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MSK","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/getting-started-with-snowpipe-streaming-aws-msk","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for 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guide will walk you through how to apply a data-intensive approach to real-time data using Snowpipe Streaming and Amazon Managed Streaming for Apache Kafka (MSK).","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\u003ENote that you can either stream the data into a regular Snowflake table or a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg\"\u003ESnowflake managed Apache Iceberg table\u003C/a\u003E depending on your use case.\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 the Snowflake \u003Ca href=\"https://docs.snowflake.com/en/connectors/kafkahp/setup-kafka\"\u003EHigh Performance (HP) Kafka Connector\u003C/a\u003E (v4.x) via \u003Ca href=\"https://docs.snowflake.com/en/user-guide/kafka-connector-overview.html\"\u003EKafka Connect\u003C/a\u003E. The HP connector uses a server-side architecture with a PIPE object in Snowflake that manages data processing and buffering, delivering up to 10 GB/s throughput per table with 5-10 second latency.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This quickstart uses the Snowflake High Performance (HP) Kafka Connector (v4), which is currently \u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-kafka-connector-v4-ga/?utm_campaign=Product&utm_content=1777396741&utm_medium=Snowflake&utm_source=linkedin\"\u003EGA\u003C/a\u003E\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\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.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/Overview-2-flight-arch.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/Overview-2-dashboarding.png?v=baaef5e9\" 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_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. 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.\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/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 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\u003ESubnet1\u003C/code\u003E and \u003Ccode\u003ESubnet2\u003C/code\u003E, in the drop-down menu, pick two different subnets respectively, they can be either public or private subnets depending on the network layout of your VPC. Please note that if\nyou plan to use \u003Ca href=\"https://aws.amazon.com/msk/features/msk-connect/\"\u003EAmazon MSK Connect\u003C/a\u003E later, you should choose private subnets here.\nFor \u003Ccode\u003EMSKSecurityGroupId\u003C/code\u003E, we recommend\nusing 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, if you do not have the default security group, \u003Ca href=\"https://docs.aws.amazon.com/vpc/latest/userguide/default-security-group.html\"\u003Ecreate one\u003C/a\u003E on your own before moving forward. 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/getting-started-with-snowpipe-streaming-aws-msk/msk-cft-stack-1.png?v=baaef5e9\" 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/getting-started-with-snowpipe-streaming-aws-msk/session-mgr-1.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/session-mgr-1.0.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/session-mgr-1.1.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/session-mgr-1.1.5.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/session-mgr-1.2.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/session-mgr-2.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/session-mgr-3.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/key-pair-sessionmgr-1.png?v=baaef5e9\" 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-snowpipe-streaming-aws-msk/key-pair-sessionmgr-2.png?v=baaef5e9\" 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' | awk '{print $1}' > pub.Key\ncat pub.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-snowpipe-streaming-aws-msk/key-pair-sessionmgr-3.png?v=baaef5e9\" 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 change it after finishing the lab\ndirectory=/home/ssm-user/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 gzip tar jq python3-pip\nsudo amazon-linux-extras install java-openjdk11 -y\nwget https://archive.apache.org/dist/kafka/3.7.2/kafka_2.13-3.7.2.tgz\ntar xvfz kafka_2.13-3.7.2.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_2.13-3.7.2/libs/aws-msk-iam-auth-1.1.1-all.jar\nrm -rf $pwd/kafka_2.13-3.7.2.tgz\ncd /tmp && cp $(find /usr/lib/jvm -name cacerts 2>/dev/null | head -1) 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 High Performance (HP) Kafka connector v4.x (Public Preview) — uses server-side Snowpipe Streaming architecture\n#v4.x is an uber-jar that bundles snowflake-ingest-sdk and snowflake-jdbc internally\nwget https://repo1.maven.org/maven2/com/snowflake/snowflake-kafka-connector/4.0.0-rc8/snowflake-kafka-connector-4.0.0-rc8.jar -O $pwd/kafka_2.13-3.7.2/libs/snowflake-kafka-connector-4.0.0-rc8.jar\n\nwget https://repo1.maven.org/maven2/org/bouncycastle/bc-fips/2.1.0/bc-fips-2.1.0.jar -O $pwd/kafka_2.13-3.7.2/libs/bc-fips-2.1.0.jar\nwget https://repo1.maven.org/maven2/org/bouncycastle/bcpkix-fips/2.1.8/bcpkix-fips-2.1.8.jar -O $pwd/kafka_2.13-3.7.2/libs/bcpkix-fips-2.1.8.jar\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that the version numbers for Kafka, the Snowflake Kafka connector, and the Snowpipe Streaming SDK are dynamic, as new versions are continually published. We are using the version numbers that have been validated to work.\u003C/p\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 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/getting-started-with-snowpipe-streaming-aws-msk/bs-1.png?v=baaef5e9\" 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/getting-started-with-snowpipe-streaming-aws-msk/bs-2.png?v=baaef5e9\" 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/getting-started-with-snowpipe-streaming-aws-msk/bs-3.png?v=baaef5e9\" 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/getting-started-with-snowpipe-streaming-aws-msk/bs-4.png?v=baaef5e9\" 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/ssm-user/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/ssm-user/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=org.apache.kafka.connect.json.JsonConverter\nkey.converter.schemas.enable=false\nvalue.converter.schemas.enable=false\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/ssm-user/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/ssm-user/snowpipe-streaming/scripts\ncat << EOF > $dir/client.properties\nsecurity.protocol=SSL\nssl.truststore.location=/tmp/kafka.client.truststore.jks\nssl.truststore.password=changeit\nssl.enabled.protocols=TLSv1.1,TLSv1.2\nEOF\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA configuration file \u003Ccode\u003Eclient.properties\u003C/code\u003E is created in directory \u003Ccode\u003E/home/ssm-user/snowpipe-streaming/scripts\u003C/code\u003E\u003C/p\u003E\n","\u003Ch4\u003E9. Create a streaming topic called “streaming” in the MSK cluster\u003C/h4\u003E\n","\u003Cp\u003ENow we can run the following commands to create a Kafka topic on the MSK cluster to stream our data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003E$HOME/snowpipe-streaming/kafka_2.13-3.7.2/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_2.13-3.7.2/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --describe --topic streaming\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see there are two partitions with a replication factor of 2 in the \u003Ccode\u003Estreaming\u003C/code\u003E topic.\nSee below example screenshot:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/list-kafka-topic.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EPrepare the Snowflake account for streaming\u003C/h2\u003E\n","\u003Ch4\u003E1. Creating user, role, database, and schema\u003C/h4\u003E\n","\u003Cp\u003ELogin to your Snowflake account as a power user with ACCOUNTADMIN role.\nThen run the following SQL commands in a worksheet to create the dedicated streaming user, role, database, schema, and warehouse that we will use in the lab. The streaming user uses key-pair authentication only (no password).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E-- Set default value for multiple variables\n-- For purpose of this workshop, it is recommended to use these defaults during the exercise to avoid errors\n-- You should change them after the workshop\nSET USER = 'STREAMING_USER';\nSET DB = 'MSK_STREAMING_DB';\nSET SCHEMA = 'MSK_STREAMING_SCHEMA';\nSET WH = 'MSK_STREAMING_WH';\nSET ROLE = 'MSK_STREAMING_RL';\n\nUSE ROLE ACCOUNTADMIN;\n\n-- CREATE ROLE\nCREATE OR REPLACE ROLE IDENTIFIER($ROLE);\n\n-- CREATE USER (key-pair auth only, no password)\n-- Uses IF NOT EXISTS so it won't fail if the user already exists.\n-- The ALTER USER below ensures correct settings even for a pre-existing user.\nCREATE USER IF NOT EXISTS IDENTIFIER($USER)\n  DEFAULT_ROLE = $ROLE\n  DEFAULT_WAREHOUSE = $WH\n  COMMENT = 'Streaming connector user - key-pair auth only';\n\nALTER USER IF EXISTS IDENTIFIER($USER) SET\n  DEFAULT_ROLE = $ROLE\n  DEFAULT_WAREHOUSE = $WH\n  DISABLED = FALSE;\n\n-- CREATE DATABASE, SCHEMA AND WAREHOUSE\nCREATE DATABASE IF NOT EXISTS IDENTIFIER($DB);\nCREATE OR REPLACE SCHEMA IDENTIFIER($DB).IDENTIFIER($SCHEMA);\nCREATE OR REPLACE WAREHOUSE IDENTIFIER($WH) WITH WAREHOUSE_SIZE = 'SMALL';\n\n-- GRANTS\nGRANT ROLE IDENTIFIER($ROLE) TO USER IDENTIFIER($USER);\nGRANT OWNERSHIP ON DATABASE IDENTIFIER($DB) TO ROLE IDENTIFIER($ROLE) COPY CURRENT GRANTS;\nGRANT OWNERSHIP ON SCHEMA IDENTIFIER($DB).IDENTIFIER($SCHEMA) TO ROLE IDENTIFIER($ROLE) COPY CURRENT GRANTS;\nGRANT USAGE ON WAREHOUSE IDENTIFIER($WH) TO ROLE IDENTIFIER($ROLE);\nGRANT CREATE TABLE ON SCHEMA IDENTIFIER($DB).IDENTIFIER($SCHEMA) TO ROLE IDENTIFIER($ROLE);\nGRANT CREATE PIPE ON SCHEMA IDENTIFIER($DB).IDENTIFIER($SCHEMA) TO ROLE IDENTIFIER($ROLE);\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\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-snowpipe-streaming-aws-msk/account-identifier.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext we need to configure the public key for the streaming user to access Snowflake programmatically.\u003C/p\u003E\n","\u003Cp\u003EIn the Snowflake worksheet, replace \u003Ccode\u003E< pubKey >\u003C/code\u003E with the content of the file \u003Ccode\u003E/home/ssm-user/pub.Key\u003C/code\u003E (see \u003Ccode\u003Estep 4\u003C/code\u003E by clicking on \u003Ccode\u003Esection #2 Create a provisioned Kafka cluster and a Linux jumphost in AWS\u003C/code\u003E 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-snowpipe-streaming-aws-msk/key-pair-snowflake.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAt this point, the Snowflake setup is complete.\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EConfigure Kafka connector for Snowpipe Streaming\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 identifier: e.g. ylmxgak-wyb53646 ==> " a\ndone\n\necho export clstr_url=\\$a.snowflakecomputing.com > $outf\nexport clstr_url=\\$a.snowflakecomputing.com\n\nread -p "Snowflake 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/ssm-user/rsa_key.p8 ==> " p8\nif [[ \\$p8 == "" ]]\nthen\n   p8="/home/ssm-user/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/getting-started-with-snowpipe-streaming-aws-msk/get_params.png?v=baaef5e9\" 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","\u003Cp\u003ENote that the High Performance (HP) connector (v4.x) will auto-create a default PIPE object named \u003Ccode\u003EMSK_STREAMING_TBL-STREAMING\u003C/code\u003E in the target schema when the connector starts. This is why the \u003Ccode\u003EGRANT CREATE PIPE\u003C/code\u003E was added earlier.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Edir=/home/ssm-user/snowpipe-streaming/scripts\ncat << EOF > $dir/snowflakeconnectorMSK.properties\nname=snowpipeStreamingHP\nconnector.class=com.snowflake.kafka.connector.SnowflakeStreamingSinkConnector\ntasks.max=4\ntopics=streaming\nsnowflake.private.key.passphrase=$key_pass\nsnowflake.database.name=MSK_STREAMING_DB\nsnowflake.schema.name=MSK_STREAMING_SCHEMA\nsnowflake.topic2table.map=streaming:MSK_STREAMING_TBL\nsnowflake.url.name=$clstr_url\nsnowflake.user.name=$user\nsnowflake.private.key=$priv_key\nsnowflake.role.name=MSK_STREAMING_RL\nsnowflake.ingestion.method=SNOWPIPE_STREAMING\nsnowflake.streaming.v2.enabled=true\nsnowflake.enable.schematization=TRUE\nbuffer.count.records=10000\nbuffer.flush.time=10\nbuffer.size.bytes=20000000\nvalue.converter.schemas.enable=false\nkey.converter=org.apache.kafka.connect.storage.StringConverter\nvalue.converter=org.apache.kafka.connect.json.JsonConverter\nerrors.tolerance=all\nerrors.log.enable=true\nEOF\n\u003C/code\u003E\u003C/pre\u003E\n<!---------------------------->\n","\u003Ch2\u003EPutting it all together\u003C/h2\u003E\n","\u003Cp\u003EFinally, we are ready to start ingesting data into the Snowflake table.\u003C/p\u003E\n","\u003Ch4\u003E1. Start the Kafka Connector for Snowpipe streaming\u003C/h4\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E If your Snowflake account has a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/network-policies\"\u003Enetwork policy\u003C/a\u003E enabled, make sure the EC2 instance's public IP (or NAT gateway IP for private subnets) is included in the allowed IP list. Otherwise, the connector will fail to connect to Snowflake with an IP access denied error.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EGo back to the Linux console and execute the following commands to start the Kafka connector.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003E$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/connect-standalone.sh $HOME/snowpipe-streaming/scripts/connect-standalone.properties $HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf everything goes well, you should see something similar to screen capture below:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/snowpipe-streaming-kc.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E2. Start the producer that will ingest real-time data to the MSK cluster\u003C/h4\u003E\n","\u003Cp\u003EStart a new Linux session in \u003Ccode\u003Estep 3\u003C/code\u003E by clicking on \u003Ccode\u003Esection #2 Create a provisioned Kafka cluster and a Linux jumphost in AWS\u003C/code\u003E in the left pane.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecurl --connect-timeout 5 http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | \\\njq -c '.[]' | \\\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/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/getting-started-with-snowpipe-streaming-aws-msk/producer.png?v=baaef5e9\" 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 API returns a JSON array, so we use \u003Ccode\u003Ejq -c '.[]'\u003C/code\u003E to break it into individual JSON objects — one per Kafka message. This is required because the HP connector with schematization enabled expects each message to be a flat JSON object whose keys map to table columns.\u003C/p\u003E\n","\u003Cp\u003EThe 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/getting-started-with-snowpipe-streaming-aws-msk/flight-json.png?v=baaef5e9\" 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 switch to the role \u003Ccode\u003EMSK_STREAMING_RL\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-sh\"\u003Euse msk_streaming_db;\nuse schema msk_streaming_schema;\n\n-- Check the default pipe status (auto-created by HP connector)\nSELECT SYSTEM$PIPE_STATUS('MSK_STREAMING_DB.MSK_STREAMING_SCHEMA."MSK_STREAMING_TBL-STREAMING"');\n\n-- Show all pipes in the schema\nSHOW PIPES IN SCHEMA MSK_STREAMING_DB.MSK_STREAMING_SCHEMA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the auto-created pipe \u003Ccode\u003EMSK_STREAMING_TBL-STREAMING\u003C/code\u003E with \u003Ccode\u003EexecutionState=RUNNING\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ENote that, at this point, you should only see one batch of rows in the table, as we have only ingested data once. We will see new rows being added later as we continue to ingest more data.\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\u003EWith schematization enabled, the HP connector automatically creates columns from the JSON keys. You should see columns like \u003Ccode\u003ERECORD_METADATA\u003C/code\u003E, \u003Ccode\u003EID\u003C/code\u003E, \u003Ccode\u003EICAO\u003C/code\u003E, \u003Ccode\u003ELAT\u003C/code\u003E, \u003Ccode\u003ELON\u003C/code\u003E, \u003Ccode\u003EALT\u003C/code\u003E, \u003Ccode\u003EORIG\u003C/code\u003E, \u003Ccode\u003EDEST\u003C/code\u003E, and \u003Ccode\u003EUTC\u003C/code\u003E — no manual flattening needed.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/raw_data.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E2. Create a view with derived columns\u003C/h4\u003E\n","\u003Cp\u003ENow execute the following SQL commands to create a convenience view with timestamps and geohashes for visualization.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sh\"\u003Ecreate or replace view flights_vw\n  as select\n    to_timestamp_ntz(utc) as ts_utc,\n    CONVERT_TIMEZONE('UTC','America/Los_Angeles',ts_utc) as ts_pt,\n    alt,\n    dest,\n    orig,\n    id,\n    icao,\n    lat,\n    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\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe SQL commands create a view that converts the epoch timestamp to a proper timestamp, applies time zone conversion, and uses 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-sh\"\u003Eselect * from flights_vw;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs a result, you will see a nicely structured output with derived timestamp and geohash columns.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/materialized_view.png?v=baaef5e9\" 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\u003EGo back to the Linux session and run the following script.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sh\"\u003Ewhile true\ndo\n  curl --connect-timeout 5 -k http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | \\\n  jq -c '.[]' | \\\n  $HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming\n  sleep 10\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 10 seconds to verify that the row counts is indeed increasing.\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EConfiguring Kafka Connector with Apache Iceberg tables - Optional\u003C/h2\u003E\n","\u003Cp\u003EIceberg table ingestion requires version 3.0.0 or later of the Kafka connector and is only supported with Snowpipe Streaming.\nThe HP connector (v4.x) natively supports Iceberg tables with the same configuration used for native tables. The only additional steps are creating an external volume, pre-creating the Iceberg table, and updating the topic-to-table mapping.\u003C/p\u003E\n","\u003Ch4\u003E1. Create an External Volume in Snowflake\u003C/h4\u003E\n","\u003Cp\u003EBefore you create an Iceberg table, you must have an external volume. An external volume is a Snowflake object that stores information about your cloud storage locations and identity and access management (IAM) entities (for example, IAM roles). Snowflake uses an external volume to establish a connection with your cloud storage in order to access Iceberg metadata and Parquet data.\u003C/p\u003E\n","\u003Cp\u003ETo create an external volume in S3, follow the steps mentioned in this \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-s3?_fsi=jKSUJNzH&_fsi=jKSUJNzH\"\u003Edocument\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EBelow is an example to create External Volume below:\nWhen creating the external volume, be sure to use the ACCOUNTADMIN role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role accountadmin;\nuse schema msk_streaming_schema;\nCREATE OR REPLACE EXTERNAL VOLUME iceberg_lab_vol\n   STORAGE_LOCATIONS =\n      (\n         (\n            NAME = 'my-s3-us-west-2'\n            STORAGE_PROVIDER = 'S3'\n            STORAGE_BASE_URL = 's3://<my s3 bucket name>/'\n            STORAGE_AWS_ROLE_ARN = '<arn:aws:iam::123456789012:role//myrole>'\n            STORAGE_AWS_EXTERNAL_ID = 'iceberg_table_external_id'\n         )\n      );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter the external volume is created, use the ACCOUNTADMIN role to grant usage to the role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT ALL ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE IDENTIFIER($ROLE) WITH GRANT OPTION;\n \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E2.Pre-create a Snowflake-managed Iceberg Table for Streaming\u003C/h4\u003E\n","\u003Cp\u003ENote that if you are using a regular table to store the streaming data, Snowpipe streaming will automatically create it for you if it doesn't exist already. But for Iceberg table, you will need to pre-create an Iceberg table referencing the external volume you just created. You can specify BASE_LOCATION to instruct Snowflake where to write table data and metadata, or leave empty to write data and metadata to the location specified in the external volume definition.\u003C/p\u003E\n","\u003Cp\u003ESharing an example of the SQL to create External volumne below:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE MSK_STREAMING_ICEBERG_TBL (\n    record_metadata OBJECT()\n  )\n  EXTERNAL_VOLUME = 'iceberg_lab_vol'\n  CATALOG = 'SNOWFLAKE'\n  BASE_LOCATION = 'MSK_STREAMING_ICEBERG_TBL';\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESince the BASE_LOCATION is specified, it will create a folder based on the value in the STORAGE_BASE_URL of the External Volume.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/s3_base_location.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESince the Scehmatization is enabled in the Kafka Connector, followig privileges are set properly on the table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT EVOLVE SCHEMA ON TABLE msk_streaming_db.msk_streaming_schema.MSK_STREAMING_ICEBERG_TBL TO ROLE MSK_STREAMING_RL; \n \nALTER ICEBERG TABLE msk_streaming_db.msk_streaming_schema.MSK_STREAMING_ICEBERG_TBL set enable_schema_evolution=true;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E3. Modify the Snowpipe streaming properties file\u003C/h4\u003E\n","\u003Cp\u003EModify the two lines in \u003Ccode\u003E$HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Etopics=streaming-iceberg\nsnowflake.topic2table.map=streaming-iceberg:MSK_STREAMING_ICEBERG_TBL\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESave the properties file. Note that \u003Ccode\u003Esnowflake.enable.schematization=TRUE\u003C/code\u003E is already enabled in the HP connector properties, and the HP connector (v4.x) natively supports Iceberg tables without any additional properties.\u003C/p\u003E\n","\u003Ch4\u003E4. Create a new topic in MSK\u003C/h4\u003E\n","\u003Cp\u003EWe now need to create a new topic \u003Ccode\u003Estreaming-iceberg\u003C/code\u003E in MSK cluster by running the following command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003E$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --create --topic streaming-iceberg --partitions 2 --replication-factor 2\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E5. Restart the consumer\u003C/h4\u003E\n","\u003Cp\u003ERestart the consumer by issuing the following shell command in a new Session Manager console.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ekill -9 `ps -ef | grep java | grep -v grep | awk '{print $2}'`\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/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","\u003Ch4\u003E6. Ingest data\u003C/h4\u003E\n","\u003Cp\u003ENow ingest some data into the newly created topic by running the following command in a new Session Manager console.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecurl --connect-timeout 5 http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | \\\njq -c '.[]' | \\\nwhile read i ; \\\ndo \\\necho $i | \\\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming-iceberg ; \\\necho $i ; \\\ndone\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E7. Query ingested data in Snowflake\u003C/h4\u003E\n","\u003Cp\u003ENow, switch back to the Snowflake console and switch to the role \u003Ccode\u003EMSK_STREAMING_RL\u003C/code\u003E.\nThe data should have been streamed into a table, ready for further processing.\u003C/p\u003E\n","\u003Cp\u003ETo verify that data has been streamed into Snowflake, execute the following SQL commands.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from MSK_STREAMING_DB.MSK_STREAMING_SCHEMA.MSK_STREAMING_ICEBERG_TBL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see that the table contains the keys in json records as column names with values and RECORD_METADATA columns are populated.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/raw_data_iceberg.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n<!---------------------------->\n","\u003Ch2\u003EUse MSK Connect (MSKC) - Optional\u003C/h2\u003E\n","\u003Cp\u003ESo far we have been hosting the Kafka connector for Snowpipe Streaming on the EC2 instance. You can also use\n\u003Ca href=\"https://aws.amazon.com/msk/features/msk-connect/\"\u003EAmazon MSK Connect\u003C/a\u003E to manage the connector.\u003C/p\u003E\n","\u003Cp\u003E*Note that in order to use MSKC, it is suggested to run your MSK cluster in a private subnet with egress to the internet via the \u003Ca href=\"https://docs.aws.amazon.com/vpc/latest/userguide/vpc-nat-gateway.html\"\u003ENAT gateway\u003C/a\u003E for it to work.\nSee this \u003Ca href=\"https://docs.aws.amazon.com/msk/latest/developerguide/msk-connect-internet-access.html\"\u003EAWS documentation\u003C/a\u003E for more information.\u003C/p\u003E\n","\u003Ch4\u003E1. Create a S3 bucket to store the custom plugins\u003C/h4\u003E\n","\u003Cp\u003EFollow this \u003Ca href=\"https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-bucket.html\"\u003EAWS documentation\u003C/a\u003E to create a S3 bucket.\u003C/p\u003E\n","\u003Ch4\u003E2. Upload the libraries to the S3 bucket\u003C/h4\u003E\n","\u003Cp\u003EOn your EC2 session, run the following commands to compress the libraries into a zipped file.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-commandline\"\u003Ecd $HOME/snowpipe-streaming/kafka_2.13-3.7.2/libs\nzip -9 /tmp/snowpipeStreaming-mskc-plugins.zip *\naws s3 cp /tmp/snowpipeStreaming-mskc-plugins.zip s3://<your s3 bucket name>/snowpipeStreaming-mskc-plugins.zip\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E3. Create a custom plugin in MSK\u003C/h4\u003E\n","\u003Cp\u003EGo to the \u003Ca href=\"https://console.aws.amazon.com/msk/home\"\u003EMSK console\u003C/a\u003E, click \u003Ccode\u003ECustom plugins\u003C/code\u003E on the left pane.\nClick \u003Ccode\u003ECreate Custom plugin\u003C/code\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/custom-plugin.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFill in the s3 path to your uploaded zip file, e.g. \u003Ccode\u003Es3://my-mskc-bucket/snowpipeStreaming-mskc-plugins.zip\u003C/code\u003E\nGive the custom plugin a name, e.g. \u003Ccode\u003Emy-mskc-plugin\u003C/code\u003E, click \u003Ccode\u003ECreate custom plugin\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/custom-plugin-2.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003E4. Create a connector\u003C/h4\u003E\n","\u003Cp\u003EClick \u003Ccode\u003EConnectors\u003C/code\u003E on the left pane, then click \u003Ccode\u003ECreate connector\u003C/code\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECheck the \u003Ccode\u003EUse existing custom plugin\u003C/code\u003E button.\nSelect the custom plugin you just created, click \u003Ccode\u003ENext\u003C/code\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-2.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGive the connector a name, e.g. \u003Ccode\u003EsnowpipeStreaming\u003C/code\u003E in the \u003Ccode\u003EBasic information\u003C/code\u003E section.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-basicinfo.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect the MSK cluster you want to associate this connector with.\nScroll down to \u003Ccode\u003EConfiguration settings\u003C/code\u003E, copy and paste\nthe content from the configuration file: \u003Ccode\u003E$HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties\u003C/code\u003E in the EC2 instance.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-config.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELeave all other settings as default, further scroll down to \u003Ccode\u003EAccess permissions\u003C/code\u003E. In the \u003Ccode\u003EChoose service role\u003C/code\u003E drop-down menu, select\nthe role created by the Cloudformation template in the beginning of this quickstarts. The role name should look something like this\n\u003Ccode\u003E<CFT stack name>-MSKConnectRole-<random characters>\u003C/code\u003E. Click \u003Ccode\u003ENext\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-permission.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003ESecurity\u003C/code\u003E page, leave everything as default, click \u003Ccode\u003ENext\u003C/code\u003E.\nSkip the \u003Ccode\u003ELogs\u003C/code\u003E page as it is optional, click \u003Ccode\u003ENext\u003C/code\u003E.\nReview the configurations and click \u003Ccode\u003ECreate connector\u003C/code\u003E. The connector will be created in about 5-10 minutes.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-running.png?v=baaef5e9\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAt this point, the Kafka connector for Snowpipestreaming has been configured, it is running and managed by MSKC, all you need to do is to\nrun the source connector to ingest live data continuously as shown in Step 3 of Section 6.\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 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/getting-started-with-snowpipe-streaming-aws-msk/cleanup.png?v=baaef5e9\" 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\n-- Drop the auto-created default pipe (HP connector)\nDROP PIPE IF EXISTS MSK_STREAMING_DB.MSK_STREAMING_SCHEMA."MSK_STREAMING_TBL-STREAMING";\n\nDROP DATABASE MSK_STREAMING_DB;\nDROP WAREHOUSE MSK_STREAMING_WH;\nDROP ROLE MSK_STREAMING_RL;\nDROP USER IF EXISTS STREAMING_USER;\n\u003C/code\u003E\u003C/pre\u003E\n<!---------------------------->\n","\u003Ch2\u003EConclusion and Resources\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. 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 Kafka connectors as well.\u003C/p\u003E\n","\u003Cp\u003ERelated Resources\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/connectors/kafkahp/setup-kafka\"\u003ESnowflake High Performance (HP) Kafka Connector\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/kafka-connector-overview\"\u003ESnowflake Kafka Connector Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview\"\u003ESnowpipe Streaming Overview\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=\"/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"],"title":"Getting Started with Snowpipe Streaming and Amazon MSK","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 Snowpipe streaming:\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 using \nSnowflake's Snowpipe streaming and [MSK (Amazon Managed Streaming for Apache Kafka)](https://aws.amazon.com/msk/).\n\nNote that you can either stream the data into a regular Snowflake table or a [Snowflake managed Apache Iceberg table](https://docs.snowflake.com/en/user-guide/tables-iceberg) depending on your use case.\n\nThe 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 the Snowflake [High Performance (HP) Kafka Connector](https://docs.snowflake.com/en/connectors/kafkahp/setup-kafka) (v4.x) via [Kafka Connect](https://docs.snowflake.com/en/user-guide/kafka-connector-overview.html). The HP connector uses a server-side architecture with a PIPE object in Snowflake that manages data processing and buffering, delivering up to 10 GB/s throughput per table with 5-10 second latency.\n\n\u003E **Note:** This quickstart uses the Snowflake High Performance (HP) Kafka Connector (v4), which is currently **[GA](https://www.snowflake.com/en/blog/snowflake-kafka-connector-v4-ga/?utm_campaign=Product&utm_content=1777396741&utm_medium=Snowflake&utm_source=linkedin)**.\n\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.\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!!).\n\n\n![Architecture diagram for the Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/Overview-2-flight-arch.png?v=baaef5e9)\n\n![Data visualization](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/Overview-2-dashboarding.png?v=baaef5e9)\n\n### Prerequisites\n\n- Familiarity with Snowflake, basic SQL knowledge, Snowsight UI and Snowflake objects\n- Familiarity with AWS Services (e.g. EC2, MSK, 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. Please refer\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)\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\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  - 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. \nNote that you must have network administrator permissions to deploy these resources. \n\n### What You'll Learn\n\n- Using [MSK (Amazon Managed Streaming for Apache Kafka)](https://aws.amazon.com/msk/)\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 Snowflake to query tables populated with time-series data\n\n### What You'll Build\n\n- [Create a provisioned Kafka cluster](https://docs.aws.amazon.com/msk/latest/developerguide/msk-create-cluster.html)\n- Create Kafka producers and connectors\n- Create topics in a Kafka cluster\n- A Snowflake database for hosting real-time flight data\n\n\u003C!----------------------------\u003E\n## Create a provisioned Kafka cluster and a Linux jumphost in AWS\n\n#### 1. Create an MSK cluster and an EC2 instance\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\nthe communications between the MSK cluster and clients are secure. \n\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)\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.\n\nClick `Next` at the `Create stack` page. \nSet the Stack name or modify the default value to customize it to your identity. Leave the default Kafka version as is. For `Subnet1` and `Subnet2`, in the drop-down menu, pick two different subnets respectively, they can be either public or private subnets depending on the network layout of your VPC. Please note that if\nyou plan to use [Amazon MSK Connect](https://aws.amazon.com/msk/features/msk-connect/) later, you should choose private subnets here. \nFor `MSKSecurityGroupId`, we recommend\nusing the [default security group](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/default-custom-security-groups.html) in your VPC, if you do not have the default security group, [create one](https://docs.aws.amazon.com/vpc/latest/userguide/default-security-group.html) on your own before moving forward. Leave `TLSMutualAuthentication` as false and the jumphost instance type and AMI id as default before clicking\n`Next`. \n\nSee below sample screen capture for reference.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/msk-cft-stack-1.png?v=baaef5e9)\n\nLeave everything as default in the `Configure stack options` page and click `Next`.\nIn the `Review` page, click `Submit`.\n\nIn 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.\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 interact with the MSK cluster.\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 MSK cluster,\nClick `Session Manager` on the left pane.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/session-mgr-1.png?v=baaef5e9)\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-snowpipe-streaming-aws-msk/session-mgr-1.0.png?v=baaef5e9)\n\nClick the `Edit` button.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/session-mgr-1.1.png?v=baaef5e9)\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-snowpipe-streaming-aws-msk/session-mgr-1.1.5.png?v=baaef5e9)\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-snowpipe-streaming-aws-msk/session-mgr-1.2.png?v=baaef5e9)\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-snowpipe-streaming-aws-msk/session-mgr-2.png?v=baaef5e9)\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-snowpipe-streaming-aws-msk/session-mgr-3.png?v=baaef5e9)\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-snowpipe-streaming-aws-msk/key-pair-sessionmgr-1.png?v=baaef5e9)\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-snowpipe-streaming-aws-msk/key-pair-sessionmgr-2.png?v=baaef5e9)\n\nNext we will print out the public key string in a correct format that we can use for Snowflake.\n```\ngrep -v KEY rsa_key.pub | tr -d '\\n' | awk '{print $1}' \u003E pub.Key\ncat pub.Key\n```\nsee below example screenshot:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/key-pair-sessionmgr-3.png?v=baaef5e9)\n\n\n#### 5. Install the Kafka connector for Snowpipe streaming\n\nRun the following command to install the Kafka connector and Snowpipe streaming SDK\n\n```commandline\npasswd=changeit  # Use the default password for the Java keystore, you should change it after finishing the lab\ndirectory=/home/ssm-user/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 gzip tar jq python3-pip\nsudo amazon-linux-extras install java-openjdk11 -y\nwget https://archive.apache.org/dist/kafka/3.7.2/kafka_2.13-3.7.2.tgz\ntar xvfz kafka_2.13-3.7.2.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_2.13-3.7.2/libs/aws-msk-iam-auth-1.1.1-all.jar\nrm -rf $pwd/kafka_2.13-3.7.2.tgz\ncd /tmp && cp $(find /usr/lib/jvm -name cacerts 2\u003E/dev/null | head -1) 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 High Performance (HP) Kafka connector v4.x (Public Preview) — uses server-side Snowpipe Streaming architecture\n#v4.x is an uber-jar that bundles snowflake-ingest-sdk and snowflake-jdbc internally\nwget https://repo1.maven.org/maven2/com/snowflake/snowflake-kafka-connector/4.0.0-rc8/snowflake-kafka-connector-4.0.0-rc8.jar -O $pwd/kafka_2.13-3.7.2/libs/snowflake-kafka-connector-4.0.0-rc8.jar\n\nwget https://repo1.maven.org/maven2/org/bouncycastle/bc-fips/2.1.0/bc-fips-2.1.0.jar -O $pwd/kafka_2.13-3.7.2/libs/bc-fips-2.1.0.jar\nwget https://repo1.maven.org/maven2/org/bouncycastle/bcpkix-fips/2.1.8/bcpkix-fips-2.1.8.jar -O $pwd/kafka_2.13-3.7.2/libs/bcpkix-fips-2.1.8.jar\n\n```\nNote that the version numbers for Kafka, the Snowflake Kafka connector, and the Snowpipe Streaming SDK are dynamic, as new versions are continually published. We are using the version numbers that have been validated to work.\n\n#### 6. Retrieve the broker string from the MSK cluster.\nGo to the [MSK](https://us-west-2.console.aws.amazon.com/msk/#/clusters) console and click the newly created MSK cluster, it should have a substring `MSKCluster` in its name.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/bs-1.png?v=baaef5e9)\n\nClick `View client information`\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/bs-2.png?v=baaef5e9)\n\nWe are going to use TLS authentication between the client and brokers. Copy down the broker string under `Private endpoint` for TLS authentication type.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/bs-3.png?v=baaef5e9)\n\nNow switch back to the Session Manager window and execute the following command by replacing `\u003Cbroker string\u003E` with \nthe copied values.\n\n```commandline\nexport BS=\u003Cbroker string\u003E\n```\n\nNow run the following command to add `BS` as an environment variable so it is recognized across the Linux sessions.\n```\necho \"export BS=$BS\" \u003E\u003E ~/.bashrc\n```\nSee the following example screen capture.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/bs-4.png?v=baaef5e9)\n#### 7. Create a configuration file `connect-standalone.properties` for the Kafka connector\n\nRun the following commands to generate a configuration file `connect-standalone.properties` in directory `/home/ssm-user/snowpipe-streaming/scripts` for the client to authenticate\nwith the Kafka cluster.\n\n```commandline\ndir=/home/ssm-user/snowpipe-streaming/scripts\nmkdir -p $dir && cd $dir\ncat \u003C\u003C EOF \u003E $dir/connect-standalone.properties\n#************CREATING SNOWFLAKE Connector****************\nbootstrap.servers=$BS\n\n#************SNOWFLAKE VALUE CONVERSION****************\nkey.converter=org.apache.kafka.connect.storage.StringConverter\nvalue.converter=org.apache.kafka.connect.json.JsonConverter\nkey.converter.schemas.enable=false\nvalue.converter.schemas.enable=false\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```\nA configuration file `connect-standalone.properties` is created in directory `/home/ssm-user/snowpipe-streaming/scripts`\n\n#### 8. Create a security client.properties configuration file for the producer\n\nRun the following commands to create a security configuration file `client.properties` for the MSK cluster\n```commandline\ndir=/home/ssm-user/snowpipe-streaming/scripts\ncat \u003C\u003C EOF \u003E $dir/client.properties\nsecurity.protocol=SSL\nssl.truststore.location=/tmp/kafka.client.truststore.jks\nssl.truststore.password=changeit\nssl.enabled.protocols=TLSv1.1,TLSv1.2\nEOF\n\n```\n\nA configuration file `client.properties` is created in directory `/home/ssm-user/snowpipe-streaming/scripts`\n\n#### 9. Create a streaming topic called “streaming” in the MSK cluster\n\nNow we can run the following commands to create a Kafka topic on the MSK cluster to stream our data.\n \n```commandline\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --create --topic streaming --partitions 2 --replication-factor 2\n```\nYou should see the response `Created topic streaming` if it is successful.\n\nTo describe the topic, run the following commands:\n```commandline\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --describe --topic streaming\n```\nYou should see there are two partitions with a replication factor of 2 in the `streaming` topic.\nSee below example screenshot:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/list-kafka-topic.png?v=baaef5e9)\n\n\u003C!----------------------------\u003E\n## Prepare the Snowflake account for streaming\n\n#### 1. Creating user, role, database, and schema\nLogin to your Snowflake account as a power user with ACCOUNTADMIN role. \nThen run the following SQL commands in a worksheet to create the dedicated streaming user, role, database, schema, and warehouse that we will use in the lab. The streaming user uses key-pair authentication only (no password).\n\n```\n-- Set default value for multiple variables\n-- For purpose of this workshop, it is recommended to use these defaults during the exercise to avoid errors\n-- You should change them after the workshop\nSET USER = 'STREAMING_USER';\nSET DB = 'MSK_STREAMING_DB';\nSET SCHEMA = 'MSK_STREAMING_SCHEMA';\nSET WH = 'MSK_STREAMING_WH';\nSET ROLE = 'MSK_STREAMING_RL';\n\nUSE ROLE ACCOUNTADMIN;\n\n-- CREATE ROLE\nCREATE OR REPLACE ROLE IDENTIFIER($ROLE);\n\n-- CREATE USER (key-pair auth only, no password)\n-- Uses IF NOT EXISTS so it won't fail if the user already exists.\n-- The ALTER USER below ensures correct settings even for a pre-existing user.\nCREATE USER IF NOT EXISTS IDENTIFIER($USER)\n  DEFAULT_ROLE = $ROLE\n  DEFAULT_WAREHOUSE = $WH\n  COMMENT = 'Streaming connector user - key-pair auth only';\n\nALTER USER IF EXISTS IDENTIFIER($USER) SET\n  DEFAULT_ROLE = $ROLE\n  DEFAULT_WAREHOUSE = $WH\n  DISABLED = FALSE;\n\n-- CREATE DATABASE, SCHEMA AND WAREHOUSE\nCREATE DATABASE IF NOT EXISTS IDENTIFIER($DB);\nCREATE OR REPLACE SCHEMA IDENTIFIER($DB).IDENTIFIER($SCHEMA);\nCREATE OR REPLACE WAREHOUSE IDENTIFIER($WH) WITH WAREHOUSE_SIZE = 'SMALL';\n\n-- GRANTS\nGRANT ROLE IDENTIFIER($ROLE) TO USER IDENTIFIER($USER);\nGRANT OWNERSHIP ON DATABASE IDENTIFIER($DB) TO ROLE IDENTIFIER($ROLE) COPY CURRENT GRANTS;\nGRANT OWNERSHIP ON SCHEMA IDENTIFIER($DB).IDENTIFIER($SCHEMA) TO ROLE IDENTIFIER($ROLE) COPY CURRENT GRANTS;\nGRANT USAGE ON WAREHOUSE IDENTIFIER($WH) TO ROLE IDENTIFIER($ROLE);\nGRANT CREATE TABLE ON SCHEMA IDENTIFIER($DB).IDENTIFIER($SCHEMA) TO ROLE IDENTIFIER($ROLE);\nGRANT CREATE PIPE ON SCHEMA IDENTIFIER($DB).IDENTIFIER($SCHEMA) TO ROLE IDENTIFIER($ROLE);\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-snowpipe-streaming-aws-msk/account-identifier.png?v=baaef5e9)\n\nNext we need to configure the public key for the streaming user to access Snowflake programmatically.\n\nIn the Snowflake worksheet, replace `\u003C pubKey \u003E` with the content of the file `/home/ssm-user/pub.Key` (see `step 4` by clicking on `section #2 Create a provisioned Kafka cluster and a Linux jumphost in AWS` 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-snowpipe-streaming-aws-msk/key-pair-snowflake.png?v=baaef5e9)\n\nAt this point, the Snowflake setup is complete.\n\n\u003C!----------------------------\u003E\n## Configure Kafka connector for Snowpipe Streaming\n\n#### 1. Run the following commands to collect various connection parameters for the Kafka connector\n```commandline\ncd $HOME\noutf=/tmp/params\ncat \u003C\u003C EOF \u003E /tmp/get_params\na=''\nuntil [ ! -z \\$a ]\ndo\n read -p \"Input Snowflake account identifier: e.g. ylmxgak-wyb53646 ==\u003E \" a\ndone\n\necho export clstr_url=\\$a.snowflakecomputing.com \u003E $outf\nexport clstr_url=\\$a.snowflakecomputing.com\n\nread -p \"Snowflake user name, default: streaming_user ==\u003E \" user\nif [[ \\$user == \"\" ]]\nthen\n   user=\"streaming_user\"\nfi\n\necho export user=\\$user \u003E\u003E $outf\nexport user=\\$user\n\npass=''\nuntil [ ! -z \\$pass ]\ndo\n  read -p \"Private key passphrase ==\u003E \" pass\ndone\n\necho export key_pass=\\$pass \u003E\u003E $outf\nexport key_pass=\\$pass\n\nread -p \"Full path to your Snowflake private key file, default: /home/ssm-user/rsa_key.p8 ==\u003E \" p8\nif [[ \\$p8 == \"\" ]]\nthen\n   p8=\"/home/ssm-user/rsa_key.p8\"\nfi\n\npriv_key=\\`cat \\$p8 | grep -v PRIVATE | tr -d '\\n'\\`\necho export priv_key=\\$priv_key  \u003E\u003E $outf\nexport priv_key=\\$priv_key\ncat $outf \u003E\u003E $HOME/.bashrc\nEOF\n. /tmp/get_params\n\n```\nSee below example screen capture.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/get_params.png?v=baaef5e9)\n\n#### 2. Run the following commands to create a Snowflake Kafka connect property configuration file:\n\nNote that the High Performance (HP) connector (v4.x) will auto-create a default PIPE object named `MSK_STREAMING_TBL-STREAMING` in the target schema when the connector starts. This is why the `GRANT CREATE PIPE` was added earlier.\n\n```commandline\ndir=/home/ssm-user/snowpipe-streaming/scripts\ncat \u003C\u003C EOF \u003E $dir/snowflakeconnectorMSK.properties\nname=snowpipeStreamingHP\nconnector.class=com.snowflake.kafka.connector.SnowflakeStreamingSinkConnector\ntasks.max=4\ntopics=streaming\nsnowflake.private.key.passphrase=$key_pass\nsnowflake.database.name=MSK_STREAMING_DB\nsnowflake.schema.name=MSK_STREAMING_SCHEMA\nsnowflake.topic2table.map=streaming:MSK_STREAMING_TBL\nsnowflake.url.name=$clstr_url\nsnowflake.user.name=$user\nsnowflake.private.key=$priv_key\nsnowflake.role.name=MSK_STREAMING_RL\nsnowflake.ingestion.method=SNOWPIPE_STREAMING\nsnowflake.streaming.v2.enabled=true\nsnowflake.enable.schematization=TRUE\nbuffer.count.records=10000\nbuffer.flush.time=10\nbuffer.size.bytes=20000000\nvalue.converter.schemas.enable=false\nkey.converter=org.apache.kafka.connect.storage.StringConverter\nvalue.converter=org.apache.kafka.connect.json.JsonConverter\nerrors.tolerance=all\nerrors.log.enable=true\nEOF\n```\n\n\u003C!----------------------------\u003E\n## Putting it all together\n\nFinally, we are ready to start ingesting data into the Snowflake table.\n\n#### 1. Start the Kafka Connector for Snowpipe streaming\n\n\u003E **Important:** If your Snowflake account has a [network policy](https://docs.snowflake.com/en/user-guide/network-policies) enabled, make sure the EC2 instance's public IP (or NAT gateway IP for private subnets) is included in the allowed IP list. Otherwise, the connector will fail to connect to Snowflake with an IP access denied error.\n\nGo back to the Linux console and execute the following commands to start the Kafka connector.\n```commandline\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/connect-standalone.sh $HOME/snowpipe-streaming/scripts/connect-standalone.properties $HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties\n```\n\nIf everything goes well, you should see something similar to screen capture below:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/snowpipe-streaming-kc.png?v=baaef5e9)\n\n#### 2. Start the producer that will ingest real-time data to the MSK cluster\n\nStart a new Linux session in `step 3` by clicking on `section #2 Create a provisioned Kafka cluster and a Linux jumphost in AWS` in the left pane.\n```commandline\ncurl --connect-timeout 5 http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | \\\njq -c '.[]' | \\\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming\n```\nYou should see response similar to screen capture below if everything works well.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/producer.png?v=baaef5e9)\n\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 \nBay Area in JSON format. The API returns a JSON array, so we use `jq -c '.[]'` to break it into individual JSON objects — one per Kafka message. This is required because the HP connector with schematization enabled expects each message to be a flat JSON object whose keys map to table columns.\n\nThe 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, \nlatitude, and altitude of the aircraft, etc. The data is ingested into the `streaming` topic on the MSK cluster and \nthen picked up by the Snowpipe streaming Kafka connector, which delivers it directly into a Snowflake \ntable `msk_streaming_db.msk_streaming_schema.msk_streaming_tbl`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/flight-json.png?v=baaef5e9)\n\n\u003C!----------------------------\u003E\n## Query ingested data in Snowflake\n\nNow, switch back to the Snowflake console and switch to the role `MSK_STREAMING_RL`. \nThe data should have been streamed into a table, ready for further processing.\n\n#### 1. Query the raw data\nTo verify that data has been streamed into Snowflake, execute the following SQL commands.\n\n```sh\nuse msk_streaming_db;\nuse schema msk_streaming_schema;\n\n-- Check the default pipe status (auto-created by HP connector)\nSELECT SYSTEM$PIPE_STATUS('MSK_STREAMING_DB.MSK_STREAMING_SCHEMA.\"MSK_STREAMING_TBL-STREAMING\"');\n\n-- Show all pipes in the schema\nSHOW PIPES IN SCHEMA MSK_STREAMING_DB.MSK_STREAMING_SCHEMA;\n```\nYou should see the auto-created pipe `MSK_STREAMING_TBL-STREAMING` with `executionState=RUNNING`.\n\nNote that, at this point, you should only see one batch of rows in the table, as we have only ingested data once. We will see new rows being added later as we continue to ingest more data.\n\nNow run the following query on the table.\n```\nselect * from msk_streaming_tbl;\n```\nWith schematization enabled, the HP connector automatically creates columns from the JSON keys. You should see columns like `RECORD_METADATA`, `ID`, `ICAO`, `LAT`, `LON`, `ALT`, `ORIG`, `DEST`, and `UTC` — no manual flattening needed.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/raw_data.png?v=baaef5e9)\n\n#### 2. Create a view with derived columns\nNow execute the following SQL commands to create a convenience view with timestamps and geohashes for visualization.\n\n```sh\ncreate or replace view flights_vw\n  as select\n    to_timestamp_ntz(utc) as ts_utc,\n    CONVERT_TIMEZONE('UTC','America/Los_Angeles',ts_utc) as ts_pt,\n    alt,\n    dest,\n    orig,\n    id,\n    icao,\n    lat,\n    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```\n\nThe SQL commands create a view that converts the epoch timestamp to a proper timestamp, applies time zone conversion, and uses 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.\n\nLet's query the view `flights_vw` now.\n```sh\nselect * from flights_vw;\n```\n\nAs a result, you will see a nicely structured output with derived timestamp and geohash columns.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/materialized_view.png?v=baaef5e9)\n\n#### 3. Stream real-time flight data continuously to Snowflake\n\nWe can now write a loop to stream the flight data continuously into Snowflake.\n\nGo back to the Linux session and run the following script.\n\n```sh\nwhile true\ndo\n  curl --connect-timeout 5 -k http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | \\\n  jq -c '.[]' | \\\n  $HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming\n  sleep 10\ndone\n\n```\nYou can now go back to the Snowflake worksheet to run a `select count(1) from flights_vw` query every 10 seconds to verify that the row counts is indeed increasing.\n\n\u003C!----------------------------\u003E\n## Configuring Kafka Connector with Apache Iceberg tables - Optional\n\nIceberg table ingestion requires version 3.0.0 or later of the Kafka connector and is only supported with Snowpipe Streaming.\nThe HP connector (v4.x) natively supports Iceberg tables with the same configuration used for native tables. The only additional steps are creating an external volume, pre-creating the Iceberg table, and updating the topic-to-table mapping.\n\n#### 1. Create an External Volume in Snowflake\nBefore you create an Iceberg table, you must have an external volume. An external volume is a Snowflake object that stores information about your cloud storage locations and identity and access management (IAM) entities (for example, IAM roles). Snowflake uses an external volume to establish a connection with your cloud storage in order to access Iceberg metadata and Parquet data.\n\nTo create an external volume in S3, follow the steps mentioned in this [document](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-s3?_fsi=jKSUJNzH&_fsi=jKSUJNzH).\n\nBelow is an example to create External Volume below:\nWhen creating the external volume, be sure to use the ACCOUNTADMIN role.\n\n```sql\nuse role accountadmin;\nuse schema msk_streaming_schema;\nCREATE OR REPLACE EXTERNAL VOLUME iceberg_lab_vol\n   STORAGE_LOCATIONS =\n      (\n         (\n            NAME = 'my-s3-us-west-2'\n            STORAGE_PROVIDER = 'S3'\n            STORAGE_BASE_URL = 's3://\u003Cmy s3 bucket name\u003E/'\n            STORAGE_AWS_ROLE_ARN = '\u003Carn:aws:iam::123456789012:role//myrole\u003E'\n            STORAGE_AWS_EXTERNAL_ID = 'iceberg_table_external_id'\n         )\n      );\n```      \n\nAfter the external volume is created, use the ACCOUNTADMIN role to grant usage to the role.\n\n```sql\nGRANT ALL ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE IDENTIFIER($ROLE) WITH GRANT OPTION;\n \n```\n\n#### 2.Pre-create a Snowflake-managed Iceberg Table for Streaming\n\nNote that if you are using a regular table to store the streaming data, Snowpipe streaming will automatically create it for you if it doesn't exist already. But for Iceberg table, you will need to pre-create an Iceberg table referencing the external volume you just created. You can specify BASE_LOCATION to instruct Snowflake where to write table data and metadata, or leave empty to write data and metadata to the location specified in the external volume definition.\n\nSharing an example of the SQL to create External volumne below:\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE MSK_STREAMING_ICEBERG_TBL (\n    record_metadata OBJECT()\n  )\n  EXTERNAL_VOLUME = 'iceberg_lab_vol'\n  CATALOG = 'SNOWFLAKE'\n  BASE_LOCATION = 'MSK_STREAMING_ICEBERG_TBL';\n\n  ```\n\nSince the BASE_LOCATION is specified, it will create a folder based on the value in the STORAGE_BASE_URL of the External Volume.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/s3_base_location.png?v=baaef5e9)\n\nSince the Scehmatization is enabled in the Kafka Connector, followig privileges are set properly on the table.\n\n```sql\nGRANT EVOLVE SCHEMA ON TABLE msk_streaming_db.msk_streaming_schema.MSK_STREAMING_ICEBERG_TBL TO ROLE MSK_STREAMING_RL; \n \nALTER ICEBERG TABLE msk_streaming_db.msk_streaming_schema.MSK_STREAMING_ICEBERG_TBL set enable_schema_evolution=true;\n\n```\n\n#### 3. Modify the Snowpipe streaming properties file\n\nModify the two lines in `$HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties` \n\n```commandline\ntopics=streaming-iceberg\nsnowflake.topic2table.map=streaming-iceberg:MSK_STREAMING_ICEBERG_TBL\n```\nSave the properties file. Note that `snowflake.enable.schematization=TRUE` is already enabled in the HP connector properties, and the HP connector (v4.x) natively supports Iceberg tables without any additional properties.\n\n\n#### 4. Create a new topic in MSK\n\nWe now need to create a new topic `streaming-iceberg` in MSK cluster by running the following command:\n```commandline\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-topics.sh --bootstrap-server $BS --command-config $HOME/snowpipe-streaming/scripts/client.properties --create --topic streaming-iceberg --partitions 2 --replication-factor 2\n\n```\n\n#### 5. Restart the consumer \n\nRestart the consumer by issuing the following shell command in a new Session Manager console.\n\n```commandline\nkill -9 `ps -ef | grep java | grep -v grep | awk '{print $2}'`\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/connect-standalone.sh $HOME/snowpipe-streaming/scripts/connect-standalone.properties $HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties\n```\n\n#### 6. Ingest data \nNow ingest some data into the newly created topic by running the following command in a new Session Manager console.\n\n```commandline\ncurl --connect-timeout 5 http://ecs-alb-1504531980.us-west-2.elb.amazonaws.com:8502/opensky | \\\njq -c '.[]' | \\\nwhile read i ; \\\ndo \\\necho $i | \\\n$HOME/snowpipe-streaming/kafka_2.13-3.7.2/bin/kafka-console-producer.sh --broker-list $BS --producer.config $HOME/snowpipe-streaming/scripts/client.properties --topic streaming-iceberg ; \\\necho $i ; \\\ndone\n```\n\n#### 7. Query ingested data in Snowflake\n\nNow, switch back to the Snowflake console and switch to the role `MSK_STREAMING_RL`. \nThe data should have been streamed into a table, ready for further processing.\n\nTo verify that data has been streamed into Snowflake, execute the following SQL commands.\n\n```\nselect * from MSK_STREAMING_DB.MSK_STREAMING_SCHEMA.MSK_STREAMING_ICEBERG_TBL;\n```\nYou should see that the table contains the keys in json records as column names with values and RECORD_METADATA columns are populated.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/raw_data_iceberg.png?v=baaef5e9)\n\n\n\u003C!----------------------------\u003E\n## Use MSK Connect (MSKC) - Optional\n\nSo far we have been hosting the Kafka connector for Snowpipe Streaming on the EC2 instance. You can also use\n[Amazon MSK Connect](https://aws.amazon.com/msk/features/msk-connect/) to manage the connector.\n\n*Note that in order to use MSKC, it is suggested to run your MSK cluster in a private subnet with egress to the internet via the [NAT gateway](https://docs.aws.amazon.com/vpc/latest/userguide/vpc-nat-gateway.html) for it to work.\nSee this [AWS documentation](https://docs.aws.amazon.com/msk/latest/developerguide/msk-connect-internet-access.html) for more information.\n\n#### 1. Create a S3 bucket to store the custom plugins\nFollow this [AWS documentation](https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-bucket.html) to create a S3 bucket.\n\n#### 2. Upload the libraries to the S3 bucket\nOn your EC2 session, run the following commands to compress the libraries into a zipped file.\n\n```commandline\ncd $HOME/snowpipe-streaming/kafka_2.13-3.7.2/libs\nzip -9 /tmp/snowpipeStreaming-mskc-plugins.zip *\naws s3 cp /tmp/snowpipeStreaming-mskc-plugins.zip s3://\u003Cyour s3 bucket name\u003E/snowpipeStreaming-mskc-plugins.zip\n```\n\n#### 3. Create a custom plugin in MSK\nGo to the [MSK console](https://console.aws.amazon.com/msk/home), click `Custom plugins` on the left pane.\nClick `Create Custom plugin`.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/custom-plugin.png?v=baaef5e9)\n\nFill in the s3 path to your uploaded zip file, e.g. `s3://my-mskc-bucket/snowpipeStreaming-mskc-plugins.zip`\nGive the custom plugin a name, e.g. `my-mskc-plugin`, click `Create custom plugin`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/custom-plugin-2.png?v=baaef5e9)\n\n#### 4. Create a connector\nClick `Connectors` on the left pane, then click `Create connector`.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector.png?v=baaef5e9)\n\nCheck the `Use existing custom plugin` button.\nSelect the custom plugin you just created, click `Next`.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-2.png?v=baaef5e9)\n\nGive the connector a name, e.g. `snowpipeStreaming` in the `Basic information` section.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-basicinfo.png?v=baaef5e9)\n\nSelect the MSK cluster you want to associate this connector with.\nScroll down to `Configuration settings`, copy and paste\nthe content from the configuration file: `$HOME/snowpipe-streaming/scripts/snowflakeconnectorMSK.properties` in the EC2 instance.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-config.png?v=baaef5e9)\n\nLeave all other settings as default, further scroll down to `Access permissions`. In the `Choose service role` drop-down menu, select\nthe role created by the Cloudformation template in the beginning of this quickstarts. The role name should look something like this\n`\u003CCFT stack name\u003E-MSKConnectRole-\u003Crandom characters\u003E`. Click `Next`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-permission.png?v=baaef5e9)\n\nIn the `Security` page, leave everything as default, click `Next`.\nSkip the `Logs` page as it is optional, click `Next`.\nReview the configurations and click `Create connector`. The connector will be created in about 5-10 minutes.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/mskc-connector-running.png?v=baaef5e9)\n\nAt this point, the Kafka connector for Snowpipestreaming has been configured, it is running and managed by MSKC, all you need to do is to \nrun the source connector to ingest live data continuously as shown in Step 3 of Section 6. \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 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.\n\nSee example screen capture below.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/cleanup.png?v=baaef5e9)\n\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.\n\nFor Snowflake cleanup, execute the following SQL commands.\n\n```commandline\nUSE ROLE ACCOUNTADMIN;\n\n-- Drop the auto-created default pipe (HP connector)\nDROP PIPE IF EXISTS MSK_STREAMING_DB.MSK_STREAMING_SCHEMA.\"MSK_STREAMING_TBL-STREAMING\";\n\nDROP DATABASE MSK_STREAMING_DB;\nDROP WAREHOUSE MSK_STREAMING_WH;\nDROP ROLE MSK_STREAMING_RL;\nDROP USER IF EXISTS STREAMING_USER;\n```\n\n\u003C!----------------------------\u003E\n## Conclusion and Resources\n\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 \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 \nscalability and resilience through the AWS infrastructure. 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 Kafka connectors as well.\n\nRelated Resources\n\n- [Snowflake High Performance (HP) Kafka Connector](https://docs.snowflake.com/en/connectors/kafkahp/setup-kafka)\n- [Snowflake Kafka Connector Overview](https://docs.snowflake.com/en/user-guide/kafka-connector-overview)\n- [Snowpipe Streaming Overview](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview)\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 Marketplace](/en/data-cloud/marketplace/)\n- [Amazon Managed Streaming for Apache Kafka (MSK)](https://aws.amazon.com/msk/)\n\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"isDeveloperGuidesPage":false,"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-279fefbc5e","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-bc978e66d7",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-4bceae0172","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-30",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-a81c9875ef","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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a provisioned Kafka cluster and a Linux jumphost in AWS\u003C/h2\u003E","\u003Ch2\u003EPrepare the Snowflake account for streaming\u003C/h2\u003E","\u003Ch2\u003EConfigure Kafka connector for Snowpipe Streaming\u003C/h2\u003E","\u003Ch2\u003EPutting it all together\u003C/h2\u003E","\u003Ch2\u003EQuery ingested data in Snowflake\u003C/h2\u003E","\u003Ch2\u003EConfiguring Kafka Connector with Apache Iceberg tables - Optional\u003C/h2\u003E","\u003Ch2\u003EUse MSK Connect (MSKC) - Optional\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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