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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":{"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"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-bf80204c6e","quickstartHeroBreadcrumbs":[{"title":"Getting Started with Snowflake and Amazon Data Firehose (ADF)","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/getting-started-with-snowflake-and-aws-kdf","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Getting Started with Snowflake and Amazon Data Firehose (ADF)"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"James Sun","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Snowpipe 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Repo"},":type":"snowflake-site/components/quickstart/quickstart-hero","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/getting-started-with-snowflake-and-aws-kdf"},"flexible_column_cont":{"id":"flexible-column-container-32236feafc","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-efcbc665cb",":items":{"contentfragment":{"id":"contentfragment-91909d229c","paragraphs":["<!---------------------------->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003ESnowflake's Snowpipe streaming capabilities are designed for rowsets with variable arrival frequency.\nIt focuses on lower latency and cost for smaller data sets. This helps data workers stream rows into Snowflake\nwithout requiring files with a more attractive cost/latency profile.\u003C/p\u003E\n","\u003Cp\u003EHere are some of the use cases that can benefit from this integration:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIoT time-series data ingestion\u003C/li\u003E\u003Cli\u003ECDC streams from OLTP systems\u003C/li\u003E\u003Cli\u003ELog ingestion from SIEM systems\u003C/li\u003E\u003Cli\u003EIngestion into ML feature stores\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn our demo, we will use real-time commercial flight data over the San Francisco Bay Area from the \u003Ca href=\"https://opensky-network.org\"\u003EOpensky Network\u003C/a\u003E to illustrate the solution leveraging\nthe \u003Ca href=\"https://aws.amazon.com/about-aws/whats-new/2024/01/stream-data-snowflake-kinesis-data-firehose-snowpipe-streaming-preview/\"\u003Enative integration\u003C/a\u003E between Snowflake and \u003Ca href=\"https://aws.amazon.com/firehose/\"\u003EADF (Amazon Data Firehose)\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe architecture diagram below shows the deployment. A Linux\nEC2 instance (jumphost) will be provisioned in the subnet of an AWS VPC.\nThe Linux jumphost will host the data producer that ingests real-time flight data into the Firehose delivery stream.\u003C/p\u003E\n","\u003Cp\u003EThe data producer calls the data sources' REST API and receives time-series data in JSON format. This data is then ingested into the Firehose delivery stream and delivered to a Snowflake table.\nThe data in Snowflake table can be visualized in real-time with \u003Ca href=\"https://aws.amazon.com/grafana/\"\u003EAMG (Amazon Managed Grafana)\u003C/a\u003E and \u003Ca href=\"https://streamlit.io\"\u003EStreamlit\u003C/a\u003E\nThe historical data can also be analyzed by BI tools like \u003Ca href=\"https://aws.amazon.com/quicksight/?trk=56601b48-df3f-4cb4-9ef7-9f52efa1d0b8&sc_channel=ps&ef_id=Cj0KCQiA_bieBhDSARIsADU4zLebWWM6ZmxRODjR9Xlc7ztNm5JGwqEMSi0EjCLZ9CXYa1YvXL3LMYYaAnV_EALw_wcB:G:s&s_kwcid=AL!4422!3!629393324770!!!g!!\"\u003EAmazon Quicksight\u003C/a\u003E.\nPlease note that in the demo, we are not demonstrating the visualization aspect. We will have a future Quickstart demo that focuses on visualization.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/Overview-2-flight-arch.png\" alt=\"Architecture diagram for the Demo\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/Overview-2-dashboarding.png\" alt=\"Data visualization\"\u003E\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\u003Cli\u003EFamiliarity with AWS Services (e.g. EC2, ADF, etc), Networking and the Management Console\u003C/li\u003E\u003Cli\u003EBasic knowledge of Python and Linux shell scripting\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need Before the Lab\u003C/h3\u003E\n","\u003Cp\u003ETo participate in the virtual hands-on lab, attendees need the following resources.\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 Account on preferred AWS region\u003C/a\u003E with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access\u003C/li\u003E\u003Cli\u003EAn \u003Ca href=\"https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/\"\u003EAWS Account\u003C/a\u003E with \u003Ccode\u003EAdministrator Access\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECreate your own VPC and subnets (This is optional if you have an existing VPC with subnets you can leverage.\n\u003Cul\u003E\u003Cli\u003EIn the AWS account, \u003Ca href=\"https://docs.aws.amazon.com/vpc/latest/userguide/working-with-vpcs.html\"\u003Ecreate a VPC\u003C/a\u003E, preferably in the same region as the Snowflake account\u003C/li\u003E\u003Cli\u003EIn the VPC, \u003Ca href=\"https://docs.aws.amazon.com/vpc/latest/userguide/working-with-subnets.html\"\u003Ecreate subnets\u003C/a\u003E and attach an \u003Ca href=\"https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Internet_Gateway.html\"\u003Einternet gateway\u003C/a\u003E to allow egress traffic to the internet by using a routing table and security group for outbound traffic.\nNote that the subnets can be public or private, for private subnets, you will need to attach a \u003Ca href=\"https://docs.aws.amazon.com/vpc/latest/userguide/vpc-nat-gateway.html\"\u003ENAT gateway\u003C/a\u003E to allow egress traffic to the internet. Public subnets are sufficient for this lab.\u003C/li\u003E\u003Cli\u003EIf you have decided to create your own VPC/subnets, for your convenience, click \u003Ca href=\"https://console.aws.amazon.com/cloudformation/home?region=us-west-2#/stacks/new?stackName=MSK-Snowflake-VPC&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_Snowpipe_Streaming_MSK/MyFullVPC-2pub-2priv.json\"\u003Ehere\u003C/a\u003E to deploy a VPC with a pair of public and private subnets, internet gateway and NAT gateway for you.\nNote that you must have network administrator permissions to deploy these resources.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUsing \u003Ca href=\"https://aws.amazon.com/firehose/\"\u003EADF (Amazon Data Firehose)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EConnecting to EC2 instances with \u003Ca href=\"https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager.html\"\u003EAmazon System Session Manager\u003C/a\u003E, this is an alternative\nto SSH if your instance is in a private subnet\u003C/li\u003E\u003Cli\u003EUsing \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql.html\"\u003ESnowSQL\u003C/a\u003E, the command line client for connecting to Snowflake to execute SQL queries and perform all DDL and DML operations, including loading data into and unloading data out of database tables.\u003C/li\u003E\u003Cli\u003EUsing Snowflake to query tables populated with time-series data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreate an \u003Ca href=\"https://docs.aws.amazon.com/firehose/latest/dev/basic-create.html\"\u003EADF delivery stream\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESetup \u003Ccode\u003EDirect Put\u003C/code\u003E as the source for the ADF delivery stream\u003C/li\u003E\u003Cli\u003ESetup \u003Ccode\u003ESnowflake\u003C/code\u003E as the destination for the ADF delivery stream\u003C/li\u003E\u003Cli\u003EOptionally, secure the connection between Snowflake and ADF with \u003Ca href=\"https://aws.amazon.com/privatelink\"\u003EPrivatelink\u003C/a\u003E. Note \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-editions#security-governance-and-data-protection\"\u003ESnowflake Business Critical Edition\u003C/a\u003E and above is a pre-requisite for Privatelink.\u003C/li\u003E\u003Cli\u003EA Snowflake database and table for hosting real-time flight data\u003C/li\u003E\u003C/ul\u003E\n<!---------------------------->\n","\u003Ch2\u003EProvision a Linux jumphost in AWS\u003C/h2\u003E\n","\u003Ch4\u003E1. Create an EC2 instance\u003C/h4\u003E\n","\u003Cp\u003EFirst, click \u003Ca href=\"https://console.aws.amazon.com/cloudformation/home#/stacks/new?stackName=ADF-Snowflake&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_KDF/kdf-bastion.json\"\u003Ehere\u003C/a\u003E\nto launch an EC2 instance(jumphost) with Cloudformation. Note the default AWS region is \u003Ccode\u003Eus-west-2 (Oregon)\u003C/code\u003E, at the time of writing this quickstart, three regions are available\nfor this integration preview: \u003Ccode\u003Eus-east-1\u003C/code\u003E, \u003Ccode\u003Eus-west-2\u003C/code\u003E, and \u003Ccode\u003Eeu-west-1\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003ESubnet1\u003C/code\u003E, in the drop-down menu, pick an existing subnet, it can be either public or private subnets depending on the network layout of your VPC.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003EInstanceSecurityGroupId\u003C/code\u003E, we recommend using the default security group in your VPC, if you do not have the default security group, create one on your own before moving forward.\u003C/p\u003E\n","\u003Cp\u003EClick Next at the Create stack page. Set the Stack name or modify the default value to customize it to your identity.\u003C/p\u003E\n","\u003Cp\u003ESee below sample screen capture for reference.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/ec2-cft-stack-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELeave everything as default in the \u003Ccode\u003EConfigure stack options\u003C/code\u003E page and click \u003Ccode\u003ENext\u003C/code\u003E.\nIn the \u003Ccode\u003EReview\u003C/code\u003E page, click \u003Ccode\u003ESubmit\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn about 5 minutes, the Cloudformation template provisions a Linux EC2 instance in the subnet you selected. We will then use it to run the ADF producer for data ingestion.\u003C/p\u003E\n","\u003Ch4\u003E2. Configure the Linux session for timeout and default shell\u003C/h4\u003E\n","\u003Cp\u003EIn this step we need to connect to the EC2 instance in order to ingest the real-time data.\u003C/p\u003E\n","\u003Cp\u003EGo to the AWS \u003Ca href=\"https://us-west-2.console.aws.amazon.com/systems-manager/home\"\u003ESystems Manager\u003C/a\u003E console in the same region where you setup the EC2 instance,\nClick \u003Ccode\u003ESession Manager\u003C/code\u003E on the left pane.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, we will set the preferred shell as bash.\u003C/p\u003E\n","\u003Cp\u003EClick the \u003Ccode\u003EPreferences\u003C/code\u003E tab.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.0.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick the \u003Ccode\u003EEdit\u003C/code\u003E button.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGo to \u003Ccode\u003EGeneral preferences\u003C/code\u003E section, type in 60 minutes for idle session timeout value.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.1.5.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFurther scroll down to \u003Ccode\u003ELinux shell profile\u003C/code\u003E section, and type in \u003Ccode\u003E/bin/bash\u003C/code\u003E before clicking \u003Ccode\u003ESave\u003C/code\u003E button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E3. Connect to the Linux EC2 instance console\u003C/h4\u003E\n","\u003Cp\u003ENow go back to the \u003Ccode\u003ESession\u003C/code\u003E tab and click the \u003Ccode\u003EStart session\u003C/code\u003E button.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow you should see the EC2 instance created by the Cloudformation template under \u003Ccode\u003ETarget instances\u003C/code\u003E.\nIts name should be \u003Ccode\u003E<Cloudformation stack name>-jumphost\u003C/code\u003E, select it and click \u003Ccode\u003EStart session\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E4. Create a key-pair to be used for authenticating with Snowflake\u003C/h4\u003E\n","\u003Cp\u003ECreate a key pair in AWS Session Manager console by executing the following commands. You will be prompted to give an encryption password, remember\nthis phrase, you will need it later.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecd $HOME\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESee below example screenshot:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-sessionmgr-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext we will create a public key by running following commands. You will be prompted to type in the phrase you used in above step.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Esee below example screenshot:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-sessionmgr-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext we will print out the public and private key string in a correct format that we can use for configuration later.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Egrep -v KEY rsa_key.pub | tr -d '\\n' | awk '{print $1}' > pub.Key\ncat pub.Key\n\ngrep -v KEY rsa_key.p8 | tr -d '\\n' | awk '{print $1}' > priv.Key\ncat priv.Key\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Esee below example screenshot:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-sessionmgr-3.png\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EPrepare the Snowflake account for streaming\u003C/h2\u003E\n","\u003Ch4\u003E1. Creating user, role, and database\u003C/h4\u003E\n","\u003Cp\u003EFirst login to your Snowflake account as a power user with ACCOUNTADMIN role.\nThen run the following SQL commands in a worksheet to create a user, database and the role that we will use in the lab.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E-- Set default value for multiple variables\n-- For purpose of this workshop, it is recommended to use these defaults during the exercise to avoid errors\n-- You should change them after the workshop\nSET PWD = 'Test1234567';\nSET USER = 'STREAMING_USER';\nSET DB = 'ADF_STREAMING_DB';\nSET WH = 'ADF_STREAMING_WH';\nSET ROLE = 'ADF_STREAMING_RL';\n\nUSE ROLE ACCOUNTADMIN;\n\n-- CREATE USERS\nCREATE USER IF NOT EXISTS IDENTIFIER($USER) PASSWORD=$PWD  COMMENT='STREAMING USER';\n\n-- CREATE ROLES\nCREATE OR REPLACE ROLE IDENTIFIER($ROLE);\n\n-- CREATE DATABASE AND WAREHOUSE\nCREATE DATABASE IF NOT EXISTS IDENTIFIER($DB);\nUSE IDENTIFIER($DB);\nCREATE OR REPLACE WAREHOUSE IDENTIFIER($WH) WITH WAREHOUSE_SIZE = 'SMALL';\n\n-- GRANTS\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE IDENTIFIER($ROLE);\nGRANT ROLE IDENTIFIER($ROLE) TO USER IDENTIFIER($USER);\nGRANT OWNERSHIP ON DATABASE IDENTIFIER($DB) TO ROLE IDENTIFIER($ROLE);\nGRANT USAGE ON WAREHOUSE IDENTIFIER($WH) TO ROLE IDENTIFIER($ROLE);\n\n-- SET DEFAULTS\nALTER USER IDENTIFIER($USER) SET DEFAULT_ROLE=$ROLE;\nALTER USER IDENTIFIER($USER) SET DEFAULT_WAREHOUSE=$WH;\n\n-- RUN FOLLOWING COMMANDS TO FIND YOUR ACCOUNT IDENTIFIER, COPY IT DOWN FOR USE LATER\n-- IT WILL BE SOMETHING LIKE <organization_name>-<account_name>\n-- e.g. ykmxgak-wyb52636\n\nWITH HOSTLIST AS \n(SELECT * FROM TABLE(FLATTEN(INPUT => PARSE_JSON(SYSTEM$allowlist()))))\nSELECT REPLACE(VALUE:host,'.snowflakecomputing.com','') AS ACCOUNT_IDENTIFIER\nFROM HOSTLIST\nWHERE VALUE:type = 'SNOWFLAKE_DEPLOYMENT_REGIONLESS';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPlease write down the Account Identifier, we will need it later.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/account-identifier.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe need to retrieve the \u003Ccode\u003ESnowflake private account URL\u003C/code\u003E for use later, run below SQL command now and record the output, e.g. \u003Ccode\u003Ehttps://xyz12345.us-west-2.privatelink.snowflakecomputing.com\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith PL as\n(SELECT * FROM TABLE(FLATTEN(INPUT => PARSE_JSON(SYSTEM$GET_PRIVATELINK_CONFIG()))) where key = 'privatelink-account-url')\nSELECT concat('https://'|| REPLACE(VALUE,'"','')) AS SNOWFLAKE_PRIVATE_ACCOUNT_URL\nfrom PL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we need to retrieve the value of \u003Ccode\u003EVPCE ID\u003C/code\u003E for use later, run below SQL command now and record the output, e.g. \u003Ccode\u003Ecom.amazonaws.vpce.us-west-2.vpce-svc-xyzabce999777333f1\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith PL as\n(SELECT * FROM TABLE(FLATTEN(INPUT => PARSE_JSON(SYSTEM$GET_PRIVATELINK_CONFIG()))) where key = 'privatelink-vpce-id')\nSELECT REPLACE(VALUE,'"','') AS PRIVATE_LINK_VPCE_ID\nfrom PL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we need to configure the public key for the streaming user to access Snowflake programmatically.\u003C/p\u003E\n","\u003Cp\u003EFirst, in the Snowflake worksheet, replace < pubKey > with the content of the file \u003Ccode\u003E/home/azureuser/pub.Key\u003C/code\u003E (see \u003Ccode\u003Estep 4\u003C/code\u003E in \u003Ccode\u003Esection #2 Provision a Linux jumphost in AWS\u003C/code\u003E located in the left pane) in the following SQL command and execute.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Euse role accountadmin;\nalter user streaming_user set rsa_public_key='< pubKey >';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESee below example screenshot:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-snowflake.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow logout of Snowflake, sign back in as the default user \u003Ccode\u003Estreaming_user\u003C/code\u003E we just created with the associated password (default: Test1234567).\nRun the following SQL commands in a worksheet to create a schema (e.g. \u003Ccode\u003EADF_STREAMING_SCHEMA\u003C/code\u003E) in the default database (e.g. \u003Ccode\u003EADF_STREAMING_DB\u003C/code\u003E):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003ESET DB = 'ADF_STREAMING_DB';\nSET SCHEMA = 'ADF_STREAMING_SCHEMA';\n\nUSE IDENTIFIER($DB);\nCREATE OR REPLACE SCHEMA IDENTIFIER($SCHEMA);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E2. Install SnowSQL (optional but highly recommended)\u003C/h4\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql.html\"\u003ESnowSQL\u003C/a\u003E is the command line client for connecting to Snowflake to execute SQL queries and perform all DDL and DML operations, including loading data into and unloading data out of database tables.\u003C/p\u003E\n","\u003Cp\u003ETo install SnowSQL. Execute the following commands on the Linux Session Manager console:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecurl https://sfc-repo.snowflakecomputing.com/snowsql/bootstrap/1.2/linux_x86_64/snowsql-1.2.24-linux_x86_64.bash -o /tmp/snowsql-1.2.24-linux_x86_64.bash\necho -e "~/bin \\n y" > /tmp/ans\nbash /tmp/snowsql-1.2.24-linux_x86_64.bash < /tmp/ans\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESee below example screenshot:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/install-snowsql.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext set the environment variable for Snowflake Private Key Phrase:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Eexport SNOWSQL_PRIVATE_KEY_PASSPHRASE=<key phrase you set up when running openssl previously>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that you should add the command above in the ~/.bashrc file to preserve this environment variable across sessions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Eecho "export SNOWSQL_PRIVATE_KEY_PASSPHRASE=$SNOWSQL_PRIVATE_KEY_PASSPHRASE" >> ~/.bashrc\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow you can execute this command to interact with Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003E$HOME/bin/snowsql -a <The Account Identifier that you recorded earlier> -u streaming_user --private-key-path $HOME/rsa_key.p8 -d adf_streaming_db -s adf_streaming_schema\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESee below example screenshot:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-snowsql.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EType \u003Ccode\u003ECtrl-D\u003C/code\u003E to get out of SnowSQL session.\u003C/p\u003E\n","\u003Cp\u003EYou can edit the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql-config.html#snowsql-config-file\"\u003E\u003Ccode\u003E~/.snowsql/config\u003C/code\u003E\u003C/a\u003E file to set default parameters and eliminate the need to specify them every time you run snowsql.\u003C/p\u003E\n","\u003Cp\u003EAt this point, the Snowflake setup is complete.\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003ECreate an ADF delivery stream\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we are going to create an ADF delivery stream for data streaming.\u003C/p\u003E\n","\u003Cp\u003ENavigate to the \u003Ca href=\"https://console.aws.amazon.com/firehose/home?streams\"\u003EADF console\u003C/a\u003E and click \u003Ccode\u003ECreate delivery stream\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003ESource\u003C/code\u003E section, select \u003Ccode\u003EDirect PUT\u003C/code\u003E from the drop-down menu. Optionally, you can pick \u003Ccode\u003EKinesis Data Streams\u003C/code\u003E as the source, but we will focus on \u003Ccode\u003EDirect Put\u003C/code\u003E in this workshop.\u003C/p\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003EDestination\u003C/code\u003E section, select \u003Ccode\u003ESnowflake\u003C/code\u003E from the drop-down menu.\u003C/p\u003E\n","\u003Cp\u003EType in a name for the \u003Ccode\u003EFirehose stream name\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-stream-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESkip \u003Ccode\u003ETransform records\u003C/code\u003E setup.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003ESnowflake account URL\u003C/code\u003E, enter the URL you recorded previously from step 1 in Chapter 3, e.g. \u003Ccode\u003Ehttps://xyz12345.us-west-2.privatelink.snowflakecomputing.com\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ENote here we are going to use Amazon PrivateLink (Note, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-editions#security-governance-and-data-protection\"\u003ESnowflake Business Critical Edition\u003C/a\u003E or above is required) to secure the communication between Snowflake and ADF, so the URL is a private endpoint with \u003Ccode\u003Eprivatelink\u003C/code\u003E as a substring.\u003C/p\u003E\n","\u003Cp\u003EAlternatively, you can use the public endpoint without the \u003Ccode\u003Eprivatelink\u003C/code\u003E substring, e.g. \u003Ccode\u003Ehttps://xyz12345.us-west-2.snowflakecomputing.com\u003C/code\u003E, if this is the case, also leave the \u003Ccode\u003EVPCE ID\u003C/code\u003E field blank below.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003EUser\u003C/code\u003E, type in \u003Ccode\u003ESTREAMING_USER\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003EPrivate key\u003C/code\u003E, go back to your EC2 console in Systems Manager and run\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecat ~/priv.key\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy the output string and paste into the \u003Ccode\u003EPrivate key\u003C/code\u003E field.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003EPassphrase\u003C/code\u003E, type in the phrase you used when generating the public key with openssl earlier.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-stream-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003ERole\u003C/code\u003E, select \u003Ccode\u003EUse custom Snowflake role\u003C/code\u003E and type in \u003Ccode\u003EADF_STREAMING_RL\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003EVPCE ID\u003C/code\u003E, enter the value you recorded from step 1 in Chapter 3, e.g. \u003Ccode\u003Ecom.amazonaws.vpce.us-west-2.vpce-svc-xyzabce999777333f1\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003ESnowflake database\u003C/code\u003E, type in \u003Ccode\u003EADF_STREAMING_DB\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003ESnowflake Schema\u003C/code\u003E, type in \u003Ccode\u003EADF_STREAMING_SCHEMA\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003ESnowflake table\u003C/code\u003E, type in \u003Ccode\u003EADF_STREAMING_TBL\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003EData loading options for your Snowflake table\u003C/code\u003E, select \u003Ccode\u003EUse JSON keys as table column names\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-stream-3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor \u003Ccode\u003ES3 backup bucket\u003C/code\u003E, pick an existing S3 bucket where you want to save the logs or error messages. \u003Ca href=\"https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html\"\u003ECreate a S3 bucket\u003C/a\u003E if you don't have one.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-stream-4.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELeave everything else as default and click \u003Ccode\u003ECreate delivery stream\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EYour delivery stream will be generated in about 5 minutes.\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EIngest and Query data in Snowflake\u003C/h2\u003E\n","\u003Cp\u003ENow, switch back to the Snowflake console and make sure that you signed in as the default user \u003Ccode\u003Estreaming_user\u003C/code\u003E.\nThe data should have been streamed into a table, ready for further processing.\u003C/p\u003E\n","\u003Ch4\u003E1. Create a destination table in Snowflake\u003C/h4\u003E\n","\u003Cp\u003ERun the following SQL command to create the table \u003Ccode\u003EADF_STREAMING_TBL\u003C/code\u003E we specified when provisioning\nthe delivery stream. Note that here we use varchar type for most of the columns, we will\ngenerate a view later to transform them into the correct types.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Euse ADF_STREAMING_DB;\nuse schema ADF_STREAMING_SCHEMA;\ncreate or replace TABLE ADF_STREAMING_TBL (\n\tORIG VARCHAR(20),\n\tUTC NUMBER(38,0),\n\tALT VARCHAR(20),\n\tICAO VARCHAR(20),\n\tLON VARCHAR(20),\n\tID VARCHAR(20),\n\tDEST VARCHAR(20),\n\tLAT VARCHAR(20)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E2. Ingest real-time data\u003C/h4\u003E\n","\u003Cp\u003EGo to the EC2 console, and run the following command.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Epython3 /tmp/adf-producer.py <ADF delivery stream name>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe Python script gets the raw flight data from a \u003Ca href=\"http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky\"\u003Ereal-time source\u003C/a\u003E and streams into the delivery stream.\nYou should see the flight data being ingested continuously to the ADF delivery stream in json format.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-producer-ingest.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E3. Query the raw data in Snowflake\u003C/h4\u003E\n","\u003Cp\u003ETo verify that data has been streamed into Snowflake, execute the following SQL commands.\u003C/p\u003E\n","\u003Cp\u003ENow run the following query on the table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from adf_streaming_tbl;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere is the screen capture of the sample output.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/raw_data.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E2. Convert the raw data table into a view with correct data types\u003C/h4\u003E\n","\u003Cp\u003ENow execute the following SQL command.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecreate or replace view flights_vw\n  as select\n    utc::timestamp_ntz ts_utc,\n    CONVERT_TIMEZONE('UTC','America/Los_Angeles',ts_utc::timestamp_ntz) as ts_pt,\n    alt::integer alt,\n    dest::string dest,\n    orig::string orig,\n    id::string id,\n    icao::string icao,\n    lat::float lat,\n    lon::float lon,\n    st_geohash(to_geography(st_makepoint(lon, lat)),12) geohash,\n    st_distance(st_makepoint(-122.366340, 37.616245), st_makepoint(lon, lat))/1609::float dist_to_sfo,\n    year(ts_pt) yr,\n    month(ts_pt) mo,\n    day(ts_pt) dd,\n    hour(ts_pt) hr\nFROM adf_streaming_tbl;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe SQL command creates a view, converts timestamps to different time zones, and use Snowflake's \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/st_geohash.html\"\u003EGeohash function\u003C/a\u003E to generate geohashes that can be used in time-series visualization tools such as Grafana.\nYou can also easily calculate the distance in miles between two geo locations. In above example, the \u003Ccode\u003Est_distance\u003C/code\u003E function is used to calculate the distance between an airplane and San Francisco Airport.\u003C/p\u003E\n","\u003Cp\u003ELet's query the view \u003Ccode\u003Eflights_vw\u003C/code\u003E now.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from flights_vw;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs a result, you will see a nicely structured output with columns derived from the JSONs at the \u003Ca href=\"http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky\"\u003Esource\u003C/a\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/flight_view.png\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EUse Copilot to interact with your data in natural language\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-copilot\"\u003ESnowflake Copilot\u003C/a\u003E is an LLM-powered assistant that simplifies data analysis while maintaining robust data governance, and seamlessly integrates into your existing Snowflake workflow. You can gain insights from your data stored in Snowflake by using natural language.\u003C/p\u003E\n","\u003Cp\u003ETo get started, on the console, click 'Ask Copilot' button located at the bottom.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/ask-copilot.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the conversation window, type in a question like the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eshow me the unique flights from KLAX arriving at KSFO between 2000 to 4000 feet altitude?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopilot will then analyze the question and show you its thinking process step-by-step and give you the SQL command to get the answer.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/copilot1.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's ask Copilot another question that uses Snowflake's geo-spatial functions to calculate the distance between two locations.\u003C/p\u003E\n","\u003Cp\u003EAsk a question like the following.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eif the lattitude and longitude for KSFO is 37.619254,-122.4491623 respectively, calculate the distance (in miles) between the flights and KSFO with Snowflake's geo-spatial functions, also show the related flight ids, original and destination airports\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAgain, Copilot gives the SQL command which you can execute to get the distance between all flights to San Francisco internation airport.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/copilot2.gif\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EUse Amazon Managed Service for Flink for real-time analytics - Optional\u003C/h2\u003E\n","\u003Cp\u003ENote that you will need to complete the base workshop first without cleaning up the resources in order to proceed.\u003C/p\u003E\n","\u003Cp\u003EApache Flink is a powerful stream processing framework with a wide range of use cases across industries. Some common applications include Real-time analytics, Fraud detection, Sendor data process, Supply chain optimization, and many more.\u003C/p\u003E\n","\u003Cp\u003EHere we will show you how to integrate our current demo with \u003Ca href=\"https://aws.amazon.com/managed-service-apache-flink/\"\u003EAmazon Managed Service for Apache Flink\u003C/a\u003E and \u003Ca href=\"https://aws.amazon.com/kinesis/data-streams/\"\u003EKinesis Data Streams (KDS)\u003C/a\u003E to do real-time analytics.\u003C/p\u003E\n","\u003Cp\u003EThe schematic diagram below illustrates the flow of data from the source, which is streamed into an input Kinesis stream. The data is then processed by \u003Ca href=\"https://docs.aws.amazon.com/managed-flink/latest/java/how-sinks.html#sinks-firehose-create\"\u003EFlink Studio notebook\u003C/a\u003E in real-time, before being ingested into an output Kinesis stream, it is picked up by Data Firehose and ultimately lands in Snowflake. \u003Ca href=\"https://docs.aws.amazon.com/prescriptive-guidance/latest/serverless-etl-aws-glue/aws-glue-data-catalog.html\"\u003EAWS Glue Data Catalog\u003C/a\u003E serves as a metadata store for Flink Studio notebook tables.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/flink-schematic.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will be focusing on using Flink Studio notebook for this demo. Optionally, if you are writing Flink applications instead of using the Flink Studio notebook, you can use the \u003Ca href=\"https://docs.aws.amazon.com/managed-flink/latest/java/how-sinks.html#sinks-firehose-create\"\u003EFirehose Producer\u003C/a\u003E to bypass the output Kinesis stream.\u003C/p\u003E\n","\u003Ch4\u003E1. Create a table in Snowflake to receive Flink-filtered data\u003C/h4\u003E\n","\u003Cp\u003ELog into the Snowflake account as \u003Ccode\u003Estreaming_user\u003C/code\u003E. Run the following SQL commands to generate a table for capturing the filtered streams.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse ADF_STREAMING_DB;\nuse schema ADF_STREAMING_SCHEMA;\ncreate or replace TABLE ADF_FLINK_TBL (\n\tORIG VARCHAR(5),\n\tUTC VARCHAR(20),\n\tALT INTEGER,\n\tICAO VARCHAR(20),\n\tLON FLOAT,\n\tID VARCHAR(10),\n\tDEST VARCHAR(5),\n\tLAT FLOAT\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E2. Deploy Flink Studio notebook and Kinesis Data Streams\u003C/h4\u003E\n","\u003Cp\u003ETo make the process of deploying necessary resources easier, click \u003Ca href=\"https://console.aws.amazon.com/cloudformation/home#/stacks/new?stackName=amf-snowflake&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_ADF/flink-kds-cfn.json\"\u003Ehere\u003C/a\u003E to deploy necessary resources including a \u003Ca href=\"https://docs.aws.amazon.com/managed-flink/latest/java/how-notebook.html\"\u003EFlink Studio notebook\u003C/a\u003E, a Glue database to store metadata of the tables in Flink and two Kinesis Data Streams (KDS). One Kinesis stream serves as the input stream to Flink and the other one serves as the output stream.\u003C/p\u003E\n","\u003Cp\u003EPlease enter appropriate values into the empty fields where you entered(i.e. bucket, private key, keyphrase, etc.) in previous modules when prompted during Cloudformation deployment. In about 5 minutes, the template should be deployed successfully.\u003C/p\u003E\n","\u003Cp\u003EThe Cloudformant template will be deployed in about 5 minutes, navigate to the\u003C/p\u003E\n","\u003Ch4\u003E3. Configure Flink Studio notebook\u003C/h4\u003E\n","\u003Cp\u003ENavigate to the \u003Ca href=\"https://console.aws.amazon.com/flink/home#/list/notebooks\"\u003EStudio notebook console\u003C/a\u003E, you should see that your notebook status is ready. Check the notebook and click \u003Ccode\u003ERun\u003C/code\u003E button at the top. The notebook status should change to \u003Ccode\u003ERunning\u003C/code\u003E in about 5 minutes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/run-flink-nb.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENavigate to the CloudFormation stack that we successfully deployed (e.g.\u003Ccode\u003Eamf_snowflake\u003C/code\u003E) and click on the \u003Ccode\u003EOutputs\u003C/code\u003E tab to record the values for \u003Ccode\u003EKinesisDataInputStream\u003C/code\u003E and \u003Ccode\u003EKinesisDataOutputStream\u003C/code\u003E, we will need them later.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/cfn-output.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will also notice notice that a Glue database is also created. Navigate to the \u003Ca href=\"https://us-west-2.console.aws.amazon.com/glue/home#/v2/data-catalog/databases\"\u003EGlue console\u003C/a\u003E to verify.\u003C/p\u003E\n","\u003Ch4\u003E4. Configure Zeppelin notebook\u003C/h4\u003E\n","\u003Cp\u003EClick \u003Ccode\u003EOpen Apache Zeppelin\u003C/code\u003E when the notebook is running.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/zeppelin.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDownload a Zeppelin note from \u003Ca href=\"https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_ADF/Flink-nb.zpln\"\u003Ehere\u003C/a\u003E, and save it to your desktop.\u003C/p\u003E\n","\u003Cp\u003EIn Zeppelin, click \u003Ccode\u003Eimport note\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/zeppelin-import-note-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGive the note a name, i.e. \u003Ccode\u003EmyNote\u003C/code\u003E. Select \u003Ccode\u003ESelect JSON File/IPYNB File\u003C/code\u003E when prompted.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/zeppelin-import-note-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPick the \u003Ccode\u003EFlink-nb.zpln\u003C/code\u003E file you just dowonloaded. You should now see the note name in the browser window, click the note to open.\u003C/p\u003E\n","\u003Ch4\u003E5. Run Flink notebook\u003C/h4\u003E\n","\u003Cp\u003EIn the left cell, type in the value for \u003Ccode\u003EKinesisDataInputStream\u003C/code\u003E you recorded from step 3 above, change \u003Ccode\u003Eaws.region\u003C/code\u003E to your local region. Highlight the content of the left cell, then click the triangle play button at the top. You should see the status changing from \u003Ccode\u003EPending\u003C/code\u003E to \u003Ccode\u003ERunning\u003C/code\u003E to \u003Ccode\u003EFinished\u003C/code\u003E. This creates a Flink input table which maps to the input Kinesis data stream.\u003C/p\u003E\n","\u003Cp\u003EDo the same thing for the right cell. A Flink output table is also created.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/flink-nb-run-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, scroll down to the remaining two cells to start filtering and monitoring by clicking the play button located at the top-right corner of each cell.\u003C/p\u003E\n","\u003Cp\u003EFirst cell is to filter out any live United Airlines flight tracks below 7000 feet and the other cell is to monitor the filtered results.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/flink-nb-run-3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELeave your Flink notebook window open.\u003C/p\u003E\n","\u003Ch4\u003E6. Start ingesting live data to the input Kinesis data stream\u003C/h4\u003E\n","\u003Cp\u003EWe are ready to ingest data now. Go to your EC2 console via \u003Ca href=\"https://console.aws.amazon.com/systems-manager/session-manager\"\u003ESession Manager\u003C/a\u003E as instructed in step 3 of Chapter 2.\u003C/p\u003E\n","\u003Cp\u003EKick off the ingestion by executing below shell command, replace \u003Ccode\u003E<your input Kinesis stream>\u003C/code\u003E with the name of your input Kinesis stream.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Ecurl -s https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_ADF/kds-producer.py | python3 - <your input Kinesis stream name>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kds-ingestion.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn a short moment, you will see some filtered data appearing in the Flink notebook monitoring cell and they are all UA airplanes flying below 7000 feet.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/real-time-analytics.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E7. Verify the results in Snowflake\u003C/h4\u003E\n","\u003Cp\u003ENow go back to your Snowflake account as user 'streaming_user', and run the following SQL commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ADF_STREAMING_DB;\nUSE SCHEMA ADF_STREAMING_SCHEMA;\nSELECT * from ADF_FLINK_TBL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the filtered flight tracks are captured in table \u003Ccode\u003EADF_FLINK_TBL\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/verify-snowflake-result.png\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EWhen you are done with the demo, to tear down the AWS resources, simply go to the \u003Ca href=\"https://console.aws.amazon.com/cloudformation/home?stacks\"\u003ECloudformation\u003C/a\u003E console.\nSelect the Cloudformation template you used to deploy the jumphost at the start of the demo, also the template for Amazon Managed Flink if you optionally deployed it, then click the \u003Ccode\u003EDelete\u003C/code\u003E tab.\u003C/p\u003E\n","\u003Cp\u003ESee example screen capture below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/cleanup.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENavigate to the \u003Ca href=\"https://console.aws.amazon.com/ec2/home#Instances:instanceState=running\"\u003EEC2 console\u003C/a\u003E and delete the jumphost.\u003C/p\u003E\n","\u003Cp\u003EYou will also need to delete the Firehose delivery stream. Navigate to the \u003Ca href=\"https://console.aws.amazon.com/firehose/home?streams\"\u003EADF Console\u003C/a\u003E, select the delivery stream you created, and select \u003Ccode\u003EDelete\u003C/code\u003E button at the top.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/delete-adf.png\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003EIn this lab, we built a demo to show how to ingest real-time data using Amazon Data Firehose with low latency. We demonstrated this using an ADF\nconnector on an EC2 instance. Alternatively, if you have infrastructure supported by either \u003Ca href=\"https://aws.amazon.com/eks/\"\u003EAmazon EKS\u003C/a\u003E or\n\u003Ca href=\"https://aws.amazon.com/ecs/\"\u003EAmazon ECS\u003C/a\u003E, you can use them to host your containerized ADF producers as well.\u003C/p\u003E\n","\u003Cp\u003EFor those of you who are interested in learning more about how to build sleek dashboards for monitoring the live flight data, please navigate to this\n\u003Ca href=\"https://quickstarts.snowflake.com/guide/404.html/\"\u003Equickstart\u003C/a\u003E to continue.\u003C/p\u003E\n","\u003Cp\u003ERelated Resources\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/blogs/big-data/uplevel-your-data-architecture-with-real-time-streaming-using-amazon-data-firehose-and-snowflake/\"\u003EUplevel your data architecture with real- time streaming using Amazon Data Firehose and Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/unleashing-the-full-potential-of-real-time-streaming-with-amazon-kinesis-data-firehose-and-snowpipe-0283fb599364#Snowflake\"\u003EUnleashing the Full Potential of Real-Time Streaming with Amazon Kinesis Data Firehose and Snowpipe Streaming\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/firehose/\"\u003EAmazon Data Firehose (ADF)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/managed-service-apache-flink/\"\u003EAmazon Managed Service for Apache Flink\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/snowpipe-streaming-demystified-e1ee385c6d9c\"\u003ESnowpipe Streaming Demystified\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/404.html/\"\u003EGetting Started with Amazon Managed Service for Grafana and Streamlit On Real-time Dashboarding\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/\"\u003EGetting started with Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/marketplace/seller-profile?id=18d60ae8-2c99-4881-a31a-e74770d70347\"\u003ESnowflake on AWS Marketplace\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/Workloads/data-sharing/\"\u003ESnowflake for Data Sharing\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/data-cloud/marketplace/\"\u003ESnowflake Marketplace\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"This guide will walk you through how to apply a data-intensive approach to real-time data using Snowpipe Streaming and Amazon Data Firehose (ADF).","title":"Getting Started with Snowflake and Amazon Data Firehose (ADF)","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!----------------------------\u003E\n## Overview\n\nSnowflake's Snowpipe streaming capabilities are designed for rowsets with variable arrival frequency.\nIt focuses on lower latency and cost for smaller data sets. This helps data workers stream rows into Snowflake\nwithout requiring files with a more attractive cost/latency profile.\n\nHere are some of the use cases that can benefit from this integration:\n- IoT time-series data ingestion\n- CDC streams from OLTP systems \n- Log ingestion from SIEM systems\n- Ingestion into ML feature stores \n\nIn our demo, we will use real-time commercial flight data over the San Francisco Bay Area from the [Opensky Network](https://opensky-network.org) to illustrate the solution leveraging\nthe [native integration](https://aws.amazon.com/about-aws/whats-new/2024/01/stream-data-snowflake-kinesis-data-firehose-snowpipe-streaming-preview/) between Snowflake and [ADF (Amazon Data Firehose)](https://aws.amazon.com/firehose/).\n\nThe architecture diagram below shows the deployment. A Linux \nEC2 instance (jumphost) will be provisioned in the subnet of an AWS VPC. \nThe Linux jumphost will host the data producer that ingests real-time flight data into the Firehose delivery stream.\n\nThe data producer calls the data sources' REST API and receives time-series data in JSON format. This data is then ingested into the Firehose delivery stream and delivered to a Snowflake table.\nThe data in Snowflake table can be visualized in real-time with [AMG (Amazon Managed Grafana)](https://aws.amazon.com/grafana/) and [Streamlit](https://streamlit.io)\nThe historical data can also be analyzed by BI tools like [Amazon Quicksight](https://aws.amazon.com/quicksight/?trk=56601b48-df3f-4cb4-9ef7-9f52efa1d0b8&sc_channel=ps&ef_id=Cj0KCQiA_bieBhDSARIsADU4zLebWWM6ZmxRODjR9Xlc7ztNm5JGwqEMSi0EjCLZ9CXYa1YvXL3LMYYaAnV_EALw_wcB:G:s&s_kwcid=AL!4422!3!629393324770!!!g!!).\nPlease note that in the demo, we are not demonstrating the visualization aspect. We will have a future Quickstart demo that focuses on visualization.\n\n![Architecture diagram for the Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/Overview-2-flight-arch.png)\n\n![Data visualization](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/Overview-2-dashboarding.png)\n\n### Prerequisites\n\n- Familiarity with Snowflake, basic SQL knowledge, Snowsight UI and Snowflake objects\n- Familiarity with AWS Services (e.g. EC2, ADF, etc), Networking and the Management Console\n- Basic knowledge of Python and Linux shell scripting\n\n### What You'll Need Before the Lab\n\nTo participate in the virtual hands-on lab, attendees need the following resources.\n\n- A [Snowflake Enterprise Account on preferred AWS region](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with `ACCOUNTADMIN` access\n- An [AWS Account](https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/) with `Administrator Access`\n- Create your own VPC and subnets (This is optional if you have an existing VPC with subnets you can leverage.\n  - In the AWS account, [create a VPC](https://docs.aws.amazon.com/vpc/latest/userguide/working-with-vpcs.html), preferably in the same region as the Snowflake account\n  - In the VPC, [create subnets](https://docs.aws.amazon.com/vpc/latest/userguide/working-with-subnets.html) and attach an [internet gateway](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Internet_Gateway.html) to allow egress traffic to the internet by using a routing table and security group for outbound traffic.\n  Note that the subnets can be public or private, for private subnets, you will need to attach a [NAT gateway](https://docs.aws.amazon.com/vpc/latest/userguide/vpc-nat-gateway.html) to allow egress traffic to the internet. Public subnets are sufficient for this lab.\n  - If you have decided to create your own VPC/subnets, for your convenience, click [here](https://console.aws.amazon.com/cloudformation/home?region=us-west-2#/stacks/new?stackName=MSK-Snowflake-VPC&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_Snowpipe_Streaming_MSK/MyFullVPC-2pub-2priv.json) to deploy a VPC with a pair of public and private subnets, internet gateway and NAT gateway for you. \nNote that you must have network administrator permissions to deploy these resources. \n\n### What You'll Learn\n\n- Using [ADF (Amazon Data Firehose)](https://aws.amazon.com/firehose/)\n- Connecting to EC2 instances with [Amazon System Session Manager](https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager.html), this is an alternative\nto SSH if your instance is in a private subnet\n- Using [SnowSQL](https://docs.snowflake.com/en/user-guide/snowsql.html), the command line client for connecting to Snowflake to execute SQL queries and perform all DDL and DML operations, including loading data into and unloading data out of database tables.\n- Using Snowflake to query tables populated with time-series data\n\n### What You'll Build\n\n- Create an [ADF delivery stream](https://docs.aws.amazon.com/firehose/latest/dev/basic-create.html)\n- Setup `Direct Put` as the source for the ADF delivery stream\n- Setup `Snowflake` as the destination for the ADF delivery stream\n- Optionally, secure the connection between Snowflake and ADF with [Privatelink](https://aws.amazon.com/privatelink). Note [Snowflake Business Critical Edition](https://docs.snowflake.com/en/user-guide/intro-editions#security-governance-and-data-protection) and above is a pre-requisite for Privatelink.\n- A Snowflake database and table for hosting real-time flight data\n\n\u003C!----------------------------\u003E\n## Provision a Linux jumphost in AWS\n\n#### 1. Create an EC2 instance\nFirst, click [here](https://console.aws.amazon.com/cloudformation/home#/stacks/new?stackName=ADF-Snowflake&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_KDF/kdf-bastion.json)\nto launch an EC2 instance(jumphost) with Cloudformation. Note the default AWS region is `us-west-2 (Oregon)`, at the time of writing this quickstart, three regions are available\nfor this integration preview: `us-east-1`, `us-west-2`, and `eu-west-1`.\n\nFor `Subnet1`, in the drop-down menu, pick an existing subnet, it can be either public or private subnets depending on the network layout of your VPC. \n\nFor `InstanceSecurityGroupId`, we recommend using the default security group in your VPC, if you do not have the default security group, create one on your own before moving forward.\n\nClick Next at the Create stack page. Set the Stack name or modify the default value to customize it to your identity.\n\nSee below sample screen capture for reference.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/ec2-cft-stack-1.png)\n\nLeave everything as default in the `Configure stack options` page and click `Next`.\nIn the `Review` page, click `Submit`.\n\nIn about 5 minutes, the Cloudformation template provisions a Linux EC2 instance in the subnet you selected. We will then use it to run the ADF producer for data ingestion.\n\n#### 2. Configure the Linux session for timeout and default shell\n\nIn this step we need to connect to the EC2 instance in order to ingest the real-time data.\n\nGo to the AWS [Systems Manager](https://us-west-2.console.aws.amazon.com/systems-manager/home) console in the same region where you setup the EC2 instance,\nClick `Session Manager` on the left pane.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.png)\n\nNext, we will set the preferred shell as bash.\n\nClick the `Preferences` tab.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.0.png)\n\nClick the `Edit` button.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.1.png)\n\nGo to `General preferences` section, type in 60 minutes for idle session timeout value.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.1.5.png)\n\nFurther scroll down to `Linux shell profile` section, and type in `/bin/bash` before clicking `Save` button.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-1.2.png)\n\n\n#### 3. Connect to the Linux EC2 instance console\nNow go back to the `Session` tab and click the `Start session` button.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-2.png)\n\nNow you should see the EC2 instance created by the Cloudformation template under `Target instances`.\nIts name should be `\u003CCloudformation stack name\u003E-jumphost`, select it and click `Start session`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/session-mgr-3.png)\n\n#### 4. Create a key-pair to be used for authenticating with Snowflake\nCreate a key pair in AWS Session Manager console by executing the following commands. You will be prompted to give an encryption password, remember \nthis phrase, you will need it later.\n\n```commandline\ncd $HOME\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8\n```\nSee below example screenshot:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-sessionmgr-1.png)\n\nNext we will create a public key by running following commands. You will be prompted to type in the phrase you used in above step.\n```\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n```\nsee below example screenshot:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-sessionmgr-2.png)\n\nNext we will print out the public and private key string in a correct format that we can use for configuration later.\n```\ngrep -v KEY rsa_key.pub | tr -d '\\n' | awk '{print $1}' \u003E pub.Key\ncat pub.Key\n\ngrep -v KEY rsa_key.p8 | tr -d '\\n' | awk '{print $1}' \u003E priv.Key\ncat priv.Key\n```\nsee below example screenshot:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-sessionmgr-3.png)\n\n\u003C!----------------------------\u003E\n## Prepare the Snowflake account for streaming\n\n#### 1. Creating user, role, and database\nFirst login to your Snowflake account as a power user with ACCOUNTADMIN role. \nThen run the following SQL commands in a worksheet to create a user, database and the role that we will use in the lab.\n\n```\n-- Set default value for multiple variables\n-- For purpose of this workshop, it is recommended to use these defaults during the exercise to avoid errors\n-- You should change them after the workshop\nSET PWD = 'Test1234567';\nSET USER = 'STREAMING_USER';\nSET DB = 'ADF_STREAMING_DB';\nSET WH = 'ADF_STREAMING_WH';\nSET ROLE = 'ADF_STREAMING_RL';\n\nUSE ROLE ACCOUNTADMIN;\n\n-- CREATE USERS\nCREATE USER IF NOT EXISTS IDENTIFIER($USER) PASSWORD=$PWD  COMMENT='STREAMING USER';\n\n-- CREATE ROLES\nCREATE OR REPLACE ROLE IDENTIFIER($ROLE);\n\n-- CREATE DATABASE AND WAREHOUSE\nCREATE DATABASE IF NOT EXISTS IDENTIFIER($DB);\nUSE IDENTIFIER($DB);\nCREATE OR REPLACE WAREHOUSE IDENTIFIER($WH) WITH WAREHOUSE_SIZE = 'SMALL';\n\n-- GRANTS\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE IDENTIFIER($ROLE);\nGRANT ROLE IDENTIFIER($ROLE) TO USER IDENTIFIER($USER);\nGRANT OWNERSHIP ON DATABASE IDENTIFIER($DB) TO ROLE IDENTIFIER($ROLE);\nGRANT USAGE ON WAREHOUSE IDENTIFIER($WH) TO ROLE IDENTIFIER($ROLE);\n\n-- SET DEFAULTS\nALTER USER IDENTIFIER($USER) SET DEFAULT_ROLE=$ROLE;\nALTER USER IDENTIFIER($USER) SET DEFAULT_WAREHOUSE=$WH;\n\n-- RUN FOLLOWING COMMANDS TO FIND YOUR ACCOUNT IDENTIFIER, COPY IT DOWN FOR USE LATER\n-- IT WILL BE SOMETHING LIKE \u003Corganization_name\u003E-\u003Caccount_name\u003E\n-- e.g. ykmxgak-wyb52636\n\nWITH HOSTLIST AS \n(SELECT * FROM TABLE(FLATTEN(INPUT =\u003E PARSE_JSON(SYSTEM$allowlist()))))\nSELECT REPLACE(VALUE:host,'.snowflakecomputing.com','') AS ACCOUNT_IDENTIFIER\nFROM HOSTLIST\nWHERE VALUE:type = 'SNOWFLAKE_DEPLOYMENT_REGIONLESS';\n```\n\nPlease write down the Account Identifier, we will need it later.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/account-identifier.png)\n\nWe need to retrieve the `Snowflake private account URL` for use later, run below SQL command now and record the output, e.g. `https://xyz12345.us-west-2.privatelink.snowflakecomputing.com`\n\n```sql\nwith PL as\n(SELECT * FROM TABLE(FLATTEN(INPUT =\u003E PARSE_JSON(SYSTEM$GET_PRIVATELINK_CONFIG()))) where key = 'privatelink-account-url')\nSELECT concat('https://'|| REPLACE(VALUE,'\"','')) AS SNOWFLAKE_PRIVATE_ACCOUNT_URL\nfrom PL;\n```\n\nNow we need to retrieve the value of `VPCE ID` for use later, run below SQL command now and record the output, e.g. `com.amazonaws.vpce.us-west-2.vpce-svc-xyzabce999777333f1`.\n\n```sql\nwith PL as\n(SELECT * FROM TABLE(FLATTEN(INPUT =\u003E PARSE_JSON(SYSTEM$GET_PRIVATELINK_CONFIG()))) where key = 'privatelink-vpce-id')\nSELECT REPLACE(VALUE,'\"','') AS PRIVATE_LINK_VPCE_ID\nfrom PL;\n```\n\nNext we need to configure the public key for the streaming user to access Snowflake programmatically.\n\nFirst, in the Snowflake worksheet, replace \u003C pubKey \u003E with the content of the file `/home/azureuser/pub.Key` (see `step 4` in `section #2 Provision a Linux jumphost in AWS` located in the left pane) in the following SQL command and execute.\n```commandline\nuse role accountadmin;\nalter user streaming_user set rsa_public_key='\u003C pubKey \u003E';\n```\nSee below example screenshot:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-snowflake.png)\n\nNow logout of Snowflake, sign back in as the default user `streaming_user` we just created with the associated password (default: Test1234567).\nRun the following SQL commands in a worksheet to create a schema (e.g. `ADF_STREAMING_SCHEMA`) in the default database (e.g. `ADF_STREAMING_DB`):\n\n```commandline\nSET DB = 'ADF_STREAMING_DB';\nSET SCHEMA = 'ADF_STREAMING_SCHEMA';\n\nUSE IDENTIFIER($DB);\nCREATE OR REPLACE SCHEMA IDENTIFIER($SCHEMA);\n```\n\n#### 2. Install SnowSQL (optional but highly recommended)\n\n[SnowSQL](https://docs.snowflake.com/en/user-guide/snowsql.html) is the command line client for connecting to Snowflake to execute SQL queries and perform all DDL and DML operations, including loading data into and unloading data out of database tables.\n\nTo install SnowSQL. Execute the following commands on the Linux Session Manager console:\n```commandline\ncurl https://sfc-repo.snowflakecomputing.com/snowsql/bootstrap/1.2/linux_x86_64/snowsql-1.2.24-linux_x86_64.bash -o /tmp/snowsql-1.2.24-linux_x86_64.bash\necho -e \"~/bin \\n y\" \u003E /tmp/ans\nbash /tmp/snowsql-1.2.24-linux_x86_64.bash \u003C /tmp/ans\n\n```\nSee below example screenshot:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/install-snowsql.png)\n\nNext set the environment variable for Snowflake Private Key Phrase:\n```commandline\nexport SNOWSQL_PRIVATE_KEY_PASSPHRASE=\u003Ckey phrase you set up when running openssl previously\u003E\n```\nNote that you should add the command above in the ~/.bashrc file to preserve this environment variable across sessions.\n```commandline\necho \"export SNOWSQL_PRIVATE_KEY_PASSPHRASE=$SNOWSQL_PRIVATE_KEY_PASSPHRASE\" \u003E\u003E ~/.bashrc\n```\n\nNow you can execute this command to interact with Snowflake:\n```commandline\n$HOME/bin/snowsql -a \u003CThe Account Identifier that you recorded earlier\u003E -u streaming_user --private-key-path $HOME/rsa_key.p8 -d adf_streaming_db -s adf_streaming_schema\n```\nSee below example screenshot:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/key-pair-snowsql.png)\n\nType `Ctrl-D` to get out of SnowSQL session.\n\nYou can edit the [`~/.snowsql/config`](https://docs.snowflake.com/en/user-guide/snowsql-config.html#snowsql-config-file) file to set default parameters and eliminate the need to specify them every time you run snowsql.\n\nAt this point, the Snowflake setup is complete.\n\n\u003C!----------------------------\u003E\n## Create an ADF delivery stream\n\nIn this step, we are going to create an ADF delivery stream for data streaming.\n\nNavigate to the [ADF console](https://console.aws.amazon.com/firehose/home?streams) and click `Create delivery stream`.\n\nIn the `Source` section, select `Direct PUT` from the drop-down menu. Optionally, you can pick `Kinesis Data Streams` as the source, but we will focus on `Direct Put` in this workshop.\n\nIn the `Destination` section, select `Snowflake` from the drop-down menu.\n\nType in a name for the `Firehose stream name`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-stream-1.png)\n\nSkip `Transform records` setup.\n\nFor `Snowflake account URL`, enter the URL you recorded previously from step 1 in Chapter 3, e.g. `https://xyz12345.us-west-2.privatelink.snowflakecomputing.com`.\n\nNote here we are going to use Amazon PrivateLink (Note, [Snowflake Business Critical Edition](https://docs.snowflake.com/en/user-guide/intro-editions#security-governance-and-data-protection) or above is required) to secure the communication between Snowflake and ADF, so the URL is a private endpoint with `privatelink` as a substring. \n\nAlternatively, you can use the public endpoint without the `privatelink` substring, e.g. `https://xyz12345.us-west-2.snowflakecomputing.com`, if this is the case, also leave the `VPCE ID` field blank below.\n\nFor `User`, type in `STREAMING_USER`.\n\nFor `Private key`, go back to your EC2 console in Systems Manager and run\n```commandline\ncat ~/priv.key\n```\nCopy the output string and paste into the `Private key` field.\n\nFor `Passphrase`, type in the phrase you used when generating the public key with openssl earlier.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-stream-2.png)\n\nFor `Role`, select `Use custom Snowflake role` and type in `ADF_STREAMING_RL`.\n\nFor `VPCE ID`, enter the value you recorded from step 1 in Chapter 3, e.g. `com.amazonaws.vpce.us-west-2.vpce-svc-xyzabce999777333f1`.\n\nFor `Snowflake database`, type in `ADF_STREAMING_DB`.\n\nFor `Snowflake Schema`, type in `ADF_STREAMING_SCHEMA`.\n\nFor `Snowflake table`, type in `ADF_STREAMING_TBL`.\n\nFor `Data loading options for your Snowflake table`, select `Use JSON keys as table column names`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-stream-3.png)\n\nFor `S3 backup bucket`, pick an existing S3 bucket where you want to save the logs or error messages. [Create a S3 bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html) if you don't have one.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-stream-4.png)\n\nLeave everything else as default and click `Create delivery stream`.\n\nYour delivery stream will be generated in about 5 minutes.\n\n\u003C!----------------------------\u003E\n## Ingest and Query data in Snowflake\n\nNow, switch back to the Snowflake console and make sure that you signed in as the default user `streaming_user`. \nThe data should have been streamed into a table, ready for further processing.\n\n#### 1. Create a destination table in Snowflake\n\nRun the following SQL command to create the table `ADF_STREAMING_TBL` we specified when provisioning\nthe delivery stream. Note that here we use varchar type for most of the columns, we will\ngenerate a view later to transform them into the correct types.\n\n```commandline\nuse ADF_STREAMING_DB;\nuse schema ADF_STREAMING_SCHEMA;\ncreate or replace TABLE ADF_STREAMING_TBL (\n\tORIG VARCHAR(20),\n\tUTC NUMBER(38,0),\n\tALT VARCHAR(20),\n\tICAO VARCHAR(20),\n\tLON VARCHAR(20),\n\tID VARCHAR(20),\n\tDEST VARCHAR(20),\n\tLAT VARCHAR(20)\n);\n```\n#### 2. Ingest real-time data \n\nGo to the EC2 console, and run the following command.\n```commandline\npython3 /tmp/adf-producer.py \u003CADF delivery stream name\u003E\n```\nThe Python script gets the raw flight data from a [real-time source](http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky) and streams into the delivery stream.\nYou should see the flight data being ingested continuously to the ADF delivery stream in json format.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kdf-producer-ingest.png)\n\n#### 3. Query the raw data in Snowflake\nTo verify that data has been streamed into Snowflake, execute the following SQL commands.\n\nNow run the following query on the table.\n```\nselect * from adf_streaming_tbl;\n```\n\nHere is the screen capture of the sample output.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/raw_data.png)\n\n\n#### 2. Convert the raw data table into a view with correct data types\nNow execute the following SQL command.\n\n```commandline\ncreate or replace view flights_vw\n  as select\n    utc::timestamp_ntz ts_utc,\n    CONVERT_TIMEZONE('UTC','America/Los_Angeles',ts_utc::timestamp_ntz) as ts_pt,\n    alt::integer alt,\n    dest::string dest,\n    orig::string orig,\n    id::string id,\n    icao::string icao,\n    lat::float lat,\n    lon::float lon,\n    st_geohash(to_geography(st_makepoint(lon, lat)),12) geohash,\n    st_distance(st_makepoint(-122.366340, 37.616245), st_makepoint(lon, lat))/1609::float dist_to_sfo,\n    year(ts_pt) yr,\n    month(ts_pt) mo,\n    day(ts_pt) dd,\n    hour(ts_pt) hr\nFROM adf_streaming_tbl;\n```\n\nThe SQL command creates a view, converts timestamps to different time zones, and use Snowflake's [Geohash function](https://docs.snowflake.com/en/sql-reference/functions/st_geohash.html) to generate geohashes that can be used in time-series visualization tools such as Grafana.\nYou can also easily calculate the distance in miles between two geo locations. In above example, the `st_distance` function is used to calculate the distance between an airplane and San Francisco Airport.\n\nLet's query the view `flights_vw` now.\n```sql\nselect * from flights_vw;\n```\n\nAs a result, you will see a nicely structured output with columns derived from the JSONs at the [source](http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky).\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/flight_view.png)\n\n\u003C!----------------------------\u003E\n## Use Copilot to interact with your data in natural language\n\n[Snowflake Copilot](https://docs.snowflake.com/en/user-guide/snowflake-copilot) is an LLM-powered assistant that simplifies data analysis while maintaining robust data governance, and seamlessly integrates into your existing Snowflake workflow. You can gain insights from your data stored in Snowflake by using natural language. \n\nTo get started, on the console, click 'Ask Copilot' button located at the bottom. \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/ask-copilot.png)\n\nIn the conversation window, type in a question like the following:\n```\nshow me the unique flights from KLAX arriving at KSFO between 2000 to 4000 feet altitude?\n```\nCopilot will then analyze the question and show you its thinking process step-by-step and give you the SQL command to get the answer.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/copilot1.gif)\n\nLet's ask Copilot another question that uses Snowflake's geo-spatial functions to calculate the distance between two locations.\n\nAsk a question like the following.\n```\nif the lattitude and longitude for KSFO is 37.619254,-122.4491623 respectively, calculate the distance (in miles) between the flights and KSFO with Snowflake's geo-spatial functions, also show the related flight ids, original and destination airports\n```\n\nAgain, Copilot gives the SQL command which you can execute to get the distance between all flights to San Francisco internation airport.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/copilot2.gif)\n\n\n\u003C!----------------------------\u003E\n## Use Amazon Managed Service for Flink for real-time analytics - Optional\n\nNote that you will need to complete the base workshop first without cleaning up the resources in order to proceed.\n\nApache Flink is a powerful stream processing framework with a wide range of use cases across industries. Some common applications include Real-time analytics, Fraud detection, Sendor data process, Supply chain optimization, and many more.\n\nHere we will show you how to integrate our current demo with [Amazon Managed Service for Apache Flink](https://aws.amazon.com/managed-service-apache-flink/) and [Kinesis Data Streams (KDS)](https://aws.amazon.com/kinesis/data-streams/) to do real-time analytics.\n\nThe schematic diagram below illustrates the flow of data from the source, which is streamed into an input Kinesis stream. The data is then processed by [Flink Studio notebook](https://docs.aws.amazon.com/managed-flink/latest/java/how-sinks.html#sinks-firehose-create) in real-time, before being ingested into an output Kinesis stream, it is picked up by Data Firehose and ultimately lands in Snowflake. [AWS Glue Data Catalog](https://docs.aws.amazon.com/prescriptive-guidance/latest/serverless-etl-aws-glue/aws-glue-data-catalog.html) serves as a metadata store for Flink Studio notebook tables.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/flink-schematic.png)\n\nWe will be focusing on using Flink Studio notebook for this demo. Optionally, if you are writing Flink applications instead of using the Flink Studio notebook, you can use the [Firehose Producer](https://docs.aws.amazon.com/managed-flink/latest/java/how-sinks.html#sinks-firehose-create) to bypass the output Kinesis stream.\n\n#### 1. Create a table in Snowflake to receive Flink-filtered data\nLog into the Snowflake account as `streaming_user`. Run the following SQL commands to generate a table for capturing the filtered streams.\n\n```sql\nuse ADF_STREAMING_DB;\nuse schema ADF_STREAMING_SCHEMA;\ncreate or replace TABLE ADF_FLINK_TBL (\n\tORIG VARCHAR(5),\n\tUTC VARCHAR(20),\n\tALT INTEGER,\n\tICAO VARCHAR(20),\n\tLON FLOAT,\n\tID VARCHAR(10),\n\tDEST VARCHAR(5),\n\tLAT FLOAT\n);\n```\n\n#### 2. Deploy Flink Studio notebook and Kinesis Data Streams\nTo make the process of deploying necessary resources easier, click [here](https://console.aws.amazon.com/cloudformation/home#/stacks/new?stackName=amf-snowflake&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_ADF/flink-kds-cfn.json) to deploy necessary resources including a [Flink Studio notebook](https://docs.aws.amazon.com/managed-flink/latest/java/how-notebook.html), a Glue database to store metadata of the tables in Flink and two Kinesis Data Streams (KDS). One Kinesis stream serves as the input stream to Flink and the other one serves as the output stream.\n\nPlease enter appropriate values into the empty fields where you entered(i.e. bucket, private key, keyphrase, etc.) in previous modules when prompted during Cloudformation deployment. In about 5 minutes, the template should be deployed successfully.\n\nThe Cloudformant template will be deployed in about 5 minutes, navigate to the \n\n#### 3. Configure Flink Studio notebook\nNavigate to the [Studio notebook console](https://console.aws.amazon.com/flink/home#/list/notebooks), you should see that your notebook status is ready. Check the notebook and click `Run` button at the top. The notebook status should change to `Running` in about 5 minutes. \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/run-flink-nb.png)\n\nNavigate to the CloudFormation stack that we successfully deployed (e.g.`amf_snowflake`) and click on the `Outputs` tab to record the values for `KinesisDataInputStream` and `KinesisDataOutputStream`, we will need them later.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/cfn-output.png)\n\nYou will also notice notice that a Glue database is also created. Navigate to the [Glue console](https://us-west-2.console.aws.amazon.com/glue/home#/v2/data-catalog/databases) to verify.\n\n\n#### 4. Configure Zeppelin notebook\nClick `Open Apache Zeppelin` when the notebook is running.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/zeppelin.png)\n\nDownload a Zeppelin note from [here](https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_ADF/Flink-nb.zpln), and save it to your desktop.\n\nIn Zeppelin, click `import note`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/zeppelin-import-note-1.png)\n\nGive the note a name, i.e. `myNote`. Select `Select JSON File/IPYNB File` when prompted.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/zeppelin-import-note-2.png)\n\nPick the `Flink-nb.zpln` file you just dowonloaded. You should now see the note name in the browser window, click the note to open.\n\n#### 5. Run Flink notebook\nIn the left cell, type in the value for `KinesisDataInputStream` you recorded from step 3 above, change `aws.region` to your local region. Highlight the content of the left cell, then click the triangle play button at the top. You should see the status changing from `Pending` to `Running` to `Finished`. This creates a Flink input table which maps to the input Kinesis data stream.\n\nDo the same thing for the right cell. A Flink output table is also created.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/flink-nb-run-2.png)\n\nNow, scroll down to the remaining two cells to start filtering and monitoring by clicking the play button located at the top-right corner of each cell.\n\nFirst cell is to filter out any live United Airlines flight tracks below 7000 feet and the other cell is to monitor the filtered results.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/flink-nb-run-3.png)\n\nLeave your Flink notebook window open.\n\n#### 6. Start ingesting live data to the input Kinesis data stream\nWe are ready to ingest data now. Go to your EC2 console via [Session Manager](https://console.aws.amazon.com/systems-manager/session-manager) as instructed in step 3 of Chapter 2.\n\nKick off the ingestion by executing below shell command, replace `\u003Cyour input Kinesis stream\u003E` with the name of your input Kinesis stream.\n\n```shell\ncurl -s https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_ADF/kds-producer.py | python3 - \u003Cyour input Kinesis stream name\u003E\n```\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/kds-ingestion.gif)\n\n In a short moment, you will see some filtered data appearing in the Flink notebook monitoring cell and they are all UA airplanes flying below 7000 feet.\n\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/real-time-analytics.gif)\n\n\n#### 7. Verify the results in Snowflake\nNow go back to your Snowflake account as user 'streaming_user', and run the following SQL commands:\n\n```sql\nUSE ADF_STREAMING_DB;\nUSE SCHEMA ADF_STREAMING_SCHEMA;\nSELECT * from ADF_FLINK_TBL;\n```\nYou should see the filtered flight tracks are captured in table `ADF_FLINK_TBL`.\n\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/verify-snowflake-result.png)\n\n\u003C!----------------------------\u003E\n## Cleanup\n\nWhen you are done with the demo, to tear down the AWS resources, simply go to the [Cloudformation](https://console.aws.amazon.com/cloudformation/home?stacks) console.\nSelect the Cloudformation template you used to deploy the jumphost at the start of the demo, also the template for Amazon Managed Flink if you optionally deployed it, then click the `Delete` tab.\n\nSee example screen capture below.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/cleanup.png)\n\nNavigate to the [EC2 console](https://console.aws.amazon.com/ec2/home#Instances:instanceState=running) and delete the jumphost.\n\nYou will also need to delete the Firehose delivery stream. Navigate to the [ADF Console](https://console.aws.amazon.com/firehose/home?streams), select the delivery stream you created, and select `Delete` button at the top.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-and-aws-kdf/delete-adf.png)\n\n\u003C!----------------------------\u003E\n## Conclusion\n\nIn this lab, we built a demo to show how to ingest real-time data using Amazon Data Firehose with low latency. We demonstrated this using an ADF\nconnector on an EC2 instance. Alternatively, if you have infrastructure supported by either [Amazon EKS](https://aws.amazon.com/eks/) or\n[Amazon ECS](https://aws.amazon.com/ecs/), you can use them to host your containerized ADF producers as well.\n\nFor those of you who are interested in learning more about how to build sleek dashboards for monitoring the live flight data, please navigate to this\n[quickstart](https://quickstarts.snowflake.com/guide/404.html/) to continue.\n\nRelated Resources\n\n- [Uplevel your data architecture with real- time streaming using Amazon Data Firehose and Snowflake](https://aws.amazon.com/blogs/big-data/uplevel-your-data-architecture-with-real-time-streaming-using-amazon-data-firehose-and-snowflake/)\n- [Unleashing the Full Potential of Real-Time Streaming with Amazon Kinesis Data Firehose and Snowpipe Streaming](https://medium.com/snowflake/unleashing-the-full-potential-of-real-time-streaming-with-amazon-kinesis-data-firehose-and-snowpipe-0283fb599364#Snowflake)\n- [Amazon Data Firehose (ADF)](https://aws.amazon.com/firehose/)\n- [Amazon Managed Service for Apache Flink](https://aws.amazon.com/managed-service-apache-flink/)\n- [Snowpipe Streaming Demystified](https://medium.com/snowflake/snowpipe-streaming-demystified-e1ee385c6d9c)\n- [Getting Started with Amazon Managed Service for Grafana and Streamlit On Real-time Dashboarding](https://quickstarts.snowflake.com/guide/404.html/)\n- [Getting started with Snowflake](/en/developers/guides/)\n- [Snowflake on AWS Marketplace](https://aws.amazon.com/marketplace/seller-profile?id=18d60ae8-2c99-4881-a31a-e74770d70347)\n- [Snowflake for Data Sharing](/Workloads/data-sharing/)\n- [Snowflake Marketplace](/en/data-cloud/marketplace/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-1ae0662080","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-dba486fd58",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0c61aec6d1","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-dba7a575f7","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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