementation, the incoming simulated data stream will be external.\n\n```\ncreate or replace task PROCESS_FILES_TASK\nUSER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE = 'XSMALL'\nSCHEDULE = '3 minute'\nCOMMENT = 'Ingests Incoming Staging Datafiles into Staging Table'\nas\ncopy into CC_TRANS_STAGING from @VHOL_STAGE PATTERN='.*SNOW_.*';\n```\nNote:  Snowflake does track what files in a stage it has already processed for a period of time, but, if you get thousands and thousands of files in your stage, you don't want this process to sort through them for each run of the Task. Using \"PURGE\" is a way to delete them during the COPY INTO step, or you can create a separate step to archive or delete these files on your preferred schedule.\n\n### i)  Check Staging Table, View, and Stream\nQuery Staging Table and its Stream tracking changes, before running Process Task.  Note before and after row counts.\n```\nselect count(*) from CC_TRANS_STAGING;\nselect * from CC_TRANS_STAGING limit 10;\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\nselect * from CC_TRANS_STAGING_VIEW_STREAM limit 10;\n```\n\n### j)  Execute Task Manually\n```\nexecute task PROCESS_FILES_TASK;\n```\nWait here for processing (Can monitor from your Query History tab)\n\n### k)  Query Staging View and its Stream\nStreams make it so easy to track changes, after running Process Task:\n```\nselect count(*) from CC_TRANS_STAGING_VIEW;\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\n```\n\n### l)  Begin task to run on its schedule\n```\nalter task PROCESS_FILES_TASK resume;\n```\n\n### m)  Create Third Task\nThis task will run every 4 minutes, leveraging the Staging Stream to process new records and load into the Analytical Table.\nNote that this first checks if there are records in the stream and then will process and load records if the stream has records.\n```\ncreate or replace task REFINE_TASK\nUSER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE = 'XSMALL'\nSCHEDULE = '4 minute'\nCOMMENT = '2.  ELT Process New Transactions in Landing/Staging Table into a more Normalized/Refined Table (flattens JSON payloads)'\nwhen\nSYSTEM$STREAM_HAS_DATA('CC_TRANS_STAGING_VIEW_STREAM')\nas\ninsert into CC_TRANS_ALL (select                     \ncard_id, merchant_id, transaction_id, amount, currency, approved, type, timestamp\nfrom CC_TRANS_STAGING_VIEW_STREAM);\n```\nStaging tables are typically periodically purged.  This Table will be permanent, storing all transactions for their useful lifecycle.  This ELT process would also typically include more transformations, validations, and refine/enrichment however that is not the focus of this hands-on Lab.\n\n### n)  Query Analytical Table\nYou should see there are no records in this empty table\n```\nselect count(*) from CC_TRANS_ALL;\n```\n\n### o)  Query Staging Table's Stream\nStream is showing there are records and has not yet been flushed/consumed.\n```\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\n```\n\n### p)  Run Task Manually\n```\nexecute task REFINE_TASK;\n```\n\nWait here for task to complete, check Query History view to monitor.\n\n### q)  Query Staging Table's Stream\nStream now showing stream after it has been flushed/consumed\n```\nselect count(*) from CC_TRANS_ALL;\nselect count(*) from CC_TRANS_STAGING_VIEW_STREAM;\n```\n\nNote: The Stream will first show just the initial records (1,000,000).  Once those are consumed for the initial rows, then the stream will show the rows added by the scheduled tasks.\n\n### r)  Schedule Task\nResume Task so it will run on its schedule.  Note it is fully independent of the other tasks we have running.  You can also configure tasks to run at a certain time, using CRON-based syntax option.\n```\nalter task REFINE_TASK resume;\n```\n\n### s)  Reporting\nOne can report on LOAD metadata generated by Snowflake.  This is available in two views:\n1)  For no latency, but only 14 days of retention:\n```\nselect * from VHOL_ST.INFORMATION_SCHEMA.LOAD_HISTORY where SCHEMA_NAME=current_schema() and TABLE_NAME='CC_TRANS_STAGING';\n```\n\n2)  Up to 90 minutes latency with this view, but 365 days of retention:\n```\nselect * from SNOWFLAKE.ACCOUNT_USAGE.LOAD_HISTORY where SCHEMA_NAME=current_schema() and TABLE_NAME='CC_TRANS_STAGING';\n```\n\n### t)  Stop Tasks\n```\nalter task REFINE_TASK SUSPEND;\nalter task PROCESS_FILES_TASK SUSPEND;\nalter task GENERATE_TASK SUSPEND;\n```\n\n### u)  See how many transactions we have fully processed so far\n```\nselect count(*) from CC_TRANS_ALL;\n```\n\n## Create Data Pipeline #2\n\nThis section's Data Pipeline will be very similar to the first, except will orchestrate tasks with dependencies, rather than being independently scheduled and executed.\n\nSee diagram:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/pipeline2.png)\n\n### a)  Create Two Tasks\nSimilar to the tasks created in the previous section, but ones that will become subtasks (rather than independent scheduling).\nNote: Define all of these to run within the same warehouse, but that is not a requirement.\n- First Task\n```\ncreate or replace task REFINE_TASK2\nWAREHOUSE=VHOL_WH\nas\ninsert into CC_TRANS_ALL (select                     \ncard_id, merchant_id, transaction_id, amount, currency, approved, type, timestamp\nfrom CC_TRANS_STAGING_VIEW_STREAM);\n```\n- Second Task\n```\ncreate or replace task PROCESS_FILES_TASK2\nWAREHOUSE=VHOL_WH\nas\ncopy into CC_TRANS_STAGING from @VHOL_STAGE PATTERN='.*SNOW_.*';\n```\n\n### b)  Create Root Task\nThis is the Task where all others will be subprocesses of.\n```\ncreate or replace task LOAD_TASK\nWAREHOUSE=VHOL_WH\nSCHEDULE = '1 minute'\nCOMMENT = 'Full Sequential Orchestration'\nas\ncall SIMULATE_KAFKA_STREAM('@VHOL_STAGE','SNOW_',1000000);\n```\n\n### c)  First Predecessor\n-Have task REFINE_TASK2 be a predecessor of PROCESS_FILES_TASK2\n```\nalter task REFINE_TASK2 add after PROCESS_FILES_TASK2;\nalter task REFINE_TASK2 RESUME;\n```\n-Have task LOAD_TASK be a predecessor of PROCESS_FILES_TASK2\n```\nalter task PROCESS_FILES_TASK2 add after LOAD_TASK;\nalter task PROCESS_FILES_TASK2 RESUME;\n```\nNote:  One can also use the 'after' within the create task command\n\n### d)  View DAG Orchestration\n- In a new tab (use right-click on \"home icon\" at upper left), Navigate in Snowsight to:  \n  **Data\u003EDatabases\u003EVHOL_ST\u003EPUBLIC\u003ETasks\u003ELOAD_TASK**\n- Review \"Task Details\" tab:    \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image7-1.png)\n\n- Review \"Graph\" to see graphical representation of our simple flow:    \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image7-2.png)\nRemember your warehouse is 'VHOL_WH'\n\n- Review \"Run History\" to view Task Executions:   \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-streams-and-tasks/image7-3.png)\n\nNote:  Graph and Task History are recently added previews, so may need to ask on how to enable in your account if these menu options are missing.\n\n### e)  Start LOAD Task\n```\nalter task LOAD_TASK RESUME;\n```\nWait, as task will run after one minute.  Then, see a file created in Stage\n```\nlist @VHOL_STAGE PATTERN='.*SNOW_.*';\n```\nGo back to Activity tab, reviewing Query History and Task's Run History (refresh)\n\n### f)  Tasks in Parallel\nTasks do not have to be sequential, they can also run in parallel.  Let's create a simple task to demonstrate this.  To add to the root task @ creation, you have to first suspend:\n```\nalter task LOAD_TASK SUSPEND;\ncreate or replace task WAIT_TASK\n  WAREHOUSE=VHOL_WH\n  after PROCESS_FILES_TASK2\nas\n  call SYSTEM$wait(1);\n```\n\n### g)  Run Load Task\nAfter this addition, resume/enable both tasks. Note we utilized the \"AFTER\" option during creation rather than using \"ALTER\" afterward to create this dependency to the DAG process.\n```\nalter task WAIT_TASK RESUME;\nalter task LOAD_TASK RESUME;\n```\n\n### h)  Monitor\nGo back to your Task tab, refreshing your Graph view and task's Run History.\n\n### i)  Task Dependencies\nCan also see task dependencies via SQL.  As this is available via query, this can be collected for your data catalog and other analysis/reporting purposes.\n```\nselect * from table(information_schema.task_dependents(task_name =\u003E 'LOAD_TASK', recursive =\u003E true));\n```\n\n### j)  Section is Complete\nSuspend your Tasks for this Section\n```\nalter task LOAD_TASK SUSPEND;\nalter task REFINE_TASK2 SUSPEND;\nalter task PROCESS_FILES_TASK2 SUSPEND;\nalter task WAIT_TASK SUSPEND;\n```\n\n\n## Final Steps & Cleanup\n### a)  See how many transactions we have processed\n```\nselect count(*) from CC_TRANS_ALL;\n```\n### b)  Look at our newest record now in your Analytical table:\n```\nselect * from CC_TRANS_ALL order by TIMESTAMP desc limit 1;\n```\n\n### c)  See all tasks we have created, and to confirm their state is all \"Suspended\"\n```\nshow tasks;\n```\n(suspend any still running)\n\n### d)    Drop Database, removing all objects created by this Hands-on Lab (Optional)  \n```\ndrop database VHOL_ST;\n```\n\n### e)    Drop Warehouse (Optional)\n```\n\nuse role ACCOUNTADMIN;\ndrop warehouse VHOL_WH;\n```\n\n### e)    Drop Role (Optional)\n```\nuse role ACCOUNTADMIN;\ndrop role VHOL;\n```\n\n## Conclusion\n\nCongratulations, you have completed this Lab!  \n\n### What We Covered\n- Created a Snowflake Stream\n- Created and Scheduled a Snowflake Task\n- Assembled tasks into Data Pipelines\n- Snowpark can be used to build new types of user-defined functions and stored procedures\n- Managed and Monitored your Snowflake Tasks and Data Pipelines\n\n\n### Related Resources\n- [Streams](https://docs.snowflake.com/en/user-guide/streams-intro.html)\n- [Tasks](https://docs.snowflake.com/en/user-guide/tasks-intro.html)\n- [Execute Tasks](https://docs.snowflake.com/en/sql-reference/sql/execute-task.html)\n- [Handling Task Errors](https://docs.snowflake.com/en/user-guide/tasks-errors.html)\n- [Snowpipe](https://docs.snowflake.com/en/user-guide/data-load-snowpipe.html)\n- [Kafka Connector](https://docs.snowflake.com/en/user-guide/kafka-connector.html)\n\n\n\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-844c0e91b5","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-56baf996fe",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-da4d7d6552","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-2e61c9e11b","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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