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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-9d9b0e2443","isDeveloperGuidesPage":false,"quickstartHeroBreadcrumbs":[{"title":"Getting Started with Snowpipe Streaming and Amazon MSK","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/vhol-snowpipe-streaming-msk","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}],":type":"snowflake-site/components/quickstart/quickstart-hero","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/vhol-snowpipe-streaming-msk","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Transformation","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/transformation","tagIcon":""},"quickstartHeroAuthor":"James Sun","quickstartHeroTitle":{"lines":["Getting Started with Snowpipe Streaming and Amazon MSK"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroForkRepoLink":{"id":"button-d1f385c39e","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/vhol-snowpipe-streaming-msk"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"}},"flexible_column_cont":{"id":"flexible-column-container-c8f92c2d71","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-0318391bdb",":items":{"contentfragment":{"id":"contentfragment-34693fce4c","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 Snowpipe streaming:\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 using\nSnowflake's Snowpipe streaming and \u003Ca href=\"https://aws.amazon.com/msk/\"\u003EMSK (Amazon Managed Streaming for Apache Kafka)\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe architecture diagram below shows the deployment. An MSK cluster and a Linux\nEC2 instance (jumphost) will be provisioned in private subnets of an AWS VPC.\nThe Linux jumphost will host the Kafka producer and Snowpipe streaming via \u003Ca href=\"https://docs.snowflake.com/en/user-guide/kafka-connector-overview.html\"\u003EKafka Connect\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe Kafka producer calls the data sources' REST API and receives time-series data in JSON format. This data is then ingested into the Kafka cluster before being picked up by the Kafka connector 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/vhol-snowpipe-streaming-msk/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/vhol-snowpipe-streaming-msk/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, MSK, 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_cta=quickstarts_\"\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. Please refer\nto this \u003Ca href=\"https://docs.aws.amazon.com/whitepapers/latest/amazon-msk-migration-guide/amazon-managed-streaming-for-apache-kafka-amazon-msk.html\"\u003EAWS document\u003C/a\u003E for the MSK networking topology)\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, preferrably 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\u003ENow if 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.\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/msk/\"\u003EMSK (Amazon Managed Streaming for Apache Kafka)\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\u003E\u003Ca href=\"https://docs.aws.amazon.com/msk/latest/developerguide/msk-create-cluster.html\"\u003ECreate a provisioned Kafka cluster\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ECreate Kafka producers and connectors\u003C/li\u003E\u003Cli\u003ECreate topics in a Kafka cluster\u003C/li\u003E\u003Cli\u003EA Snowflake database for hosting real-time flight data\u003C/li\u003E\u003C/ul\u003E\n<!---------------------------->\n","\u003Ch2\u003ECreate a provisioned Kafka cluster and a Linux jumphost in AWS\u003C/h2\u003E\n","\u003Ch4\u003E1. Create an MSK cluster and an EC2 instance\u003C/h4\u003E\n","\u003Cp\u003EThe MSK cluster is created in a VPC managed by Amazon. We will deploy our Kafka clients in our own VPC and use security groups to ensure\nthe communications between the MSK cluster and clients are secure.\u003C/p\u003E\n","\u003Cp\u003EFirst, click \u003Ca href=\"https://console.aws.amazon.com/cloudformation/home?region=us-west-2#/stacks/new?stackName=MSK-Snowflake&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_Snowpipe_Streaming_MSK/msk-CFT-for-SE-Sandbox.json\"\u003Ehere\u003C/a\u003E\nto launch a provisioned MSK cluster. Note the default AWS region is \u003Ccode\u003Eus-west-2 (Oregon)\u003C/code\u003E, feel free to select a region you prefer to deploy the environment.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ENext\u003C/code\u003E at the \u003Ccode\u003ECreate stack\u003C/code\u003E page.\nSet the Stack name or modify the default value to customize it to your identity. Leave the default Kafka version as is. For \u003Ccode\u003EMSKSecurityGroupId\u003C/code\u003E, we recommend\nto use the \u003Ca href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/default-custom-security-groups.html\"\u003Edefault security group\u003C/a\u003E in your VPC. In the drop-down menu, pick two subnets, they can be either public or private subnets depending on the network layout of your VPC. Leave \u003Ccode\u003ETLSMutualAuthentication\u003C/code\u003E as false and the jumphost instance type and AMI id as default before clicking\n\u003Ccode\u003ENext\u003C/code\u003E.\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/vhol-snowpipe-streaming-msk/msk-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 10-30 minutes depending on your AWS region, the Cloudformation template provisions an MSK cluster with two brokers. It will also\nprovision a Linux EC2 instance in the subnet you selected. We will then use it to run the Kafka connector with Snowpipe streaming SDK and the producer.\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 interact with the MSK cluster.\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 MSK cluster,\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/vhol-snowpipe-streaming-msk/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/vhol-snowpipe-streaming-msk/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/vhol-snowpipe-streaming-msk/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/vhol-snowpipe-streaming-msk/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/vhol-snowpipe-streaming-msk/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/vhol-snowpipe-streaming-msk/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.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/session-mgr-3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E4. Create a key-pair to be used for authenticating with Snowflake programmatically\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/vhol-snowpipe-streaming-msk/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/vhol-snowpipe-streaming-msk/key-pair-sessionmgr-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext we will print out the public key string in a correct format that we can use for Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Egrep -v KEY rsa_key.pub | tr -d 'n' > pub.Key\ncat pub.Key\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/vhol-snowpipe-streaming-msk/key-pair-sessionmgr-3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E5. Install the Kafka connector for Snowpipe streaming\u003C/h4\u003E\n","\u003Cp\u003ERun the following command to install the Kafka connector and Snowpipe streaming SDK\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Epasswd=changeit  # Use the default password for the Java keystore, you should chang it after finishing the lab\ndirectory=/home/azureuser/snowpipe-streaming # Installation directory\n\ncd $HOME\nmkdir -p $directory\ncd $directory\npwd=`pwd`\nsudo yum clean all\nsudo yum -y install openssl vim-common java-1.8.0-openjdk-devel.x86_64 gzip tar jq python3-pip\nwget https://archive.apache.org/dist/kafka/2.8.1/kafka_2.12-2.8.1.tgz\ntar xvfz kafka_2.12-2.8.1.tgz -C $pwd\nwget https://github.com/aws/aws-msk-iam-auth/releases/download/v1.1.1/aws-msk-iam-auth-1.1.1-all.jar -O $pwd/kafka_2.12-2.8.1/libs/aws-msk-iam-auth-1.1.1-all.jar\nrm -rf $pwd/kafka_2.12-2.8.1.tgz\ncd /tmp && cp /usr/lib/jvm/java-openjdk/jre/lib/security/cacerts kafka.client.truststore.jks\ncd /tmp && keytool -genkey -keystore kafka.client.keystore.jks -validity 300 -storepass $passwd -keypass $passwd -dname "CN=snowflake.com" -alias snowflake -storetype pkcs12\n\n#Snowflake kafka connector\nwget https://repo1.maven.org/maven2/com/snowflake/snowflake-kafka-connector/1.8.2/snowflake-kafka-connector-1.8.2.jar -O $pwd/kafka_2.12-2.8.1/libs/snowflake-kafka-connector-1.8.2.jar\n\n#Snowpipe streaming SDK\nwget https://repo1.maven.org/maven2/net/snowflake/snowflake-ingest-sdk/1.0.3-beta/snowflake-ingest-sdk-1.0.3-beta.jar   -O $pwd/kafka_2.12-2.8.1/libs/snowflake-ingest-sdk-1.0.3-beta.jar  \nwget https://repo1.maven.org/maven2/net/snowflake/snowflake-jdbc/3.13.15/snowflake-jdbc-3.13.15.jar -O $pwd/kafka_2.12-2.8.1/libs/snowflake-jdbc-3.13.15.jar\nwget https://repo1.maven.org/maven2/org/bouncycastle/bc-fips/1.0.1/bc-fips-1.0.1.jar -O $pwd/kafka_2.12-2.8.1/libs/bc-fips-1.0.1.jar\nwget https://repo1.maven.org/maven2/org/bouncycastle/bcpkix-fips/1.0.3/bcpkix-fips-1.0.3.jar -O $pwd/kafka_2.12-2.8.1/libs/bcpkix-fips-1.0.3.jar\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E6. Retrieve the broker string from the MSK cluster.\u003C/h4\u003E\n","\u003Cp\u003EGo to the \u003Ca href=\"https://us-west-2.console.aws.amazon.com/msk/#/clusters\"\u003EMSK\u003C/a\u003E console and click on the newly created MSK cluster, it should have a substring \u003Ccode\u003EMSKCluster\u003C/code\u003E in its name.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/bs-1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Ccode\u003EView client information\u003C/code\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/bs-2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe are going to use TLS authentication between the client and brokers. Copy down the broker string under \u003Ccode\u003EPrivate endpoint\u003C/code\u003E for TLS authentication type.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/bs-3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow switch back to the Session Manager window and execute the following command by replacing \u003Ccode\u003E<broker string>\u003C/code\u003E with\nthe copied values.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Eexport BS=<broker string>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow run the following command to add \u003Ccode\u003EBS\u003C/code\u003E as an environment variable so it is recognized across the Linux sessions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eecho "export BS=$BS" >> ~/.bashrc\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESee the following example screen capture.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/bs-4.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E7. Create a configuration file \u003Ccode\u003Econnect-standalone.properties\u003C/code\u003E for the Kafka connector\u003C/h4\u003E\n","\u003Cp\u003ERun the following commands to generate a configuration file \u003Ccode\u003Econnect-standalone.properties\u003C/code\u003E in directory \u003Ccode\u003E/home/azureuser/snowpipe-streaming/scripts\u003C/code\u003E for the client to authenticate\nwith the Kafka cluster.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Edir=/home/azureuser/snowpipe-streaming/scripts\nmkdir -p $dir && cd $dir\ncat << EOF > $dir/connect-standalone.properties\n#************CREATING SNOWFLAKE Connector****************\nbootstrap.servers=$BS\n\n#************SNOWFLAKE VALUE CONVERSION****************\nkey.converter=org.apache.kafka.connect.storage.StringConverter\nvalue.converter=com.snowflake.kafka.connector.records.SnowflakeJsonConverter\nkey.converter.schemas.enable=true\nvalue.converter.schemas.enable=true\n#************SNOWFLAKE ****************\n\noffset.storage.file.filename=/tmp/connect.offsets\n# Flush much faster than normal, which is useful for testing/debugging\noffset.flush.interval.ms=10000\n\n#*********** FOR SSL  ****************\nsecurity.protocol=SSL\nssl.truststore.location=/tmp/kafka.client.truststore.jks\nssl.truststore.password=changeit\nssl.enabled.protocols=TLSv1.1,TLSv1.2\n\nconsumer.security.protocol=SSL\nconsumer.ssl.truststore.location=/tmp/kafka.client.truststore.jks\nconsumer.ssl.truststore.password=changeit\nconsumer.ssl.enabled.protocols=TLSv1.1,TLSv1.2\nEOF\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA configuration file \u003Ccode\u003Econnect-standalone.properties\u003C/code\u003E is created in directory \u003Ccode\u003E/home/azureuser/snowpipe-streaming/scripts\u003C/code\u003E\u003C/p\u003E\n","\u003Ch4\u003E8. Create a security client.properties configuration file for the producer\u003C/h4\u003E\n","\u003Cp\u003ERun the following commands to create a security configuration file \u003Ccode\u003Eclient.properties\u003C/code\u003E for the MSK cluster\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Edir=/home/azureuser/snowpipe-streaming/scripts\ncat << EOF > $dir/client.properties\nsecurity.protocol=SSL\nssl.truststore.location=/tmp/kafka.client.truststore.jks\nssl.truststore.password=changeit\nssl.enabled.protocols=TLSv1.1,TLSv1.2\nEOF\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA configuration file \u003Ccode\u003Eclient.properties\u003C/code\u003E is created in directory \u003Ccode\u003E/home/azureuser/snowpipe-streaming/scripts\u003C/code\u003E\u003C/p\u003E\n","\u003Ch4\u003E9. Create a streaming topic called “streaming” in the MSK cluster\u003C/h4\u003E\n","\u003Cp\u003ENow we can run the following commands to create a Kafka topic on the MSK cluster to stream our data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003E$HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --create --topic streaming --partitions 2 --replication-factor 2\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the response \u003Ccode\u003ECreated topic streaming\u003C/code\u003E if it is successful.\u003C/p\u003E\n","\u003Cp\u003ETo describe the topic, run the following commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003E$HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --describe --topic streaming\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see there are two partitions with a replication factor of 2 in the \u003Ccode\u003Estreaming\u003C/code\u003E topic.\nSee below example screenshot:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/list-kafka-topic.png\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EConfigure Snowflake to communicate with the MSK cluster\u003C/h2\u003E\n","\u003Ch4\u003E1. Creating user, role, and database\u003C/h4\u003E\n","\u003Cp\u003EFirst login to your Snowflake account as a power user with ACCOUNTADMIN role.\nThen run the following SQL commands in a worksheet to create a user, database and the role that we will use in the lab.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E-- Set default value for multiple variables\n-- For purpose of this workshop, it is recommended to use these defaults during the exercise to avoid errors\n-- You should change them after the workshop\nSET PWD = 'Test1234567';\nSET USER = 'STREAMING_USER';\nSET DB = 'MSK_STREAMING_DB';\nSET WH = 'MSK_STREAMING_WH';\nSET ROLE = 'MSK_STREAMING_RL';\n\nUSE ROLE ACCOUNTADMIN;\n\n-- CREATE USERS\nCREATE USER IF NOT EXISTS IDENTIFIER($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\u003C/code\u003E\u003C/pre\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:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003ESET DB = 'MSK_STREAMING_DB';\nSET SCHEMA = 'MSK_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\u003EFirst, in the Snowflake worksheet, replace \u003Ccode\u003E<pubKey>\u003C/code\u003E with the content of the file \u003Ccode\u003E/home/azureuser/pub.Key\u003C/code\u003E (see step 3 in previous section) 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/vhol-snowpipe-streaming-msk/key-pair-snowflake.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow we can 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/vhol-snowpipe-streaming-msk/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 <Snowflake Account Name> --region <AWS region where Snowflake is located> -u streaming_user --private-key-path $HOME/rsa_key.p8 -d msk_streaming_db -s msk_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/vhol-snowpipe-streaming-msk/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\u003EConfigure Kafka connector to Snowflake with Snowpipe streaming SDK\u003C/h2\u003E\n","\u003Ch4\u003E1. Run the following commands to collect various connection parameters for the Kafka connector\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecd $HOME\noutf=/tmp/params\ncat << EOF > /tmp/get_params\na=''\nuntil [ ! -z $a ]\ndo\n read -p "Input Snowflake account URL: e.g. gwa123456 ==> " a\ndone\n\nr=''\nuntil  [ ! -z $r ]\ndo\n  read -p "Input Snowflake account region: e.g. us-west-2 ==> " r\ndone\n\nif [[ $r == "us-west-2" ]]\nthen\n  echo export clstr_url=$a.snowflakecomputing.com > $outf\n  export clstr_url=$a.snowflakecomputing.com\nelse\n  echo export clstr_url=$a.$r.snowflakecomputing.com > $outf\n  export clstr_url=$a.$r.snowflakecomputing.com \nfi\n\nread -p "Snowflake cluster user name: default: streaming_user ==> " user\nif [[ $user == "" ]]\nthen\n   user="streaming_user"\nfi\n\necho export user=$user >> $outf\nexport user=$user\n\npass=''\nuntil [ ! -z $pass ]\ndo\n  read -p "Private key passphrase ==> " pass\ndone\n\necho export key_pass=$pass >> $outf\nexport key_pass=$pass\n\nread -p "Full path to your Snowflake private key file, default: /home/azureuser/rsa_key.p8 ==> " p8\nif [[ $p8 == "" ]]\nthen\n   p8="/home/azureuser/rsa_key.p8"\nfi\n\npriv_key=`cat $p8 | grep -v PRIVATE | tr -d 'n'`\necho export priv_key=$priv_key  >> $outf\nexport priv_key=$priv_key\ncat $outf >> $HOME/.bashrc\nEOF\n. /tmp/get_params\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESee below example screen capture.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/get_params.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E2. Run the following commands to create a Snowflake Kafka connect property configuration file:\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Edir=/home/azureuser/snowpipe-streaming/scripts\ncat << EOF > $dir/snowflakeconnectorMSK.properties\nname=snowpipeStreaming\nconnector.class=com.snowflake.kafka.connector.SnowflakeSinkConnector\ntasks.max=4\ntopics=streaming\nsnowflake.private.key.passphrase=$key_pass\nsnowflake.database.name=MSK_STREAMING_DB\nsnowflake.schema.name=MSK_STREAMING_SCHEMA\nsnowflake.topic2table.map=streaming:MSK_STREAMING_TBL\nbuffer.count.records=10000\nbuffer.flush.time=5\nbuffer.size.bytes=20000000\nsnowflake.url.name=$clstr_url\nsnowflake.user.name=$user\nsnowflake.private.key=$priv_key\nsnowflake.role.name=MSK_STREAMING_RL\nsnowflake.ingestion.method=snowpipe_streaming\nvalue.converter.schemas.enable=false\njmx=true\nkey.converter=org.apache.kafka.connect.storage.StringConverter\nvalue.converter=org.apache.kafka.connect.json.JsonConverter\nerrors.tolerance=all\nEOF\n\u003C/code\u003E\u003C/pre\u003E\n<!---------------------------->\n","\u003Ch2\u003EPutting it all together\u003C/h2\u003E\n","\u003Cp\u003EFinally, we are ready to start ingesting data into the Snowflake table.\u003C/p\u003E\n","\u003Ch4\u003E1. Start the Kafka Connector for Snowpipe streaming\u003C/h4\u003E\n","\u003Cp\u003EGo back to the Linux console and execute the following commands to start the Kafka connector.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003E$HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/connect-standalone.sh $HOME/snowpipe-streaming/scripts/connect-standalone.properties $HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf everything goes well, you should something similar to screen capture below:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/snowpipe-streaming-kc.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E2. Start the producer that will ingest real-time data to the MSK cluster\u003C/h4\u003E\n","\u003Cp\u003EStart a new Linux session by following \u003Ccode\u003Estep 3\u003C/code\u003E in the section named \u003Ccode\u003ECreate a provisioned Kafka cluster and a Linux jumphost in AWS\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecurl --connect-timeout 5 -k https://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | $HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see response similar to screen capture below if everything works well.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/producer.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENote that in the script above, the producer queries a \u003Ca href=\"http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky\"\u003ERest API\u003C/a\u003E that provides real-time flight data over the San Francisco\nBay Area in JSON format. The data includes information such as timestamps, \u003Ca href=\"https://icao.usmission.gov/mission/icao/#:~:text=Home%20%7C%20About%20the%20Mission%20%7C%20U.S.,civil%20aviation%20around%20the%20world.\"\u003Eicao\u003C/a\u003E numbers, flight IDs, destination airport, longitude,\nlatitude, and altitude of the aircraft, etc. The data is ingested into the \u003Ccode\u003Estreaming\u003C/code\u003E topic on the MSK cluster and\nthen picked up by the Snowpipe streaming Kafka connector, which delivers it directly into a Snowflake\ntable \u003Ccode\u003Emsk_streaming_db.msk_streaming_schema.msk_streaming_tbl\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/flight-json.png\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EQuery ingested 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. Query the raw data\u003C/h4\u003E\n","\u003Cp\u003ETo verify that data has been streamed into Snowflake, execute the following SQL commands.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Euse msk_streaming_db;\nuse schema msk_streaming_schema;\nshow channels in table msk_streaming_tbl;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see that there are two channels, corresponding to the two partitions created earlier in the topic.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/channels.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow run the following query on the table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from msk_streaming_tbl;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see there are two columns in the table: \u003Ccode\u003ERECORD_METADATA\u003C/code\u003E and \u003Ccode\u003ERECORD_CONTENT\u003C/code\u003E as shown in the screen capture below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/raw_data.png\" alt=\"\"\u003E\nThe \u003Ccode\u003ERECORD_CONTENT\u003C/code\u003E column is an JSON array that needs to be flattened.\u003C/p\u003E\n","\u003Ch4\u003E2. Flatten the raw JSON data\u003C/h4\u003E\n","\u003Cp\u003ENow execute the following SQL commands to flatten the raw JSONs and create a materialized view with multiple columns based on the key names.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecreate or replace materialized view flights_vw\n  as select\n    f.value:utc::timestamp_ntz ts_utc,\n    CONVERT_TIMEZONE('UTC','America/Los_Angeles',ts_utc::timestamp_ntz) as ts_pt,\n    f.value:alt::integer alt,\n    f.value:dest::string dest,\n    f.value:orig::string orig,\n    f.value:id::string id,\n    f.value:icao::string icao,\n    f.value:lat::float lat,\n    f.value:lon::float lon,\n    st_geohash(to_geography(ST_MAKEPOINT(lon, lat)),12) geohash,\n    year(ts_pt) yr,\n    month(ts_pt) mo,\n    day(ts_pt) dd,\n    hour(ts_pt) hr\nFROM   msk_streaming_tbl,\n       Table(Flatten(msk_streaming_tbl.record_content)) f;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe SQL commands create a view, convert 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 like Grafana\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-commandline\"\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\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/materialized_view.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E3. Stream real-time flight data continuously to Snowflake\u003C/h4\u003E\n","\u003Cp\u003EWe can now write a loop to stream the flight data continuously into Snowflake.\u003C/p\u003E\n","\u003Cp\u003ENote that there is a 30 seconds sleep time between the queries, it is because for our data source in this workshop, anything\nless than 30 seconds will not incur any new data as the source won't update more frequently than 30 seconds.\nFeel free to lower the frequency for other data sources that update more frequently.\u003C/p\u003E\n","\u003Cp\u003EGo back to the Linux session and run the following script.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ewhile true\ndo\n  curl --connect-timeout 5 -k https://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | $HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming\n  sleep 30\ndone\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can now go back to the Snowflake worksheet to run a \u003Ccode\u003Eselect count(1) from flights_vw\u003C/code\u003E query every 30 seconds to verify that the row counts is indeed increasing.\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://us-west-2.console.aws.amazon.com/cloudformation/home?stacks\"\u003ECloudformation\u003C/a\u003E console.\nSelect the Cloudformation template you used to deploy the MSK cluster at the start of the demo, then click the \u003Ccode\u003EDelete\u003C/code\u003E tab. All the resources that were deployed previously, such as EC2 instances, MSK clusters, roles, etc., will be cleaned up.\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/vhol-snowpipe-streaming-msk/cleanup.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAfter the deletion of the MSK cluster, you will also need to delete the Cloudformation template for VPC if you created your own at the very beginning of the lab.\u003C/p\u003E\n","\u003Cp\u003EFor Snowflake cleanup, execute the following SQL commands.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003EUSE ROLE ACCOUNTADMIN;\n\nDROP DATABASE MSK_STREAMING_DB;\nDROP WAREHOUSE MSK_STREAMING_WH;\nDROP ROLE MSK_STREAMING_RL;\n\n-- Drop the streaming user\nDROP USER IF EXISTS STREAMING_USER;\n\u003C/code\u003E\u003C/pre\u003E\n<!---------------------------->\n","\u003Ch2\u003EConclusions\u003C/h2\u003E\n","\u003Cp\u003EIn this lab, we built a demo to show how to ingest time-series data using Snowpipe streaming and Kafka with low latency. We demonstrated this using a self-managed Kafka\nconnector on an EC2 instance. However, for a production environment, we recommend using \u003Ca href=\"https://aws.amazon.com/msk/features/msk-connect/\"\u003EAmazon MSK Connect\u003C/a\u003E, which offers\nscalability and resilience through the AWS infrastructure.\u003C/p\u003E\n","\u003Cp\u003ERelated Resources\u003C/p\u003E\n\u003Cul\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\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/msk/\"\u003EAmazon Managed Streaming for Apache Kafka (MSK)\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Getting Started with Snowpipe Streaming and Amazon MSK",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"\u003C!----------------------------\u003E\r\n## Overview\r\n\r\nSnowflake's Snowpipe streaming capabilities are designed for rowsets with variable arrival frequency.\r\nIt focuses on lower latency and cost for smaller data sets. This helps data workers stream rows into Snowflake\r\nwithout requiring files with a more attractive cost/latency profile.\r\n\r\nHere are some of the use cases that can benefit from Snowpipe streaming:\r\n- IoT time-series data ingestion\r\n- CDC streams from OLTP systems\r\n- Log ingestion from SIEM systems\r\n- Ingestion into ML feature stores\r\n\r\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 using \r\nSnowflake's Snowpipe streaming and [MSK (Amazon Managed Streaming for Apache Kafka)](https://aws.amazon.com/msk/).\r\n\r\nThe architecture diagram below shows the deployment. An MSK cluster and a Linux \r\nEC2 instance (jumphost) will be provisioned in private subnets of an AWS VPC. \r\nThe Linux jumphost will host the Kafka producer and Snowpipe streaming via [Kafka Connect](https://docs.snowflake.com/en/user-guide/kafka-connector-overview.html).\r\n\r\nThe Kafka producer calls the data sources' REST API and receives time-series data in JSON format. This data is then ingested into the Kafka cluster before being picked up by the Kafka connector and delivered to a Snowflake table.\r\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)\r\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!!).\r\nPlease note that in the demo, we are not demonstrating the visualization aspect. We will have a future Quickstart demo that focuses on visualization.\r\n\r\n![Architecture diagram for the Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/Overview-2-flight-arch.png)\r\n\r\n\r\n![Data visualization](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/Overview-2-dashboarding.png)\r\n### Prerequisites\r\n\r\n- Familiarity with Snowflake, basic SQL knowledge, Snowsight UI and Snowflake objects\r\n- Familiarity with AWS Services (e.g. EC2, MSK, etc), Networking and the Management Console\r\n- Basic knowledge of Python and Linux shell scripting\r\n\r\n### What You'll Need Before the Lab\r\n\r\nTo participate in the virtual hands-on lab, attendees need the following resources.\r\n\r\n- A [Snowflake Enterprise Account on preferred AWS region](https://signup.snowflake.com/?utm_cta=quickstarts_) with `ACCOUNTADMIN` access\r\n- An [AWS Account](https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/) with `Administrator Access`\r\n- Create your own VPC and subnets (This is optional if you have an existing VPC with subnets you can leverage. Please refer\r\nto this [AWS document](https://docs.aws.amazon.com/whitepapers/latest/amazon-msk-migration-guide/amazon-managed-streaming-for-apache-kafka-amazon-msk.html) for the MSK networking topology)\r\n  - In the AWS account, [create a VPC](https://docs.aws.amazon.com/vpc/latest/userguide/working-with-vpcs.html), preferrably in the same region as the Snowflake account\r\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.\r\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.\r\n  - Now 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. \r\n### What You'll Learn\r\n\r\n- Using [MSK (Amazon Managed Streaming for Apache Kafka)](https://aws.amazon.com/msk/)\r\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\r\nto SSH if your instance is in a private subnet\r\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.\r\n- Using Snowflake to query tables populated with time-series data\r\n\r\n### What You'll Build\r\n\r\n- [Create a provisioned Kafka cluster](https://docs.aws.amazon.com/msk/latest/developerguide/msk-create-cluster.html)\r\n- Create Kafka producers and connectors\r\n- Create topics in a Kafka cluster\r\n- A Snowflake database for hosting real-time flight data\r\n\r\n\u003C!----------------------------\u003E\r\n## Create a provisioned Kafka cluster and a Linux jumphost in AWS\r\n\r\n#### 1. Create an MSK cluster and an EC2 instance\r\nThe MSK cluster is created in a VPC managed by Amazon. We will deploy our Kafka clients in our own VPC and use security groups to ensure\r\nthe communications between the MSK cluster and clients are secure. \r\n\r\nFirst, click [here](https://console.aws.amazon.com/cloudformation/home?region=us-west-2#/stacks/new?stackName=MSK-Snowflake&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_Snowpipe_Streaming_MSK/msk-CFT-for-SE-Sandbox.json)\r\nto launch a provisioned MSK cluster. Note the default AWS region is `us-west-2 (Oregon)`, feel free to select a region you prefer to deploy the environment.\r\n\r\nClick `Next` at the `Create stack` page. \r\nSet the Stack name or modify the default value to customize it to your identity. Leave the default Kafka version as is. For `MSKSecurityGroupId`, we recommend\r\nto use the [default security group](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/default-custom-security-groups.html) in your VPC. In the drop-down menu, pick two subnets, they can be either public or private subnets depending on the network layout of your VPC. Leave `TLSMutualAuthentication` as false and the jumphost instance type and AMI id as default before clicking\r\n`Next`. \r\n\r\nSee below sample screen capture for reference.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/msk-cft-stack-1.png)\r\n\r\nLeave everything as default in the `Configure stack options` page and click `Next`.\r\nIn the `Review` page, click `Submit`.\r\n\r\nIn about 10-30 minutes depending on your AWS region, the Cloudformation template provisions an MSK cluster with two brokers. It will also\r\nprovision a Linux EC2 instance in the subnet you selected. We will then use it to run the Kafka connector with Snowpipe streaming SDK and the producer.\r\n\r\n#### 2. Configure the Linux session for timeout and default shell\r\n\r\nIn this step we need to connect to the EC2 instance in order to interact with the MSK cluster.\r\n\r\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 MSK cluster,\r\nClick `Session Manager` on the left pane.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/session-mgr-1.png)\r\n\r\nNext, we will set the preferred shell as bash.\r\n\r\nClick the `Preferences` tab.\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/session-mgr-1.0.png)\r\n\r\nClick the `Edit` button.\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/session-mgr-1.1.png)\r\n\r\nGo to `General preferences` section, type in 60 minutes for idle session timeout value.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/session-mgr-1.1.5.png)\r\n\r\nFurther scroll down to `Linux shell profile` section, and type in `/bin/bash` before clicking `Save` button.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/session-mgr-1.2.png)\r\n\r\n#### 3. Connect to the Linux EC2 instance console\r\nNow go back to the `Session` tab and click the `Start session` button.\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/session-mgr-2.png)\r\n\r\nNow you should see the EC2 instance created by the Cloudformation template under `Target instances`.\r\nIts name should be `\u003CCloudformation stack name\u003E-jumphost`, select it and click `Start session`.\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/session-mgr-3.png)\r\n\r\n#### 4. Create a key-pair to be used for authenticating with Snowflake programmatically\r\nCreate a key pair in AWS Session Manager console by executing the following commands. You will be prompted to give an encryption password, remember \r\nthis phrase, you will need it later.\r\n\r\n```commandline\r\ncd $HOME\r\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8\r\n```\r\nSee below example screenshot:\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/key-pair-sessionmgr-1.png)\r\n\r\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.\r\n```\r\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\r\n```\r\nsee below example screenshot:\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/key-pair-sessionmgr-2.png)\r\n\r\nNext we will print out the public key string in a correct format that we can use for Snowflake.\r\n```\r\ngrep -v KEY rsa_key.pub | tr -d 'n' \u003E pub.Key\r\ncat pub.Key\r\n\r\n```\r\nsee below example screenshot:\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/key-pair-sessionmgr-3.png)\r\n\r\n\r\n#### 5. Install the Kafka connector for Snowpipe streaming\r\n\r\nRun the following command to install the Kafka connector and Snowpipe streaming SDK\r\n\r\n```commandline\r\npasswd=changeit  # Use the default password for the Java keystore, you should chang it after finishing the lab\r\ndirectory=/home/azureuser/snowpipe-streaming # Installation directory\r\n\r\ncd $HOME\r\nmkdir -p $directory\r\ncd $directory\r\npwd=`pwd`\r\nsudo yum clean all\r\nsudo yum -y install openssl vim-common java-1.8.0-openjdk-devel.x86_64 gzip tar jq python3-pip\r\nwget https://archive.apache.org/dist/kafka/2.8.1/kafka_2.12-2.8.1.tgz\r\ntar xvfz kafka_2.12-2.8.1.tgz -C $pwd\r\nwget https://github.com/aws/aws-msk-iam-auth/releases/download/v1.1.1/aws-msk-iam-auth-1.1.1-all.jar -O $pwd/kafka_2.12-2.8.1/libs/aws-msk-iam-auth-1.1.1-all.jar\r\nrm -rf $pwd/kafka_2.12-2.8.1.tgz\r\ncd /tmp && cp /usr/lib/jvm/java-openjdk/jre/lib/security/cacerts kafka.client.truststore.jks\r\ncd /tmp && keytool -genkey -keystore kafka.client.keystore.jks -validity 300 -storepass $passwd -keypass $passwd -dname \"CN=snowflake.com\" -alias snowflake -storetype pkcs12\r\n\r\n#Snowflake kafka connector\r\nwget https://repo1.maven.org/maven2/com/snowflake/snowflake-kafka-connector/1.8.2/snowflake-kafka-connector-1.8.2.jar -O $pwd/kafka_2.12-2.8.1/libs/snowflake-kafka-connector-1.8.2.jar\r\n\r\n#Snowpipe streaming SDK\r\nwget https://repo1.maven.org/maven2/net/snowflake/snowflake-ingest-sdk/1.0.3-beta/snowflake-ingest-sdk-1.0.3-beta.jar   -O $pwd/kafka_2.12-2.8.1/libs/snowflake-ingest-sdk-1.0.3-beta.jar  \r\nwget https://repo1.maven.org/maven2/net/snowflake/snowflake-jdbc/3.13.15/snowflake-jdbc-3.13.15.jar -O $pwd/kafka_2.12-2.8.1/libs/snowflake-jdbc-3.13.15.jar\r\nwget https://repo1.maven.org/maven2/org/bouncycastle/bc-fips/1.0.1/bc-fips-1.0.1.jar -O $pwd/kafka_2.12-2.8.1/libs/bc-fips-1.0.1.jar\r\nwget https://repo1.maven.org/maven2/org/bouncycastle/bcpkix-fips/1.0.3/bcpkix-fips-1.0.3.jar -O $pwd/kafka_2.12-2.8.1/libs/bcpkix-fips-1.0.3.jar\r\n\r\n```\r\n\r\n#### 6. Retrieve the broker string from the MSK cluster.\r\nGo to the [MSK](https://us-west-2.console.aws.amazon.com/msk/#/clusters) console and click on the newly created MSK cluster, it should have a substring `MSKCluster` in its name.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/bs-1.png)\r\n\r\nClick `View client information`\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/bs-2.png)\r\n\r\nWe are going to use TLS authentication between the client and brokers. Copy down the broker string under `Private endpoint` for TLS authentication type.\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/bs-3.png)\r\n\r\nNow switch back to the Session Manager window and execute the following command by replacing `\u003Cbroker string\u003E` with \r\nthe copied values.\r\n\r\n```commandline\r\nexport BS=\u003Cbroker string\u003E\r\n```\r\n\r\nNow run the following command to add `BS` as an environment variable so it is recognized across the Linux sessions.\r\n```\r\necho \"export BS=$BS\" \u003E\u003E ~/.bashrc\r\n```\r\nSee the following example screen capture.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/bs-4.png)\r\n#### 7. Create a configuration file `connect-standalone.properties` for the Kafka connector\r\n\r\nRun the following commands to generate a configuration file `connect-standalone.properties` in directory `/home/azureuser/snowpipe-streaming/scripts` for the client to authenticate\r\nwith the Kafka cluster.\r\n\r\n```commandline\r\ndir=/home/azureuser/snowpipe-streaming/scripts\r\nmkdir -p $dir && cd $dir\r\ncat \u003C\u003C EOF \u003E $dir/connect-standalone.properties\r\n#************CREATING SNOWFLAKE Connector****************\r\nbootstrap.servers=$BS\r\n\r\n#************SNOWFLAKE VALUE CONVERSION****************\r\nkey.converter=org.apache.kafka.connect.storage.StringConverter\r\nvalue.converter=com.snowflake.kafka.connector.records.SnowflakeJsonConverter\r\nkey.converter.schemas.enable=true\r\nvalue.converter.schemas.enable=true\r\n#************SNOWFLAKE ****************\r\n\r\noffset.storage.file.filename=/tmp/connect.offsets\r\n# Flush much faster than normal, which is useful for testing/debugging\r\noffset.flush.interval.ms=10000\r\n\r\n#*********** FOR SSL  ****************\r\nsecurity.protocol=SSL\r\nssl.truststore.location=/tmp/kafka.client.truststore.jks\r\nssl.truststore.password=changeit\r\nssl.enabled.protocols=TLSv1.1,TLSv1.2\r\n\r\nconsumer.security.protocol=SSL\r\nconsumer.ssl.truststore.location=/tmp/kafka.client.truststore.jks\r\nconsumer.ssl.truststore.password=changeit\r\nconsumer.ssl.enabled.protocols=TLSv1.1,TLSv1.2\r\nEOF\r\n\r\n```\r\nA configuration file `connect-standalone.properties` is created in directory `/home/azureuser/snowpipe-streaming/scripts`\r\n\r\n#### 8. Create a security client.properties configuration file for the producer\r\n\r\nRun the following commands to create a security configuration file `client.properties` for the MSK cluster\r\n```commandline\r\ndir=/home/azureuser/snowpipe-streaming/scripts\r\ncat \u003C\u003C EOF \u003E $dir/client.properties\r\nsecurity.protocol=SSL\r\nssl.truststore.location=/tmp/kafka.client.truststore.jks\r\nssl.truststore.password=changeit\r\nssl.enabled.protocols=TLSv1.1,TLSv1.2\r\nEOF\r\n\r\n```\r\n\r\nA configuration file `client.properties` is created in directory `/home/azureuser/snowpipe-streaming/scripts`\r\n\r\n#### 9. Create a streaming topic called “streaming” in the MSK cluster\r\n\r\nNow we can run the following commands to create a Kafka topic on the MSK cluster to stream our data.\r\n \r\n```commandline\r\n$HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --create --topic streaming --partitions 2 --replication-factor 2\r\n```\r\nYou should see the response `Created topic streaming` if it is successful.\r\n\r\nTo describe the topic, run the following commands:\r\n```commandline\r\n$HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --describe --topic streaming\r\n```\r\nYou should see there are two partitions with a replication factor of 2 in the `streaming` topic.\r\nSee below example screenshot:\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/list-kafka-topic.png)\r\n\r\n\u003C!----------------------------\u003E\r\n## Configure Snowflake to communicate with the MSK cluster\r\n\r\n#### 1. Creating user, role, and database\r\nFirst login to your Snowflake account as a power user with ACCOUNTADMIN role. \r\nThen run the following SQL commands in a worksheet to create a user, database and the role that we will use in the lab.\r\n\r\n```\r\n-- Set default value for multiple variables\r\n-- For purpose of this workshop, it is recommended to use these defaults during the exercise to avoid errors\r\n-- You should change them after the workshop\r\nSET PWD = 'Test1234567';\r\nSET USER = 'STREAMING_USER';\r\nSET DB = 'MSK_STREAMING_DB';\r\nSET WH = 'MSK_STREAMING_WH';\r\nSET ROLE = 'MSK_STREAMING_RL';\r\n\r\nUSE ROLE ACCOUNTADMIN;\r\n\r\n-- CREATE USERS\r\nCREATE USER IF NOT EXISTS IDENTIFIER($USER) PASSWORD=$PWD  COMMENT='STREAMING USER';\r\n\r\n-- CREATE ROLES\r\nCREATE OR REPLACE ROLE IDENTIFIER($ROLE);\r\n\r\n-- CREATE DATABASE AND WAREHOUSE\r\nCREATE DATABASE IF NOT EXISTS IDENTIFIER($DB);\r\nUSE IDENTIFIER($DB);\r\nCREATE OR REPLACE WAREHOUSE IDENTIFIER($WH) WITH WAREHOUSE_SIZE = 'SMALL';\r\n\r\n-- GRANTS\r\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE IDENTIFIER($ROLE);\r\nGRANT ROLE IDENTIFIER($ROLE) TO USER IDENTIFIER($USER);\r\nGRANT OWNERSHIP ON DATABASE IDENTIFIER($DB) TO ROLE IDENTIFIER($ROLE);\r\nGRANT USAGE ON WAREHOUSE IDENTIFIER($WH) TO ROLE IDENTIFIER($ROLE);\r\n\r\n-- SET DEFAULTS\r\nALTER USER IDENTIFIER($USER) SET DEFAULT_ROLE=$ROLE;\r\nALTER USER IDENTIFIER($USER) SET DEFAULT_WAREHOUSE=$WH;\r\n\r\n```\r\n\r\nNow logout of Snowflake, sign back in as the default user `streaming_user` we just created with the associated password (default: Test1234567).\r\nRun the following SQL commands in a worksheet:\r\n\r\n```commandline\r\nSET DB = 'MSK_STREAMING_DB';\r\nSET SCHEMA = 'MSK_STREAMING_SCHEMA';\r\n\r\nUSE IDENTIFIER($DB);\r\nCREATE OR REPLACE SCHEMA IDENTIFIER($SCHEMA);\r\n```\r\n\r\n#### 2. Install SnowSQL (optional but highly recommended)\r\n\r\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.\r\n\r\nFirst, in the Snowflake worksheet, replace `\u003CpubKey\u003E` with the content of the file `/home/azureuser/pub.Key` (see step 3 in previous section) in the following SQL command and execute.\r\n```commandline\r\nuse role accountadmin;\r\nalter user streaming_user set rsa_public_key='\u003CpubKey\u003E';\r\n```\r\nSee below example screenshot:\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/key-pair-snowflake.png)\r\n\r\nNow we can install SnowSQL. Execute the following commands on the Linux Session Manager console:\r\n```commandline\r\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\r\necho -e \"~/bin n y\" \u003E /tmp/ans\r\nbash /tmp/snowsql-1.2.24-linux_x86_64.bash \u003C /tmp/ans\r\n\r\n```\r\nSee below example screenshot:\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/install-snowsql.png)\r\n\r\nNext set the environment variable for Snowflake Private Key Phrase:\r\n```commandline\r\nexport SNOWSQL_PRIVATE_KEY_PASSPHRASE=\u003Ckey phrase you set up when running openssl previously\u003E\r\n```\r\nNote that you should add the command above in the ~/.bashrc file to preserve this environment variable across sessions.\r\n```commandline\r\necho \"export SNOWSQL_PRIVATE_KEY_PASSPHRASE=$SNOWSQL_PRIVATE_KEY_PASSPHRASE\" \u003E\u003E ~/.bashrc\r\n```\r\n\r\nNow you can execute this command to interact with Snowflake:\r\n```commandline\r\n$HOME/bin/snowsql -a \u003CSnowflake Account Name\u003E --region \u003CAWS region where Snowflake is located\u003E -u streaming_user --private-key-path $HOME/rsa_key.p8 -d msk_streaming_db -s msk_streaming_schema\r\n```\r\nSee below example screenshot:\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/key-pair-snowsql.png)\r\n\r\nType `Ctrl-D` to get out of SnowSQL session.\r\n\r\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.\r\n\r\nAt this point, the Snowflake setup is complete.\r\n\r\n\u003C!----------------------------\u003E\r\n## Configure Kafka connector to Snowflake with Snowpipe streaming SDK\r\n\r\n\r\n#### 1. Run the following commands to collect various connection parameters for the Kafka connector\r\n```commandline\r\ncd $HOME\r\noutf=/tmp/params\r\ncat \u003C\u003C EOF \u003E /tmp/get_params\r\na=''\r\nuntil [ ! -z $a ]\r\ndo\r\n read -p \"Input Snowflake account URL: e.g. gwa123456 ==\u003E \" a\r\ndone\r\n\r\nr=''\r\nuntil  [ ! -z $r ]\r\ndo\r\n  read -p \"Input Snowflake account region: e.g. us-west-2 ==\u003E \" r\r\ndone\r\n\r\nif [[ $r == \"us-west-2\" ]]\r\nthen\r\n  echo export clstr_url=$a.snowflakecomputing.com \u003E $outf\r\n  export clstr_url=$a.snowflakecomputing.com\r\nelse\r\n  echo export clstr_url=$a.$r.snowflakecomputing.com \u003E $outf\r\n  export clstr_url=$a.$r.snowflakecomputing.com \r\nfi\r\n\r\nread -p \"Snowflake cluster user name: default: streaming_user ==\u003E \" user\r\nif [[ $user == \"\" ]]\r\nthen\r\n   user=\"streaming_user\"\r\nfi\r\n\r\necho export user=$user \u003E\u003E $outf\r\nexport user=$user\r\n\r\npass=''\r\nuntil [ ! -z $pass ]\r\ndo\r\n  read -p \"Private key passphrase ==\u003E \" pass\r\ndone\r\n\r\necho export key_pass=$pass \u003E\u003E $outf\r\nexport key_pass=$pass\r\n\r\nread -p \"Full path to your Snowflake private key file, default: /home/azureuser/rsa_key.p8 ==\u003E \" p8\r\nif [[ $p8 == \"\" ]]\r\nthen\r\n   p8=\"/home/azureuser/rsa_key.p8\"\r\nfi\r\n\r\npriv_key=`cat $p8 | grep -v PRIVATE | tr -d 'n'`\r\necho export priv_key=$priv_key  \u003E\u003E $outf\r\nexport priv_key=$priv_key\r\ncat $outf \u003E\u003E $HOME/.bashrc\r\nEOF\r\n. /tmp/get_params\r\n\r\n```\r\nSee below example screen capture.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/get_params.png)\r\n\r\n#### 2. Run the following commands to create a Snowflake Kafka connect property configuration file:\r\n```commandline\r\ndir=/home/azureuser/snowpipe-streaming/scripts\r\ncat \u003C\u003C EOF \u003E $dir/snowflakeconnectorMSK.properties\r\nname=snowpipeStreaming\r\nconnector.class=com.snowflake.kafka.connector.SnowflakeSinkConnector\r\ntasks.max=4\r\ntopics=streaming\r\nsnowflake.private.key.passphrase=$key_pass\r\nsnowflake.database.name=MSK_STREAMING_DB\r\nsnowflake.schema.name=MSK_STREAMING_SCHEMA\r\nsnowflake.topic2table.map=streaming:MSK_STREAMING_TBL\r\nbuffer.count.records=10000\r\nbuffer.flush.time=5\r\nbuffer.size.bytes=20000000\r\nsnowflake.url.name=$clstr_url\r\nsnowflake.user.name=$user\r\nsnowflake.private.key=$priv_key\r\nsnowflake.role.name=MSK_STREAMING_RL\r\nsnowflake.ingestion.method=snowpipe_streaming\r\nvalue.converter.schemas.enable=false\r\njmx=true\r\nkey.converter=org.apache.kafka.connect.storage.StringConverter\r\nvalue.converter=org.apache.kafka.connect.json.JsonConverter\r\nerrors.tolerance=all\r\nEOF\r\n```\r\n\r\n\u003C!----------------------------\u003E\r\n## Putting it all together\r\n\r\nFinally, we are ready to start ingesting data into the Snowflake table.\r\n\r\n#### 1. Start the Kafka Connector for Snowpipe streaming\r\n\r\nGo back to the Linux console and execute the following commands to start the Kafka connector.\r\n```commandline\r\n$HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/connect-standalone.sh $HOME/snowpipe-streaming/scripts/connect-standalone.properties $HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties\r\n```\r\n\r\nIf everything goes well, you should something similar to screen capture below:\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/snowpipe-streaming-kc.png)\r\n\r\n#### 2. Start the producer that will ingest real-time data to the MSK cluster\r\n\r\nStart a new Linux session by following `step 3` in the section named `Create a provisioned Kafka cluster and a Linux jumphost in AWS`\r\n```commandline\r\ncurl --connect-timeout 5 -k https://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | $HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming\r\n```\r\nYou should see response similar to screen capture below if everything works well.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/producer.png)\r\n\r\nNote that in the script above, the producer queries a [Rest API](http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky ) that provides real-time flight data over the San Francisco \r\nBay Area in JSON format. The data includes information such as timestamps, [icao](https://icao.usmission.gov/mission/icao/#:~:text=Home%20%7C%20About%20the%20Mission%20%7C%20U.S.,civil%20aviation%20around%20the%20world.) numbers, flight IDs, destination airport, longitude, \r\nlatitude, and altitude of the aircraft, etc. The data is ingested into the `streaming` topic on the MSK cluster and \r\nthen picked up by the Snowpipe streaming Kafka connector, which delivers it directly into a Snowflake \r\ntable `msk_streaming_db.msk_streaming_schema.msk_streaming_tbl`.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/flight-json.png)\r\n\r\n\u003C!----------------------------\u003E\r\n## Query ingested data in Snowflake\r\n\r\nNow, switch back to the Snowflake console and make sure that you signed in as the default user `streaming_user`. \r\nThe data should have been streamed into a table, ready for further processing.\r\n\r\n#### 1. Query the raw data\r\nTo verify that data has been streamed into Snowflake, execute the following SQL commands.\r\n\r\n```commandline\r\nuse msk_streaming_db;\r\nuse schema msk_streaming_schema;\r\nshow channels in table msk_streaming_tbl;\r\n```\r\nYou should see that there are two channels, corresponding to the two partitions created earlier in the topic.\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/channels.png)\r\n\r\nNow run the following query on the table.\r\n```\r\nselect * from msk_streaming_tbl;\r\n```\r\nYou should see there are two columns in the table: `RECORD_METADATA` and `RECORD_CONTENT` as shown in the screen capture below.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/raw_data.png)\r\nThe `RECORD_CONTENT` column is an JSON array that needs to be flattened.\r\n\r\n#### 2. Flatten the raw JSON data\r\nNow execute the following SQL commands to flatten the raw JSONs and create a materialized view with multiple columns based on the key names.\r\n\r\n```commandline\r\ncreate or replace materialized view flights_vw\r\n  as select\r\n    f.value:utc::timestamp_ntz ts_utc,\r\n    CONVERT_TIMEZONE('UTC','America/Los_Angeles',ts_utc::timestamp_ntz) as ts_pt,\r\n    f.value:alt::integer alt,\r\n    f.value:dest::string dest,\r\n    f.value:orig::string orig,\r\n    f.value:id::string id,\r\n    f.value:icao::string icao,\r\n    f.value:lat::float lat,\r\n    f.value:lon::float lon,\r\n    st_geohash(to_geography(ST_MAKEPOINT(lon, lat)),12) geohash,\r\n    year(ts_pt) yr,\r\n    month(ts_pt) mo,\r\n    day(ts_pt) dd,\r\n    hour(ts_pt) hr\r\nFROM   msk_streaming_tbl,\r\n       Table(Flatten(msk_streaming_tbl.record_content)) f;\r\n```\r\n\r\nThe SQL commands create a view, convert 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 like Grafana\r\n\r\nLet's query the view `flights_vw` now.\r\n```commandline\r\nselect * from flights_vw;\r\n```\r\n\r\nAs a result, you will see a nicely structured output with columns derived from the JSONs\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/materialized_view.png)\r\n\r\n#### 3. Stream real-time flight data continuously to Snowflake\r\n\r\nWe can now write a loop to stream the flight data continuously into Snowflake.\r\n\r\nNote that there is a 30 seconds sleep time between the queries, it is because for our data source in this workshop, anything\r\nless than 30 seconds will not incur any new data as the source won't update more frequently than 30 seconds.\r\nFeel free to lower the frequency for other data sources that update more frequently.\r\n\r\nGo back to the Linux session and run the following script.\r\n\r\n```commandline\r\nwhile true\r\ndo\r\n  curl --connect-timeout 5 -k https://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | $HOME/snowpipe-streaming/kafka_2.12-2.8.1/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming\r\n  sleep 30\r\ndone\r\n\r\n```\r\nYou can now go back to the Snowflake worksheet to run a `select count(1) from flights_vw` query every 30 seconds to verify that the row counts is indeed increasing.\r\n\r\n\u003C!-----------------------------\u003E\r\n## Cleanup\r\n\r\nWhen you are done with the demo, to tear down the AWS resources, simply go to the [Cloudformation](https://us-west-2.console.aws.amazon.com/cloudformation/home?stacks) console.\r\nSelect the Cloudformation template you used to deploy the MSK cluster at the start of the demo, then click the `Delete` tab. All the resources that were deployed previously, such as EC2 instances, MSK clusters, roles, etc., will be cleaned up.\r\n\r\nSee example screen capture below.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowpipe-streaming-msk/cleanup.png)\r\n\r\nAfter the deletion of the MSK cluster, you will also need to delete the Cloudformation template for VPC if you created your own at the very beginning of the lab.\r\n\r\nFor Snowflake cleanup, execute the following SQL commands.\r\n\r\n```commandline\r\nUSE ROLE ACCOUNTADMIN;\r\n\r\nDROP DATABASE MSK_STREAMING_DB;\r\nDROP WAREHOUSE MSK_STREAMING_WH;\r\nDROP ROLE MSK_STREAMING_RL;\r\n\r\n-- Drop the streaming user\r\nDROP USER IF EXISTS STREAMING_USER;\r\n```\r\n\r\n\u003C!----------------------------\u003E\r\n## Conclusions\r\n\r\nIn this lab, we built a demo to show how to ingest time-series data using Snowpipe streaming and Kafka with low latency. We demonstrated this using a self-managed Kafka \r\nconnector on an EC2 instance. However, for a production environment, we recommend using [Amazon MSK Connect](https://aws.amazon.com/msk/features/msk-connect/), which offers \r\nscalability and resilience through the AWS infrastructure.\r\n\r\nRelated Resources\r\n\r\n- [Getting started with Snowflake](/en/developers/guides/)\r\n- [Snowflake on AWS Marketplace](https://aws.amazon.com/marketplace/seller-profile?id=18d60ae8-2c99-4881-a31a-e74770d70347)\r\n- [Snowflake for Data Sharing](/Workloads/data-sharing/)\r\n- [Snowflake Marketplace](/en/data-cloud/marketplace/)\r\n- [Amazon Managed Streaming for Apache Kafka (MSK)](https://aws.amazon.com/msk/)\r\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-e6fa81540f","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-7f553b22c1",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0f41b18a42","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-82469b19c5","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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