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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-c97399ed30","quickstartHeroFirstCertifiedTag":{"tagText":"Community 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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-db590101b2","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-14bbb189b3",":items":{"contentfragment":{"id":"contentfragment-9da0ef954e","description":"Build real-time CDC pipelines with Snowpipe Streaming and Dynamic Tables for continuous data synchronization and transformation.","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis guide will take you through a scenario of using Snowflake's Snowpipe Streaming to ingest a simulated stream, then utilize Dynamic tables to transform and prepare the raw ingested JSON payloads into ready-for-analytics datasets.  These are two of Snowflake's powerful Data Engineering innovations for ingestion and transformation.\u003C/p\u003E\n","\u003Cp\u003EA simulated streaming datafeed will be generated for this exercise, using a java client running on your desktop, that implements the Snowpipe Streaming SDK.  The simulated datafeed will be Stock Limit Orders, with new, changed, and cancelled orders represented as RDBMS transactions logs captured from INSERT, UPDATE, and DELETE database events.  These events will be transmitted as JSON payloads and land into a Snowflake table with a variant data column.  The simulation will be high-volume, first starting at 1 million transactions in seconds and secondly as steady stream.  Finally, data will contain sensitive fields, so our process will have extra protections for this.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image0.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is the same type of stream ingestion typically created by Change-Data-Capture (CDC) agents that parse transaction logs of a database or event notification mechanisms of modern application.  However, this could be simulating any type of stream, in any industry.  This streaming ingestion use case was modeled similarly to one previously handled with Snowflake's Kafka Connector, but no Kafka is necessary for this use case as a Snowpipe Streaming client can enable replacing the Kafka middleware infrastucture, saving cost & complexity.  Once landed, Dynamic Tables are purpose-built Snowflake objects for Data Engineering to transform the raw data into data ready for insights.\u003C/p\u003E\n","\u003Ch3\u003EThe Use Case\u003C/h3\u003E\n","\u003Cp\u003EOur Source 'database' has stock trades for the Dow Jones Industrials, \u003Ca href=\"https://www.nyse.com/quote/index/DJI\"\u003E30 US stocks\u003C/a\u003E.  On average 200M-400M stock trades are executed per day.  Our agent will be capturing Limit Order transaction events for these 30 stocks, which are new orders, updates to orders (changes in quantity or the limit price), and orders that are cancelled.  For this simulation, there are 3 new orders for every 2 updates, and then one cancellation.  This scenario's datastream will first reproduce a heavy workload of an initial market opening session and secondly a more modest continuous flow.  Snowflake data consumers want to see three perspectives on limit orders: what is the "current" list of orders that filters out stale and cancelled orders, a historical table showing every event on the source (in a traditional slowly changing dimension format), and current orders summarized by stock ticker symbol and by long or short position.  Latency needs to be minimized, 1-2 minutes would be ideal for the end-to-end process.\u003C/p\u003E\n","\u003Cp\u003EWhile not covered in this exercise, more Snowflake capabilities can further enrich your incoming data using Snowflake Marketplace data, train and deploy machine learning models, perform fraud detection, and other use cases.  This Lab was to introduce you to real-time streaming ingestion and then transformation, making processing easier and more scalable than ever before.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowflake, basic SQL knowledge, using your desktop command line and executing a java program\u003C/li\u003E\u003Cli\u003EHave a Java JRE/JDK Runtime environment on your laptop/desktop (confirm by running "java -version" showing v11 or higher).  Suggest \u003Ca href=\"https://jdk.java.net/20/\"\u003EOpenJDK\u003C/a\u003E 20 or higher if installing.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreate Private & Public keyfiles and using them for \u003Ca href=\"https://docs.snowflake.com/en/user-guide/key-pair-auth.html\"\u003Ekeypair authentication\u003C/a\u003E into Snowflake\u003C/li\u003E\u003Cli\u003EIntroduction to Snowpipe Streaming Java API, with a sample client and JSON-format datastream generator\u003C/li\u003E\u003Cli\u003EIngest real-time semi-structured data into Snowflake and store as Variant datafield\u003C/li\u003E\u003Cli\u003ECreate Dynamic Tables to do in-Snowflake ELT processing easier than ever before\u003C/li\u003E\u003Cli\u003EHow to perform field-level \u003Ca href=\"https://en.wikipedia.org/wiki/Advanced_Encryption_Standard\"\u003EAES\u003C/a\u003E-based data encryption at-the-source for security but governance controls for those authorized downstream\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n","\u003Cp\u003ETo complete this Quickstart, 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&utm_cta=developer-guides\"\u003ESnowflake Enterprise or Business Critical Account\u003C/a\u003E with \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E access or setup assistance\u003C/li\u003E\u003Cli\u003EBe able to download a SQL and a Zip file\u003C/li\u003E\u003Cli\u003EBe able to run "keytool" command on your desktop (a utility included with Java)\u003C/li\u003E\u003Cli\u003EBe able to run a Java program from the command line on your desktop\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUser and Roles to control authentication and security\u003C/li\u003E\u003Cli\u003EA Snowflake database that contains all data and objects built in this lab\u003C/li\u003E\u003Cli\u003EA Landing/Staging table to initially land your incoming data stream\u003C/li\u003E\u003Cli\u003EAnalytics-Ready Dynamic Tables\u003C/li\u003E\u003Cli\u003ESecure Functions for decrypting sensitive fields\u003C/li\u003E\u003Cli\u003ESecure Views to distribute data to various audiences\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting up Snowflake\u003C/h2\u003E\n","\u003Ch3\u003Ea) Download\u003C/h3\u003E\n","\u003Cp\u003EThe first thing you will need to do is download the following two files.  The first is a ZIP file you need to extract to a directory (for example C:/SnowflakeQuickstarts) that is the streaming simulator client.  The second file contains a series of SQL commands to execute in a Worksheet throughout this lab \u003Cstrong\u003EClick the green buttons to access the page to download the files\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://sfquickstarts.s3.us-west-1.amazonaws.com/data_engineering_CDC_snowpipestreaming_dynamictables/CDCSimulatorApp.zip\"\u003ECDCSimulatorApp.zip\u003C/a\u003E</button> (Click Download)\u003C/p\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/32fc9d8c26674f520cb894577e181dcfaeb7c7df/site/sfguides/src/data_engineering_CDC_SnowpipeStreaming_DynamicTables/files/Data_Engineering_Streams_CDC_DT_VHOL.sql\"\u003EData_Engineering_Streams_CDC_DT_VHOL.sql\u003C/a\u003E</button> (Click Raw, then from your browser, use "Save Page As")\u003C/p\u003E\n","\u003Ch3\u003Eb) Login\u003C/h3\u003E\n","\u003Cp\u003EAt this point login into your Snowflake account. If you have just created a free trial account, feel free to minimize or close hint boxes that are looking to help guide you. These will not be needed for this lab and most of the hints will be covered throughout the remainder of this exercise.\u003C/p\u003E\n","\u003Ch3\u003Ec) Create a Worksheet\u003C/h3\u003E\n","\u003Cp\u003EIn the Snowflake UI click on \u003Cstrong\u003EWorksheets\u003C/strong\u003E on the left side.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image2-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECreate a new Worksheet by clicking on the ** + ** button on the top right side and click \u003Cstrong\u003ESQL Worksheet\u003C/strong\u003E which will create a new worksheet in a tab.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image2-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, from the \u003Cstrong\u003ETab Menu\u003C/strong\u003E you can rename the tab to something more meaningful and from the same menu click \u003Cstrong\u003EImport SQL from File\u003C/strong\u003E and select the .sql file you downloaded and named earlier called \u003Cstrong\u003EData_Engineering_Streams_CDC_DT_VHOL.sql\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image2-3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEach step throughout the Snowflake portion of the guide has an associated SQL command to perform the work we are looking to execute, and so feel free to step through each action running the code one command at-a-time as you walk through the lab.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting up Your Desktop\u003C/h2\u003E\n","\u003Ch3\u003Ea) Unzip and Prepare Java Client\u003C/h3\u003E\n","\u003Cp\u003EFind where you downloaded your ZIP-compressed client application in step 2a), for example C:/SnowflakeQuickstarts.  Extract file \u003Cstrong\u003ECDCSimulatorApp.zip\u003C/strong\u003E file which will create a CDCSimulatorApp directory and many files within.\u003C/p\u003E\n","\u003Ch3\u003Eb) Generate Public and Private Keys\u003C/h3\u003E\n","\u003Cp\u003EFrom your desktop's Command Line / Terminal window, navigate to your working directory, then the directory extracted (CDCSimulatorApp) and run these two commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen file rsa_key.pub with a simple text editor and be prepared to copy your key soon in step 3.e, which is everything in-between the "- - - -" lines.  If 'openssl' command is not found, you need to review your Java installation before proceeding.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image3b.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003E(Yes, there will be carriage returns in the key, that is ok)\u003C/p\u003E\n","\u003Ch3\u003Ec) Create a Dedicated Role and Limited Login for your Streaming Application\u003C/h3\u003E\n","\u003Cp\u003EReturn to your Snowflake worksheet and run these commands, using the Public Key generated and file opened in Step 3.b above:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate role if not exists VHOL_CDC_AGENT;\ncreate or replace user vhol_streaming1 COMMENT="Creating for VHOL";\nalter user vhol_streaming1 set rsa_public_key='<Paste Your Public Key Here>';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ed) Edit Properties File\u003C/h3\u003E\n","\u003Cp\u003EYou will need to edit the snowflake.properties file to match your Snowflake account name (two places):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Euser=vhol_streaming1\nrole=VHOL_CDC_AGENT\naccount=<MY_SNOWFLAKE_ACCOUNT>\nwarehouse=VHOL_CDC_WH\nprivate_key_file=rsa_key.p8\nhost=<ACCOUNT_IDENTIFIER>.snowflakecomputing.com\ndatabase=VHOL_ENG_CDC\nschema=ENG\ntable=CDC_STREAMING_TABLE\nchannel_name=channel_1\nAES_KEY=O90hS0k9qHdsMDkPe46ZcQ==\nTOKEN_KEY=11\nDEBUG=FALSE\nSHOW_KEYS=TRUE\nNUM_ROWS=1000000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor assistance on \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier\"\u003EAccount identifiers\u003C/a\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 new roles 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($myname);\ngrant role VHOL_CDC_AGENT to user vhol_streaming1;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor the PII Section (Step #7):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate role if not exists PII_ADMIN;\ngrant role PII_ADMIN to user identifier($myname);\ncreate role if not exists PII_ALLOWED;\ngrant role PII_ALLOWED to user identifier($myname);\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_CDC_WH WAREHOUSE_SIZE = XSMALL, AUTO_SUSPEND = 5, AUTO_RESUME= TRUE;\ngrant all privileges on warehouse VHOL_CDC_WH to role VHOL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor the PII Section (Step #7):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Egrant usage on warehouse VHOL_CDC_WH to role PII_ADMIN;\ngrant usage on warehouse VHOL_CDC_WH to role PII_ALLOWED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ec)  Create Database used throughout this Lab\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate database VHOL_ENG_CDC;\nuse database VHOL_ENG_CDC;\ngrant ownership on schema PUBLIC to role VHOL;\nrevoke all privileges on database VHOL_ENG_CDC from role ACCOUNTADMIN;\ngrant ownership on database VHOL_ENG_CDC to role VHOL;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role VHOL;\nuse database VHOL_ENG_CDC;\ncreate schema ENG;\nuse VHOL_ENG_CDC.ENG;\nuse warehouse VHOL_CDC_WH;\ngrant usage on database VHOL_ENG_CDC to role VHOL_CDC_AGENT;\ngrant usage on schema ENG to role VHOL_CDC_AGENT;\ngrant usage on database VHOL_ENG_CDC to role PUBLIC;\ngrant usage on schema PUBLIC to role PUBLIC;\n\n\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 ENG.CDC_STREAMING_TABLE (RECORD_CONTENT variant);\ngrant insert on table ENG.CDC_STREAMING_TABLE to role VHOL_CDC_AGENT;\nselect * from CDC_STREAMING_TABLE;\nselect count(*) from CDC_STREAMING_TABLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ee) Test your Application and Test Connection to Snowflake\u003C/h3\u003E\n","\u003Cp\u003EReturn to your desktop, and run the program "Test.sh" or "Test.bat" to confirm your setup is complete and ready for ingestion.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E./Test.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Eor (for Microsoft Windows)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ETest.bat\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image4-e.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you get any errors / exceptions, read the error message closely and ask for help.  As this application is running on your desktop, you may be missing a prerequisite, have an older version of Java, have too restrictive user profile, or many other reasons.  Also check your snowflake.properties file to ensure you have the correct Account Name and Identifier set if you get connector errors.  If authentication issues, check and review your public key configuration.\u003C/p\u003E\n","\u003Ch2\u003EStreaming Records!\u003C/h2\u003E\n","\u003Ch3\u003Ea)  Run Streaming Application\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYou are now ready to Stream data into Snowflake!\u003C/strong\u003E\nExecute the Streaming Simulator using 'Run_MAX.sh', which will stream 1,000,000 records as fast as your desktop can run (MAX will also be in the Channel name):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E./Run_MAX.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Eor (for Microsoft Windows)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ERun_MAX.bat\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/cdc-snowpipestreaming-dynamictables/image5-a.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis should take 10-20 seconds.\u003C/p\u003E\n","\u003Ch3\u003Eb)  View New Records in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EJust like that, in a few seconds, you have 1M records in Snowflake (single-row inserts!)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect count(*) from ENG.CDC_STREAMING_TABLE;\nselect * from CDC_STREAMING_TABLE limit 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach record is a JSON payload, received via Snowpipe Streaming Ingestion API and stored in a Snowflake table as rows and Variant datafields.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image5-b.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext run 2 queries, using Snowflake's semi-structured capability, to show our new records and part of the query we will use in our Dynamic Tables before proceeding:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect RECORD_CONTENT:transaction:primaryKey_tokenized::string as orderid from ENG.CDC_STREAMING_TABLE limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect\n    RECORD_CONTENT:transaction:primaryKey_tokenized::varchar as orderid_tokenized,\n    RECORD_CONTENT:transaction:record_after:orderid_encrypted::varchar as orderid_encrypted,\n    RECORD_CONTENT:transaction:action::varchar as action,\n    RECORD_CONTENT:transaction:committed_at::varchar as committed_at,\n    RECORD_CONTENT:transaction:dbuser::varchar as dbuser,\n    RECORD_CONTENT:transaction:record_before::variant as before,\n    RECORD_CONTENT:transaction:record_after::variant as after\n  from ENG.CDC_STREAMING_TABLE\n  where RECORD_CONTENT:transaction:action::varchar='INSERT' limit 1000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs you can see, it is very easy to work with Semi-Structured data.  Our upcoming task of creating Dynamic Tables will take advantage of this.\u003C/p\u003E\n","\u003Ch3\u003Ec)  But There is More Than One Table in My Source System\u003C/h3\u003E\n","\u003Cp\u003EThe CDC Agent could easily be capturing changes from more than one source table, lets prepare for that and write each dynamic table to only use the events received for our simulated Stock Limit Order stream.  These are the key fields to use:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect distinct RECORD_CONTENT:transaction:schema::varchar,RECORD_CONTENT:transaction:table::varchar from CDC_STREAMING_TABLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECreate Dynamic Tables\u003C/h2\u003E\n","\u003Cp\u003ENow that data is landed and ready for Data Engineering, many possiblities to process.  Here are three examples:\u003C/p\u003E\n","\u003Ch3\u003Ea)  The "Current State"\u003C/h3\u003E\n","\u003Cp\u003ECreate a more finished Dynamic Table sourcing from Landing Table that reflects the "CURRENT STATE" of the source table.  This pattern, for each source table, you create a Dynamic Table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE DYNAMIC TABLE ENG.LIMIT_ORDERS_CURRENT_DT\nLAG = '1 minute'\nWAREHOUSE = 'VHOL_CDC_WH'\nAS\nSELECT * EXCLUDE (score,action) from (  \n  SELECT\n    RECORD_CONTENT:transaction:primaryKey_tokenized::varchar as orderid_tokenized,\n    RECORD_CONTENT:transaction:record_after:orderid_encrypted::varchar as orderid_encrypted,\n    TO_TIMESTAMP_NTZ(RECORD_CONTENT:transaction:committed_at::number/1000) as lastUpdated,\n    RECORD_CONTENT:transaction:action::varchar as action,\n    RECORD_CONTENT:transaction:record_after:client::varchar as client,\n    RECORD_CONTENT:transaction:record_after:ticker::varchar as ticker,\n    RECORD_CONTENT:transaction:record_after:LongOrShort::varchar as position,\n    RECORD_CONTENT:transaction:record_after:Price::number(38,3) as price,\n    RECORD_CONTENT:transaction:record_after:Quantity::number(38,3) as quantity,\n    RANK() OVER (\n        partition by orderid_tokenized order by RECORD_CONTENT:transaction:committed_at::number desc) as score\n  FROM ENG.CDC_STREAMING_TABLE \n    WHERE \n        RECORD_CONTENT:transaction:schema::varchar='PROD' AND RECORD_CONTENT:transaction:table::varchar='LIMIT_ORDERS'\n) \nWHERE score = 1 and action != 'DELETE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you run this right away, it will show a warning that table is not yet ready.  You have to wait a bit for the refresh period and the Dynamic Table to be built\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT count(*) FROM LIMIT_ORDERS_CURRENT_DT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E-- Wait for Lag Period (1 minute)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT count(*) FROM LIMIT_ORDERS_CURRENT_DT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStreaming Data is not Static Data\u003C/h3\u003E\n","\u003Cp\u003ELet's work with dynamic data going forward, return to your desktop to provide a continuous stream.  Run this command and the application will be streaming 10 records/second until you stop the application (using Cntrl-C in your command/shell window).  If you want more volume, run the "Run_Sloow" for 100/second or "Run_Slow" for 1000/second stream rate, Snowflake will absorb these easily too.  But simulator is designed to run only one of these at a time (channel name is configured in property file).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E./Run_Slooow.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Eor (for Microsoft Windows)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ERun_Slooow.bat\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eb) Slowly Changing Dimensions (SCD) Example\u003C/h3\u003E\n","\u003Cp\u003EFirst table is great, but we want to analyze how orders/records have changed and keep a historical record.  Let's do that by adding additional fields to each record to track and group them together:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE DYNAMIC TABLE ENG.LIMIT_ORDERS_SCD_DT\nLAG = '1 minute'\nWAREHOUSE = 'VHOL_CDC_WH'\nAS\nSELECT * EXCLUDE score from ( SELECT *,\n  CASE when score=1 then true else false end as Is_Latest,\n  LAST_VALUE(score) OVER (\n            partition by orderid_tokenized order by valid_from desc)+1-score as version\n  FROM (  \n      SELECT\n        RECORD_CONTENT:transaction:primaryKey_tokenized::varchar as orderid_tokenized,\n        --IFNULL(RECORD_CONTENT:transaction:record_after:orderid_encrypted::varchar,RECORD_CONTENT:transaction:record_before:orderid_encrypted::varchar) as orderid_encrypted,\n        RECORD_CONTENT:transaction:action::varchar as action,\n        IFNULL(RECORD_CONTENT:transaction:record_after:client::varchar,RECORD_CONTENT:transaction:record_before:client::varchar) as client,\n        IFNULL(RECORD_CONTENT:transaction:record_after:ticker::varchar,RECORD_CONTENT:transaction:record_before:ticker::varchar) as ticker,\n        IFNULL(RECORD_CONTENT:transaction:record_after:LongOrShort::varchar,RECORD_CONTENT:transaction:record_before:LongOrShort::varchar) as position,\n        RECORD_CONTENT:transaction:record_after:Price::number(38,3) as price,\n        RECORD_CONTENT:transaction:record_after:Quantity::number(38,3) as quantity,\n        RANK() OVER (\n            partition by orderid_tokenized order by RECORD_CONTENT:transaction:committed_at::number desc) as score,\n        TO_TIMESTAMP_NTZ(RECORD_CONTENT:transaction:committed_at::number/1000) as valid_from,\n        TO_TIMESTAMP_NTZ(LAG(RECORD_CONTENT:transaction:committed_at::number/1000,1,null) over \n                         (partition by orderid_tokenized order by RECORD_CONTENT:transaction:committed_at::number desc)) as valid_to\n      FROM ENG.CDC_STREAMING_TABLE\n      WHERE \n            RECORD_CONTENT:transaction:schema::varchar='PROD' AND RECORD_CONTENT:transaction:table::varchar='LIMIT_ORDERS'\n    ))\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun some test queries against our new dynamic table\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect  count(*) from LIMIT_ORDERS_SCD_DT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Ewait the lag period (~ 1 minute)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect  * from LIMIT_ORDERS_SCD_DT  limit 1000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E(Note, you should now see more than the 1,000,000 initial records we loaded)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect  count(*) from LIMIT_ORDERS_SCD_DT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ec)  Aggregations / Summary Example\u003C/h3\u003E\n","\u003Cp\u003ELet's try some aggregations for a Dynamic Table optimized for a specific use case.  This table summarizes by Stock and Order Type / Position:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE DYNAMIC TABLE ENG.LIMIT_ORDERS_SUMMARY_DT\nLAG = '1 minute'\nWAREHOUSE = 'VHOL_CDC_WH'\nAS\nSELECT ticker,position,min(price) as MIN_PRICE,max(price) as MAX_PRICE, TO_DECIMAL(avg(price),38,2) as AVERAGE_PRICE,\n    SUM(quantity) as TOTAL_SHARES,TO_DECIMAL(TOTAL_SHARES*AVERAGE_PRICE,38,2) as TOTAL_VALUE_USD\nfrom (  \n  SELECT\n    RECORD_CONTENT:transaction:action::varchar as action,\n    RECORD_CONTENT:transaction:record_after:ticker::varchar as ticker,\n    RECORD_CONTENT:transaction:record_after:LongOrShort::varchar as position,\n    RECORD_CONTENT:transaction:record_after:Price::number(38,3) as price,\n    RECORD_CONTENT:transaction:record_after:Quantity::number(38,3) as quantity\n  FROM ENG.CDC_STREAMING_TABLE\n  WHERE \n        RECORD_CONTENT:transaction:schema::varchar='PROD' AND RECORD_CONTENT:transaction:table::varchar='LIMIT_ORDERS'\n  QUALIFY RANK() OVER (\n        partition by RECORD_CONTENT:transaction:primaryKey_tokenized::varchar order by RECORD_CONTENT:transaction:committed_at::number desc) = 1\n) \nWHERE action != 'DELETE' group by ticker,position order by position,TOTAL_VALUE_USD DESC\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETable is created and populated.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from LIMIT_ORDERS_SUMMARY_DT where position='LONG' order by TOTAL_VALUE_USD;;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe are tracking the 30 Dow Jones Industrial Average Stocks (both Long and Short Limit Orders)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect  count(*) from LIMIT_ORDERS_SUMMARY_DT; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ed) Monitoring Dynamic Tables\u003C/h3\u003E\n","\u003Cp\u003EFirst, lets look at the properties page for a Dynamic Table, from the left-side menu, navigate to "Data, Databases>VHOL_ENG_CDC, ENG schema, Dynamic Tables" and click one of the Dynamic Tables you created.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-35.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will see five tabs (Table Details, Columns, Data Preview, Graph, & Refresh History).  Click on Refresh History first:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-36.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Graph view will show you dependencies on this Dynamic Table:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-38.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECan also leverage Query History, a more account-wide administrative view, from the left side menu, click Activity>Query History:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-2.png\" alt=\"\"\u003E \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-31.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eand utilize the filters at the top.  Remove user filter, as these refresh actions are performed by user "SYSTEM".  With Filter, enable the "Client-generated statements" to see the all dynamic table refresh activation events.  Can filter to a specific table by utilizing the "SQL Text", entering a Dynamic Table name's name, for example: "ENG.LIMIT_ORDERS_CURRENT_DT".\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-32.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003E(Return to your Worksheet)\u003C/p\u003E\n","\u003Ch3\u003Ee) Monitor Landing Table Channels\u003C/h3\u003E\n","\u003Cp\u003ESpecifically the offset token identifying the source's indicator of the last successfully-committed row identifier.  If there ever was an error on the source agent, this is the restart point.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eshow channels in table ENG.CDC_STREAMING_TABLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-37.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003Ef) Deliver to Consumers\u003C/h3\u003E\n","\u003Cp\u003EThis data is now ready for public use!  To create access for users to consume, lets use views to allow access (note, JSON path syntax not seen or needed except from landing table).\nFor our "Current View" Table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace view PUBLIC.CURRENT_LIMIT_ORDERS_VW\n  as select orderid_tokenized, lastUpdated,client,ticker,position,quantity,price\n  FROM ENG.LIMIT_ORDERS_CURRENT_DT order by orderid_tokenized;\n\ngrant select on view PUBLIC.CURRENT_LIMIT_ORDERS_VW to role PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENo need to wait\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from PUBLIC.CURRENT_LIMIT_ORDERS_VW limit 1000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote:  No reason to show consumers the encrypted orderid value\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECongrats, your consumers are able to view and analyze Limit Orders!\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EBonus:\u003C/h3\u003E\n","\u003Cp\u003EFeel free to create views for the other two dynamic tables.  Could also create a summary dynamic table (third one) sourcing from the first dynamic table instead of the landing table.\u003C/p\u003E\n","\u003Ch2\u003EHandling PII/Sensitive Data\u003C/h2\u003E\n","\u003Cp\u003EThis section will enable authorized users to utilize the orderid value, which was an encrypted-at-the-source field.  Next steps will create roles to assign to authorized users, a secure function to decrypt that value and the means to do so, and secondly create a secure view that adds the orderid as an unencypted value.  Within the Source Stream generator, there is two secure data fields added to protect the orderid value.  First is an AES-encryption method (reversible) and secondly a tokenization method (irreversible but repeatable to maintain referential integrity with other source tables).  Note:  These are not production-quality reusable security assets, but are for demonstration purposes.\u003C/p\u003E\n","\u003Ch3\u003Ea)  Setup Database for PII Assets\u003C/h3\u003E\n","\u003Cp\u003ECreate PII schema and provide Grants to the PII roles.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role VHOL;\nuse database VHOL_ENG_CDC;\ngrant ownership on schema PII to role PII_ADMIN;\ngrant usage on database VHOL_ENG_CDC to role PII_ADMIN;\ngrant usage on database VHOL_ENG_CDC to role PII_ALLOWED;\ngrant usage on schema ENG to role PII_ADMIN;\ngrant usage on schema ENG to role PII_ALLOWED;\n--grant select on dynamic table ENG.LIMIT_ORDERS_CURRENT_DT to role PII_ADMIN;\ngrant select on dynamic table ENG.LIMIT_ORDERS_CURRENT_DT to role PII_ALLOWED;\ncreate or replace schema PII COMMENT='Stay Out - Authorized Users Only';\nrevoke all privileges on schema VHOL_ENG_CDC.PII from role VHOL;\ngrant ownership on schema VHOL_ENG_CDC.PII to role PII_ADMIN;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eb)  Become PII Admin\u003C/h3\u003E\n","\u003Cp\u003ERight click on the "House" icon and click "Open in new Window".\u003C/p\u003E\n","\u003Cp\u003EClick on Worksheets, and "+" on upper right to create a new worksheet (Same as we started this Lab).\n(While you could copy all this into one worksheet, the PII Admin is usually not the same person as a Data Engineer)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role PII_ADMIN;\nuse schema VHOL_ENG_CDC.PII;\nuse warehouse VHOL_CDC_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ec)  Reusable Decryption Secure Function\u003C/h3\u003E\n","\u003Cp\u003ENeed to have a function that can decrypt the value (you may want to create your own internal or external secure function, this is just an example).\nFor consistency with the source encryption, using a java UDF to decrypt the data fields over built-in functions or Python UDF.\nHere is the first UDF:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE SECURE FUNCTION PII._DECRYPT_AES(FIELD string, ENCRYPTIONKEY string)\nRETURNS VARCHAR \nLANGUAGE JAVA\nHANDLER = 'Decrypt.decryptField'\nAS\n$$;\nimport java.security.Key;\nimport java.util.Base64;\nimport javax.crypto.Cipher;\nimport javax.crypto.KeyGenerator;\nimport javax.crypto.spec.SecretKeySpec;\nimport java.nio.charset.StandardCharsets;\n\nclass Decrypt {\n    private static final String ALGORITHM = "AES";\n    private Key KEY;\n    private KeyGenerator KEYGENERATOR;\n    private Cipher CIPHER;\n    public Decrypt() throws Exception{\n        CIPHER = Cipher.getInstance(ALGORITHM);\n    }\n    public String decryptField(String field, String encryptionKey) {\n        try {\n            setKey(encryptionKey);\n            CIPHER.init(Cipher.DECRYPT_MODE, KEY);\n            byte[] decodedBytes=CIPHER.doFinal(Base64.getDecoder().decode(field));\n            return new String(decodedBytes, StandardCharsets.UTF_8);\n        }\n        catch (Exception ex){\n            return ex.getMessage();\n        }\n  }\n  public void setKey(String k) throws Exception {\n        byte[] k0 = new String(k.toCharArray()).getBytes(StandardCharsets.UTF_8);\n        SecretKeySpec secretKey = new SecretKeySpec(k0,ALGORITHM);\n        KEY=secretKey;\n  }\n}\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest this new Function\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Egrant usage on function PII._DECRYPT_AES(string,string) to role PII_ADMIN;\nselect PII._DECRYPT_AES('NhVcyJa8/r3Wdy6WNvT0yQw+SouNYGPAy/ddVe6064Y=', 'O90hS0k9qHdsMDkPe46ZcQ==') as orderid;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ed)  This Pipeline's Decryption Secure Function\u003C/h3\u003E\n","\u003Cp\u003EFor simplicity of this Lab, we will store the decryption key in a dedicated function, but ideally this is stored in a secrets vault.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE SECURE FUNCTION PII.DECRYPT_CDC_FIELD(FIELD string)\nRETURNS VARCHAR \n as\n $$ \n select PII._DECRYPT_AES(FIELD, 'O90hS0k9qHdsMDkPe46ZcQ==')\n $$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGrant usage and test\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E grant usage on function PII.DECRYPT_CDC_FIELD(varchar) to role PII_ADMIN;\n select PII.DECRYPT_CDC_FIELD('NhVcyJa8/r3Wdy6WNvT0yQw+SouNYGPAy/ddVe6064Y=');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOk, it works.  Access no longer required.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E revoke usage on function PII.DECRYPT_CDC_FIELD(varchar) from role PII_ADMIN;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ee) Create Secure View\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate or replace secure view PII.LIMIT_ORDERS_VW\nas \n  select orderid_tokenized,orderid_encrypted,\n    PII.DECRYPT_CDC_FIELD(orderid_encrypted) as orderid_PII,\n    lastUpdated,client,ticker,position,price,quantity\n  from ENG.LIMIT_ORDERS_CURRENT_DT order by orderid_PII;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGrant usage to PII User Role\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Egrant usage on schema PII to role PII_ALLOWED;\ngrant usage on function PII._DECRYPT_AES(string,string) to role PII_ALLOWED;\ngrant usage on function PII.DECRYPT_CDC_FIELD(varchar) to role PII_ALLOWED;\ngrant select on view PII.LIMIT_ORDERS_VW to role PII_ALLOWED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ef)  Be PII-Enabled\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role PII_ALLOWED;\nuse database VHOL_ENG_CDC;\nselect * from PII.LIMIT_ORDERS_VW order by ORDERID_PII limit 1000;\nselect * from PII.LIMIT_ORDERS_VW where ticker='MMM' and position='LONG' order by ORDERID_PII;\nselect * from PII.LIMIT_ORDERS_VW limit 1000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can now see the ORDERID in three forms:  Tokenized, Encrypted, and Decrypted.  But only if you have a role for PII.\u003C/p\u003E\n","\u003Cp\u003ESnowflake's Granular, Role-Based Access enables you to control who has access to what, at the database/schema, table, column, and row level as you need!\nDynamic Masking and tagging will be more layers of security available soon, but not included or enabled in this preview.\u003C/p\u003E\n","\u003Ch3\u003Eg)  Check Security\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role VHOL;\nuse schema VHOL_ENG_CDC.PII;\nselect * from VHOL_ENG_CDC.PII.LIMIT_ORDERS_VW limit 1000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EFinal Steps & Cleanup\u003C/h2\u003E\n","\u003Ch3\u003Ea) Use Cntrl-C to kill your Streaming App running on your Desktop\u003C/h3\u003E\n","\u003Ch3\u003Eb) See how many transactions you have processed\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role VHOL;\nselect count(*) from ENG.CDC_STREAMING_TABLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ec) Remove & Cleanup Desktop Directory created for this Streaming Application\u003C/h3\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_ENG_CDC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ee) Drop Warehouse (Optional)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role ACCOUNTADMIN;\ndrop warehouse VHOL_CDC_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ef) Drop Role (Optional)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role ACCOUNTADMIN;\ndrop role VHOL;\ndrop role VHOL_CDC_AGENT;\ndrop role PII_ADMIN;\ndrop role PII_ALLOWED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECongratulations, you have completed this Lab!\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat We Covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESetup keypair authentication into Snowflake\u003C/li\u003E\u003Cli\u003EUsed Snowpipe Streaming Java API and streamed data into Snowflake\u003C/li\u003E\u003Cli\u003EIngested Semi-Structured JSON data into Snowflake in real-time instead of bulk loading\u003C/li\u003E\u003Cli\u003ECreated Multiple Dynamic Tables for Data Engineering Tasks\u003C/li\u003E\u003Cli\u003ESecured Sensitive Fields, beginning from the Source, but also decrypted for those authorized\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/content/dam/snowflake-site/developers/2024/07/Industrial-IoT_-Snowpipe-Streaming_Reference-Architecture.pdf\"\u003EDownload Reference Architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/blog/manufacturing-data-ingestion-into-snowflake/\"\u003ERead the Blog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://youtu.be/MIJ2suyE2io?list=TLGG99oO9HwspOgyNDA5MjAyNQ\"\u003EWatch the Demo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Snowpipe Streaming and Dynamic Tables for Real-Time Ingestion (CDC Use Case)","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 Snowpipe Streaming to ingest a simulated stream, then utilize Dynamic tables to transform and prepare the raw ingested JSON payloads into ready-for-analytics datasets.  These are two of Snowflake's powerful Data Engineering innovations for ingestion and transformation.\n\nA simulated streaming datafeed will be generated for this exercise, using a java client running on your desktop, that implements the Snowpipe Streaming SDK.  The simulated datafeed will be Stock Limit Orders, with new, changed, and cancelled orders represented as RDBMS transactions logs captured from INSERT, UPDATE, and DELETE database events.  These events will be transmitted as JSON payloads and land into a Snowflake table with a variant data column.  The simulation will be high-volume, first starting at 1 million transactions in seconds and secondly as steady stream.  Finally, data will contain sensitive fields, so our process will have extra protections for this.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image0.png)\n\nThis is the same type of stream ingestion typically created by Change-Data-Capture (CDC) agents that parse transaction logs of a database or event notification mechanisms of modern application.  However, this could be simulating any type of stream, in any industry.  This streaming ingestion use case was modeled similarly to one previously handled with Snowflake's Kafka Connector, but no Kafka is necessary for this use case as a Snowpipe Streaming client can enable replacing the Kafka middleware infrastucture, saving cost & complexity.  Once landed, Dynamic Tables are purpose-built Snowflake objects for Data Engineering to transform the raw data into data ready for insights.\n\n### The Use Case\nOur Source 'database' has stock trades for the Dow Jones Industrials, [30 US stocks](https://www.nyse.com/quote/index/DJI).  On average 200M-400M stock trades are executed per day.  Our agent will be capturing Limit Order transaction events for these 30 stocks, which are new orders, updates to orders (changes in quantity or the limit price), and orders that are cancelled.  For this simulation, there are 3 new orders for every 2 updates, and then one cancellation.  This scenario's datastream will first reproduce a heavy workload of an initial market opening session and secondly a more modest continuous flow.  Snowflake data consumers want to see three perspectives on limit orders: what is the \"current\" list of orders that filters out stale and cancelled orders, a historical table showing every event on the source (in a traditional slowly changing dimension format), and current orders summarized by stock ticker symbol and by long or short position.  Latency needs to be minimized, 1-2 minutes would be ideal for the end-to-end process.\n\nWhile not covered in this exercise, more Snowflake capabilities can further enrich your incoming data using Snowflake Marketplace data, train and deploy machine learning models, perform fraud detection, and other use cases.  This Lab was to introduce you to real-time streaming ingestion and then transformation, making processing easier and more scalable than ever before.\n\n\n### Prerequisites\n- Familiarity with Snowflake, basic SQL knowledge, using your desktop command line and executing a java program\n- Have a Java JRE/JDK Runtime environment on your laptop/desktop (confirm by running \"java -version\" showing v11 or higher).  Suggest [OpenJDK](https://jdk.java.net/20/) 20 or higher if installing.\n\n### What You’ll Learn\n- Create Private & Public keyfiles and using them for [keypair authentication](https://docs.snowflake.com/en/user-guide/key-pair-auth.html) into Snowflake\n- Introduction to Snowpipe Streaming Java API, with a sample client and JSON-format datastream generator\n- Ingest real-time semi-structured data into Snowflake and store as Variant datafield\n- Create Dynamic Tables to do in-Snowflake ELT processing easier than ever before \n- How to perform field-level [AES](https://en.wikipedia.org/wiki/Advanced_Encryption_Standard)-based data encryption at-the-source for security but governance controls for those authorized downstream\n\n### What You’ll Need \nTo complete this Quickstart, attendees need the following:\n\n- A [Snowflake Enterprise or Business Critical Account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with **ACCOUNTADMIN** access or setup assistance\n- Be able to download a SQL and a Zip file\n- Be able to run \"keytool\" command on your desktop (a utility included with Java)\n- Be able to run a Java program from the command line on your desktop\n\n### What You’ll Build\n- User and Roles to control authentication and security\n- A Snowflake database that contains all data and objects built in this lab\n- A Landing/Staging table to initially land your incoming data stream\n- Analytics-Ready Dynamic Tables\n- Secure Functions for decrypting sensitive fields\n- Secure Views to distribute data to various audiences\n\n\u003C!-- ------------------------ --\u003E\n## Setting up Snowflake\n\n### a) Download\nThe first thing you will need to do is download the following two files.  The first is a ZIP file you need to extract to a directory (for example C:/SnowflakeQuickstarts) that is the streaming simulator client.  The second file contains a series of SQL commands to execute in a Worksheet throughout this lab **Click the green buttons to access the page to download the files**\n\n\n\u003Cbutton\u003E[CDCSimulatorApp.zip](https://sfquickstarts.s3.us-west-1.amazonaws.com/data_engineering_CDC_snowpipestreaming_dynamictables/CDCSimulatorApp.zip)\u003C/button\u003E (Click Download)\n\n\u003Cbutton\u003E[Data_Engineering_Streams_CDC_DT_VHOL.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/32fc9d8c26674f520cb894577e181dcfaeb7c7df/site/sfguides/src/data_engineering_CDC_SnowpipeStreaming_DynamicTables/files/Data_Engineering_Streams_CDC_DT_VHOL.sql)\u003C/button\u003E (Click Raw, then from your browser, use \"Save Page As\")\n\n### b) Login\nAt this point login into your Snowflake account. If you have just created a free trial account, feel free to minimize or close hint boxes that are looking to help guide you. These will not be needed for this lab and most of the hints will be covered throughout the remainder of this exercise.\n\n### c) Create a Worksheet\nIn the Snowflake UI click on **Worksheets** on the left side.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image2-1.png)\n\nCreate a new Worksheet by clicking on the ** + ** button on the top right side and click **SQL Worksheet** which will create a new worksheet in a tab.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image2-2.png)\n\nNext, from the **Tab Menu** you can rename the tab to something more meaningful and from the same menu click **Import SQL from File** and select the .sql file you downloaded and named earlier called **Data_Engineering_Streams_CDC_DT_VHOL.sql**.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image2-3.png)\n\nEach step throughout the Snowflake portion of the guide has an associated SQL command to perform the work we are looking to execute, and so feel free to step through each action running the code one command at-a-time as you walk through the lab.\n\n\n\u003C!-- ------------------------ --\u003E\n## Setting up Your Desktop\n\n### a) Unzip and Prepare Java Client\nFind where you downloaded your ZIP-compressed client application in step 2a), for example C:/SnowflakeQuickstarts.  Extract file **CDCSimulatorApp.zip** file which will create a CDCSimulatorApp directory and many files within.\n\n\n### b) Generate Public and Private Keys\nFrom your desktop's Command Line / Terminal window, navigate to your working directory, then the directory extracted (CDCSimulatorApp) and run these two commands:\n```\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n```\nOpen file rsa_key.pub with a simple text editor and be prepared to copy your key soon in step 3.e, which is everything in-between the \"- - - -\" lines.  If 'openssl' command is not found, you need to review your Java installation before proceeding.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image3b.png)\n\n(Yes, there will be carriage returns in the key, that is ok)\n\n\n### c) Create a Dedicated Role and Limited Login for your Streaming Application\nReturn to your Snowflake worksheet and run these commands, using the Public Key generated and file opened in Step 3.b above:\n```\ncreate role if not exists VHOL_CDC_AGENT;\ncreate or replace user vhol_streaming1 COMMENT=\"Creating for VHOL\";\nalter user vhol_streaming1 set rsa_public_key='\u003CPaste Your Public Key Here\u003E';\n```\n### d) Edit Properties File\nYou will need to edit the snowflake.properties file to match your Snowflake account name (two places):\n```\nuser=vhol_streaming1\nrole=VHOL_CDC_AGENT\naccount=\u003CMY_SNOWFLAKE_ACCOUNT\u003E\nwarehouse=VHOL_CDC_WH\nprivate_key_file=rsa_key.p8\nhost=\u003CACCOUNT_IDENTIFIER\u003E.snowflakecomputing.com\ndatabase=VHOL_ENG_CDC\nschema=ENG\ntable=CDC_STREAMING_TABLE\nchannel_name=channel_1\nAES_KEY=O90hS0k9qHdsMDkPe46ZcQ==\nTOKEN_KEY=11\nDEBUG=FALSE\nSHOW_KEYS=TRUE\nNUM_ROWS=1000000\n```\nFor assistance on [Account identifiers](https://docs.snowflake.com/en/user-guide/admin-account-identifier)\n\n\n\u003C!-- ------------------------ --\u003E\n## Begin Construction\nCreate Foundational Snowflake Objects for this Hands-on Lab:\n\n### a)  Create new roles 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($myname);\ngrant role VHOL_CDC_AGENT to user vhol_streaming1;\n\n```\nFor the PII Section (Step #7):\n```\ncreate role if not exists PII_ADMIN;\ngrant role PII_ADMIN to user identifier($myname);\ncreate role if not exists PII_ALLOWED;\ngrant role PII_ALLOWED to user identifier($myname);\n```\n\n### b)  Create a Dedicated Virtual Compute Warehouse\nSize XS, dedicated for this Hands-on Lab\n```\ncreate or replace warehouse VHOL_CDC_WH WAREHOUSE_SIZE = XSMALL, AUTO_SUSPEND = 5, AUTO_RESUME= TRUE;\ngrant all privileges on warehouse VHOL_CDC_WH to role VHOL;\n```\nFor the PII Section (Step #7):\n```\ngrant usage on warehouse VHOL_CDC_WH to role PII_ADMIN;\ngrant usage on warehouse VHOL_CDC_WH to role PII_ALLOWED;\n```\n\n### c)  Create Database used throughout this Lab\n```\ncreate database VHOL_ENG_CDC;\nuse database VHOL_ENG_CDC;\ngrant ownership on schema PUBLIC to role VHOL;\nrevoke all privileges on database VHOL_ENG_CDC from role ACCOUNTADMIN;\ngrant ownership on database VHOL_ENG_CDC to role VHOL;\n```\n\n\n```\nuse role VHOL;\nuse database VHOL_ENG_CDC;\ncreate schema ENG;\nuse VHOL_ENG_CDC.ENG;\nuse warehouse VHOL_CDC_WH;\ngrant usage on database VHOL_ENG_CDC to role VHOL_CDC_AGENT;\ngrant usage on schema ENG to role VHOL_CDC_AGENT;\ngrant usage on database VHOL_ENG_CDC to role PUBLIC;\ngrant usage on schema PUBLIC to role PUBLIC;\n\n\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 ENG.CDC_STREAMING_TABLE (RECORD_CONTENT variant);\ngrant insert on table ENG.CDC_STREAMING_TABLE to role VHOL_CDC_AGENT;\nselect * from CDC_STREAMING_TABLE;\nselect count(*) from CDC_STREAMING_TABLE;\n```\n\n### e) Test your Application and Test Connection to Snowflake\nReturn to your desktop, and run the program \"Test.sh\" or \"Test.bat\" to confirm your setup is complete and ready for ingestion.\n```\n./Test.sh\n```\nor (for Microsoft Windows)\n```\nTest.bat\n```\nYou should see this:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image4-e.png)\n\nIf you get any errors / exceptions, read the error message closely and ask for help.  As this application is running on your desktop, you may be missing a prerequisite, have an older version of Java, have too restrictive user profile, or many other reasons.  Also check your snowflake.properties file to ensure you have the correct Account Name and Identifier set if you get connector errors.  If authentication issues, check and review your public key configuration.\n\n## Streaming Records!\n\n### a)  Run Streaming Application\n\n**You are now ready to Stream data into Snowflake!** \nExecute the Streaming Simulator using 'Run_MAX.sh', which will stream 1,000,000 records as fast as your desktop can run (MAX will also be in the Channel name):\n```\n./Run_MAX.sh\n```\nor (for Microsoft Windows)\n```\nRun_MAX.bat\n```\nWhich returns:  \n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image5-a.png)\n\nThis should take 10-20 seconds.\n\n### b)  View New Records in Snowflake\nJust like that, in a few seconds, you have 1M records in Snowflake (single-row inserts!)\n```\nselect count(*) from ENG.CDC_STREAMING_TABLE;\nselect * from CDC_STREAMING_TABLE limit 100;\n```\nEach record is a JSON payload, received via Snowpipe Streaming Ingestion API and stored in a Snowflake table as rows and Variant datafields.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image5-b.png)\n\nNext run 2 queries, using Snowflake's semi-structured capability, to show our new records and part of the query we will use in our Dynamic Tables before proceeding:\n```\nselect RECORD_CONTENT:transaction:primaryKey_tokenized::string as orderid from ENG.CDC_STREAMING_TABLE limit 10;\n```\n\n```\nselect\n    RECORD_CONTENT:transaction:primaryKey_tokenized::varchar as orderid_tokenized,\n    RECORD_CONTENT:transaction:record_after:orderid_encrypted::varchar as orderid_encrypted,\n    RECORD_CONTENT:transaction:action::varchar as action,\n    RECORD_CONTENT:transaction:committed_at::varchar as committed_at,\n    RECORD_CONTENT:transaction:dbuser::varchar as dbuser,\n    RECORD_CONTENT:transaction:record_before::variant as before,\n    RECORD_CONTENT:transaction:record_after::variant as after\n  from ENG.CDC_STREAMING_TABLE\n  where RECORD_CONTENT:transaction:action::varchar='INSERT' limit 1000;\n  ```\n\nAs you can see, it is very easy to work with Semi-Structured data.  Our upcoming task of creating Dynamic Tables will take advantage of this.\n\n### c)  But There is More Than One Table in My Source System\nThe CDC Agent could easily be capturing changes from more than one source table, lets prepare for that and write each dynamic table to only use the events received for our simulated Stock Limit Order stream.  These are the key fields to use:\n```\nselect distinct RECORD_CONTENT:transaction:schema::varchar,RECORD_CONTENT:transaction:table::varchar from CDC_STREAMING_TABLE;\n```\n\n## Create Dynamic Tables\nNow that data is landed and ready for Data Engineering, many possiblities to process.  Here are three examples: \n\n### a)  The \"Current State\"\nCreate a more finished Dynamic Table sourcing from Landing Table that reflects the \"CURRENT STATE\" of the source table.  This pattern, for each source table, you create a Dynamic Table:\n```\nCREATE OR REPLACE DYNAMIC TABLE ENG.LIMIT_ORDERS_CURRENT_DT\nLAG = '1 minute'\nWAREHOUSE = 'VHOL_CDC_WH'\nAS\nSELECT * EXCLUDE (score,action) from (  \n  SELECT\n    RECORD_CONTENT:transaction:primaryKey_tokenized::varchar as orderid_tokenized,\n    RECORD_CONTENT:transaction:record_after:orderid_encrypted::varchar as orderid_encrypted,\n    TO_TIMESTAMP_NTZ(RECORD_CONTENT:transaction:committed_at::number/1000) as lastUpdated,\n    RECORD_CONTENT:transaction:action::varchar as action,\n    RECORD_CONTENT:transaction:record_after:client::varchar as client,\n    RECORD_CONTENT:transaction:record_after:ticker::varchar as ticker,\n    RECORD_CONTENT:transaction:record_after:LongOrShort::varchar as position,\n    RECORD_CONTENT:transaction:record_after:Price::number(38,3) as price,\n    RECORD_CONTENT:transaction:record_after:Quantity::number(38,3) as quantity,\n    RANK() OVER (\n        partition by orderid_tokenized order by RECORD_CONTENT:transaction:committed_at::number desc) as score\n  FROM ENG.CDC_STREAMING_TABLE \n    WHERE \n        RECORD_CONTENT:transaction:schema::varchar='PROD' AND RECORD_CONTENT:transaction:table::varchar='LIMIT_ORDERS'\n) \nWHERE score = 1 and action != 'DELETE';\n```\n\n\nIf you run this right away, it will show a warning that table is not yet ready.  You have to wait a bit for the refresh period and the Dynamic Table to be built\n```\nSELECT count(*) FROM LIMIT_ORDERS_CURRENT_DT;\n```\n-- Wait for Lag Period (1 minute)\n```\nSELECT count(*) FROM LIMIT_ORDERS_CURRENT_DT;\n```\n\n### Streaming Data is not Static Data\nLet's work with dynamic data going forward, return to your desktop to provide a continuous stream.  Run this command and the application will be streaming 10 records/second until you stop the application (using Cntrl-C in your command/shell window).  If you want more volume, run the \"Run_Sloow\" for 100/second or \"Run_Slow\" for 1000/second stream rate, Snowflake will absorb these easily too.  But simulator is designed to run only one of these at a time (channel name is configured in property file).\n```\n./Run_Slooow.sh\n```\nor (for Microsoft Windows)\n```\nRun_Slooow.bat\n```\n\n\n### b) Slowly Changing Dimensions (SCD) Example\nFirst table is great, but we want to analyze how orders/records have changed and keep a historical record.  Let's do that by adding additional fields to each record to track and group them together:\n```\nCREATE OR REPLACE DYNAMIC TABLE ENG.LIMIT_ORDERS_SCD_DT\nLAG = '1 minute'\nWAREHOUSE = 'VHOL_CDC_WH'\nAS\nSELECT * EXCLUDE score from ( SELECT *,\n  CASE when score=1 then true else false end as Is_Latest,\n  LAST_VALUE(score) OVER (\n            partition by orderid_tokenized order by valid_from desc)+1-score as version\n  FROM (  \n      SELECT\n        RECORD_CONTENT:transaction:primaryKey_tokenized::varchar as orderid_tokenized,\n        --IFNULL(RECORD_CONTENT:transaction:record_after:orderid_encrypted::varchar,RECORD_CONTENT:transaction:record_before:orderid_encrypted::varchar) as orderid_encrypted,\n        RECORD_CONTENT:transaction:action::varchar as action,\n        IFNULL(RECORD_CONTENT:transaction:record_after:client::varchar,RECORD_CONTENT:transaction:record_before:client::varchar) as client,\n        IFNULL(RECORD_CONTENT:transaction:record_after:ticker::varchar,RECORD_CONTENT:transaction:record_before:ticker::varchar) as ticker,\n        IFNULL(RECORD_CONTENT:transaction:record_after:LongOrShort::varchar,RECORD_CONTENT:transaction:record_before:LongOrShort::varchar) as position,\n        RECORD_CONTENT:transaction:record_after:Price::number(38,3) as price,\n        RECORD_CONTENT:transaction:record_after:Quantity::number(38,3) as quantity,\n        RANK() OVER (\n            partition by orderid_tokenized order by RECORD_CONTENT:transaction:committed_at::number desc) as score,\n        TO_TIMESTAMP_NTZ(RECORD_CONTENT:transaction:committed_at::number/1000) as valid_from,\n        TO_TIMESTAMP_NTZ(LAG(RECORD_CONTENT:transaction:committed_at::number/1000,1,null) over \n                         (partition by orderid_tokenized order by RECORD_CONTENT:transaction:committed_at::number desc)) as valid_to\n      FROM ENG.CDC_STREAMING_TABLE\n      WHERE \n            RECORD_CONTENT:transaction:schema::varchar='PROD' AND RECORD_CONTENT:transaction:table::varchar='LIMIT_ORDERS'\n    ))\n;\n```\n\nRun some test queries against our new dynamic table\n```\nselect  count(*) from LIMIT_ORDERS_SCD_DT;\n```\nwait the lag period (~ 1 minute)\n```\nselect  * from LIMIT_ORDERS_SCD_DT  limit 1000;\n```\n(Note, you should now see more than the 1,000,000 initial records we loaded)\n```\nselect  count(*) from LIMIT_ORDERS_SCD_DT;\n```\n\n\n### c)  Aggregations / Summary Example \nLet's try some aggregations for a Dynamic Table optimized for a specific use case.  This table summarizes by Stock and Order Type / Position:\n```\nCREATE OR REPLACE DYNAMIC TABLE ENG.LIMIT_ORDERS_SUMMARY_DT\nLAG = '1 minute'\nWAREHOUSE = 'VHOL_CDC_WH'\nAS\nSELECT ticker,position,min(price) as MIN_PRICE,max(price) as MAX_PRICE, TO_DECIMAL(avg(price),38,2) as AVERAGE_PRICE,\n    SUM(quantity) as TOTAL_SHARES,TO_DECIMAL(TOTAL_SHARES*AVERAGE_PRICE,38,2) as TOTAL_VALUE_USD\nfrom (  \n  SELECT\n    RECORD_CONTENT:transaction:action::varchar as action,\n    RECORD_CONTENT:transaction:record_after:ticker::varchar as ticker,\n    RECORD_CONTENT:transaction:record_after:LongOrShort::varchar as position,\n    RECORD_CONTENT:transaction:record_after:Price::number(38,3) as price,\n    RECORD_CONTENT:transaction:record_after:Quantity::number(38,3) as quantity\n  FROM ENG.CDC_STREAMING_TABLE\n  WHERE \n        RECORD_CONTENT:transaction:schema::varchar='PROD' AND RECORD_CONTENT:transaction:table::varchar='LIMIT_ORDERS'\n  QUALIFY RANK() OVER (\n        partition by RECORD_CONTENT:transaction:primaryKey_tokenized::varchar order by RECORD_CONTENT:transaction:committed_at::number desc) = 1\n) \nWHERE action != 'DELETE' group by ticker,position order by position,TOTAL_VALUE_USD DESC\n;\n```\nTable is created and populated.\n```\nselect * from LIMIT_ORDERS_SUMMARY_DT where position='LONG' order by TOTAL_VALUE_USD;;\n```\n\nWe are tracking the 30 Dow Jones Industrial Average Stocks (both Long and Short Limit Orders)\n```\nselect  count(*) from LIMIT_ORDERS_SUMMARY_DT; \n```\n\n### d) Monitoring Dynamic Tables\n\nFirst, lets look at the properties page for a Dynamic Table, from the left-side menu, navigate to \"Data, Databases\u003EVHOL_ENG_CDC, ENG schema, Dynamic Tables\" and click one of the Dynamic Tables you created.  \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-35.png)\n\nYou will see five tabs (Table Details, Columns, Data Preview, Graph, & Refresh History).  Click on Refresh History first:\n\n ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-36.png)\n\nThe Graph view will show you dependencies on this Dynamic Table:\n\n ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-38.png)\n\nCan also leverage Query History, a more account-wide administrative view, from the left side menu, click Activity\u003EQuery History:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-2.png) ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-31.png)\n\nand utilize the filters at the top.  Remove user filter, as these refresh actions are performed by user \"SYSTEM\".  With Filter, enable the \"Client-generated statements\" to see the all dynamic table refresh activation events.  Can filter to a specific table by utilizing the \"SQL Text\", entering a Dynamic Table name's name, for example: \"ENG.LIMIT_ORDERS_CURRENT_DT\".\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-32.png)\n\n(Return to your Worksheet)\n\n ### e) Monitor Landing Table Channels\nSpecifically the offset token identifying the source's indicator of the last successfully-committed row identifier.  If there ever was an error on the source agent, this is the restart point.\n```\nshow channels in table ENG.CDC_STREAMING_TABLE;\n```\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cdc-snowpipestreaming-dynamictables/image6-37.png)\n\n\n\n### f) Deliver to Consumers\n\nThis data is now ready for public use!  To create access for users to consume, lets use views to allow access (note, JSON path syntax not seen or needed except from landing table).\nFor our \"Current View\" Table:\n```\ncreate or replace view PUBLIC.CURRENT_LIMIT_ORDERS_VW\n  as select orderid_tokenized, lastUpdated,client,ticker,position,quantity,price\n  FROM ENG.LIMIT_ORDERS_CURRENT_DT order by orderid_tokenized;\n\ngrant select on view PUBLIC.CURRENT_LIMIT_ORDERS_VW to role PUBLIC;\n```\nNo need to wait\n```\nselect * from PUBLIC.CURRENT_LIMIT_ORDERS_VW limit 1000;\n```\nNote:  No reason to show consumers the encrypted orderid value\n\n**Congrats, your consumers are able to view and analyze Limit Orders!**  \n### Bonus:\nFeel free to create views for the other two dynamic tables.  Could also create a summary dynamic table (third one) sourcing from the first dynamic table instead of the landing table.\n\n\n## Handling PII/Sensitive Data\n\nThis section will enable authorized users to utilize the orderid value, which was an encrypted-at-the-source field.  Next steps will create roles to assign to authorized users, a secure function to decrypt that value and the means to do so, and secondly create a secure view that adds the orderid as an unencypted value.  Within the Source Stream generator, there is two secure data fields added to protect the orderid value.  First is an AES-encryption method (reversible) and secondly a tokenization method (irreversible but repeatable to maintain referential integrity with other source tables).  Note:  These are not production-quality reusable security assets, but are for demonstration purposes.\n\n### a)  Setup Database for PII Assets\nCreate PII schema and provide Grants to the PII roles.\n```\nuse role VHOL;\nuse database VHOL_ENG_CDC;\ngrant ownership on schema PII to role PII_ADMIN;\ngrant usage on database VHOL_ENG_CDC to role PII_ADMIN;\ngrant usage on database VHOL_ENG_CDC to role PII_ALLOWED;\ngrant usage on schema ENG to role PII_ADMIN;\ngrant usage on schema ENG to role PII_ALLOWED;\n--grant select on dynamic table ENG.LIMIT_ORDERS_CURRENT_DT to role PII_ADMIN;\ngrant select on dynamic table ENG.LIMIT_ORDERS_CURRENT_DT to role PII_ALLOWED;\ncreate or replace schema PII COMMENT='Stay Out - Authorized Users Only';\nrevoke all privileges on schema VHOL_ENG_CDC.PII from role VHOL;\ngrant ownership on schema VHOL_ENG_CDC.PII to role PII_ADMIN;\n```\n### b)  Become PII Admin\nRight click on the \"House\" icon and click \"Open in new Window\".\n\nClick on Worksheets, and \"+\" on upper right to create a new worksheet (Same as we started this Lab).\n(While you could copy all this into one worksheet, the PII Admin is usually not the same person as a Data Engineer)\n```\nuse role PII_ADMIN;\nuse schema VHOL_ENG_CDC.PII;\nuse warehouse VHOL_CDC_WH;\n```\n\n### c)  Reusable Decryption Secure Function\nNeed to have a function that can decrypt the value (you may want to create your own internal or external secure function, this is just an example). \nFor consistency with the source encryption, using a java UDF to decrypt the data fields over built-in functions or Python UDF.\nHere is the first UDF:\n```\nCREATE OR REPLACE SECURE FUNCTION PII._DECRYPT_AES(FIELD string, ENCRYPTIONKEY string)\nRETURNS VARCHAR \nLANGUAGE JAVA\nHANDLER = 'Decrypt.decryptField'\nAS\n$$;\nimport java.security.Key;\nimport java.util.Base64;\nimport javax.crypto.Cipher;\nimport javax.crypto.KeyGenerator;\nimport javax.crypto.spec.SecretKeySpec;\nimport java.nio.charset.StandardCharsets;\n\nclass Decrypt {\n    private static final String ALGORITHM = \"AES\";\n    private Key KEY;\n    private KeyGenerator KEYGENERATOR;\n    private Cipher CIPHER;\n    public Decrypt() throws Exception{\n        CIPHER = Cipher.getInstance(ALGORITHM);\n    }\n    public String decryptField(String field, String encryptionKey) {\n        try {\n            setKey(encryptionKey);\n            CIPHER.init(Cipher.DECRYPT_MODE, KEY);\n            byte[] decodedBytes=CIPHER.doFinal(Base64.getDecoder().decode(field));\n            return new String(decodedBytes, StandardCharsets.UTF_8);\n        }\n        catch (Exception ex){\n            return ex.getMessage();\n        }\n  }\n  public void setKey(String k) throws Exception {\n        byte[] k0 = new String(k.toCharArray()).getBytes(StandardCharsets.UTF_8);\n        SecretKeySpec secretKey = new SecretKeySpec(k0,ALGORITHM);\n        KEY=secretKey;\n  }\n}\n$$;\n```\nTest this new Function\n```\ngrant usage on function PII._DECRYPT_AES(string,string) to role PII_ADMIN;\nselect PII._DECRYPT_AES('NhVcyJa8/r3Wdy6WNvT0yQw+SouNYGPAy/ddVe6064Y=', 'O90hS0k9qHdsMDkPe46ZcQ==') as orderid;\n```\n\n\n### d)  This Pipeline's Decryption Secure Function\nFor simplicity of this Lab, we will store the decryption key in a dedicated function, but ideally this is stored in a secrets vault.\n```\nCREATE OR REPLACE SECURE FUNCTION PII.DECRYPT_CDC_FIELD(FIELD string)\nRETURNS VARCHAR \n as\n $$ \n select PII._DECRYPT_AES(FIELD, 'O90hS0k9qHdsMDkPe46ZcQ==')\n $$;\n```\nGrant usage and test\n```\n grant usage on function PII.DECRYPT_CDC_FIELD(varchar) to role PII_ADMIN;\n select PII.DECRYPT_CDC_FIELD('NhVcyJa8/r3Wdy6WNvT0yQw+SouNYGPAy/ddVe6064Y=');\n```\nOk, it works.  Access no longer required.\n```\n revoke usage on function PII.DECRYPT_CDC_FIELD(varchar) from role PII_ADMIN;\n```\n\n### e) Create Secure View\n```\nCreate or replace secure view PII.LIMIT_ORDERS_VW\nas \n  select orderid_tokenized,orderid_encrypted,\n    PII.DECRYPT_CDC_FIELD(orderid_encrypted) as orderid_PII,\n    lastUpdated,client,ticker,position,price,quantity\n  from ENG.LIMIT_ORDERS_CURRENT_DT order by orderid_PII;\n```\nGrant usage to PII User Role\n```\ngrant usage on schema PII to role PII_ALLOWED;\ngrant usage on function PII._DECRYPT_AES(string,string) to role PII_ALLOWED;\ngrant usage on function PII.DECRYPT_CDC_FIELD(varchar) to role PII_ALLOWED;\ngrant select on view PII.LIMIT_ORDERS_VW to role PII_ALLOWED;\n```\n\n### f)  Be PII-Enabled\n```\nuse role PII_ALLOWED;\nuse database VHOL_ENG_CDC;\nselect * from PII.LIMIT_ORDERS_VW order by ORDERID_PII limit 1000;\nselect * from PII.LIMIT_ORDERS_VW where ticker='MMM' and position='LONG' order by ORDERID_PII;\nselect * from PII.LIMIT_ORDERS_VW limit 1000;\n```\nYou can now see the ORDERID in three forms:  Tokenized, Encrypted, and Decrypted.  But only if you have a role for PII.\n\nSnowflake's Granular, Role-Based Access enables you to control who has access to what, at the database/schema, table, column, and row level as you need!\nDynamic Masking and tagging will be more layers of security available soon, but not included or enabled in this preview.\n\n### g)  Check Security\n```\nuse role VHOL;\nuse schema VHOL_ENG_CDC.PII;\nselect * from VHOL_ENG_CDC.PII.LIMIT_ORDERS_VW limit 1000;\n```\n\n\n## Final Steps & Cleanup\n### a) Use Cntrl-C to kill your Streaming App running on your Desktop\n\n### b) See how many transactions you have processed\n```\nuse role VHOL;\nselect count(*) from ENG.CDC_STREAMING_TABLE;\n```\n\n### c) Remove & Cleanup Desktop Directory created for this Streaming Application\n\n### d) Drop Database, removing all objects created by this Hands-on Lab (Optional) \n```\ndrop database VHOL_ENG_CDC;\n```\n\n### e) Drop Warehouse (Optional)\n```\nuse role ACCOUNTADMIN;\ndrop warehouse VHOL_CDC_WH;\n```\n\n### f) Drop Role (Optional)\n```\nuse role ACCOUNTADMIN;\ndrop role VHOL;\ndrop role VHOL_CDC_AGENT;\ndrop role PII_ADMIN;\ndrop role PII_ALLOWED;\n```\n\n## Conclusion\n\n**Congratulations, you have completed this Lab!**\n\n\n\n### What We Covered\n- Setup keypair authentication into Snowflake\n- Used Snowpipe Streaming Java API and streamed data into Snowflake\n- Ingested Semi-Structured JSON data into Snowflake in real-time instead of bulk loading\n- Created Multiple Dynamic Tables for Data Engineering Tasks\n- Secured Sensitive Fields, beginning from the Source, but also decrypted for those authorized\n\n### Resources\n- [Download Reference Architecture](/content/dam/snowflake-site/developers/2024/07/Industrial-IoT_-Snowpipe-Streaming_Reference-Architecture.pdf)\n- [Read the Blog](/blog/manufacturing-data-ingestion-into-snowflake/)\n- [Watch the Demo](https://youtu.be/MIJ2suyE2io?list=TLGG99oO9HwspOgyNDA5MjAyNQ)\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":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c744e6f5b6","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-c951c9adda",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-825ca57d17","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-11a252ffb4","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. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-2151949ee3",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-2e9acfaf4c",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-b05562d7bb","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-ba40d4eedf","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/cdc-snowpipestreaming-dynamictables",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESetting 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