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build a real-time data pipeline that ingests IoT sensor data from Apache Kafka into Snowflake using the new Snowpipe Streaming v2 (High-Performance Architecture). You will then use Snowflake's Interactive Tables and Interactive Warehouses to achieve low-latency queries on this streaming data.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up Apache Kafka locally using Docker with KRaft (no Zookeeper required)\u003C/li\u003E\u003Cli\u003EHow to create Interactive Tables optimized for low-latency queries\u003C/li\u003E\u003Cli\u003EHow to create and configure Interactive Warehouses\u003C/li\u003E\u003Cli\u003EHow to configure the Snowflake Kafka Connector with Snowpipe Streaming v2 to stream data from Kafka into an Interactive Table\u003C/li\u003E\u003Cli\u003EHow to query streaming data with sub-second latency\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account in AWS us-west-2 region (Interactive Tables are available in select regions)\u003C/li\u003E\u003Cli\u003EDocker Desktop installed on your machine\u003C/li\u003E\u003Cli\u003EPython 3.9 or later\u003C/li\u003E\u003Cli\u003EBasic knowledge of Kafka and SQL\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA local Kafka environment with Kafka Connect\u003C/li\u003E\u003Cli\u003EA data generator that produces simulated IoT sensor data\u003C/li\u003E\u003Cli\u003EAn Interactive Table receiving real-time data via Snowpipe Streaming v2\u003C/li\u003E\u003Cli\u003EAn Interactive Warehouse optimized for low-latency queries\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EArchitecture Overview\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E┌─────────────────┐     ┌─────────────────┐     ┌─────────────────────────────────┐\n│  Python Data    │────▶│  Apache Kafka   │────▶│  Snowflake Kafka Connector      │\n│  Generator      │     │  (KRaft Mode)   │     │  (Snowpipe Streaming v2)        │\n└─────────────────┘     └─────────────────┘     └───────────────┬─────────────────┘\n                                                                │\n                                                                ▼\n                        ┌─────────────────┐     ┌─────────────────────────────────┐\n                        │  Interactive    │◀────│  Interactive Table              │\n                        │  Warehouse      │     │  (SENSOR_DATA)                  │\n                        │  (Low Latency)  │     │                                 │\n                        └─────────────────┘     └─────────────────────────────────┘\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EPrerequisites\u003C/h2\u003E\n","\u003Ch3\u003EVerify Docker Installation\u003C/h3\u003E\n","\u003Cp\u003EOpen a terminal and verify Docker is installed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker --version\ndocker compose version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Python Installation\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython3 --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInstall Required Python Packages\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install kafka-python confluent-kafka\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EClone or Download the Assets\u003C/h3\u003E\n","\u003Cp\u003EDownload the assets from this quickstart to a local directory from this \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-kafka-interactive-tables-streaming\"\u003EGitHub repository\u003C/a\u003E.\nThe assets include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Edocker-compose.yml\u003C/code\u003E - Kafka cluster configuration\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EDockerfile.connect\u003C/code\u003E - Kafka Connect with Snowflake connector\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Egenerate_sensor_data.py\u003C/code\u003E - Script to generate continuous sensor data\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esend_message.py\u003C/code\u003E - Script to send a single message\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EINTERACTIVE_TABLES_SETUP.ipynb\u003C/code\u003E - Snowflake notebook for setup\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup Snowflake Objects\u003C/h2\u003E\n","\u003Cp\u003EBefore we start the Kafka cluster, we need to create the necessary Snowflake objects and configure authentication. All SQL commands for Snowflake setup are in the \u003Cstrong\u003E\u003Ccode\u003EINTERACTIVE_TABLES_SETUP.ipynb\u003C/code\u003E\u003C/strong\u003E notebook.\u003C/p\u003E\n","\u003Ch3\u003EImport and Run the Setup Notebook\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, click on \u003Cstrong\u003EProjects\u003C/strong\u003E → \u003Cstrong\u003ENotebooks\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick the dropdown arrow next to \u003Cstrong\u003E+ Notebook\u003C/strong\u003E and select \u003Cstrong\u003EImport .ipynb file\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EUpload the \u003Ccode\u003EINTERACTIVE_TABLES_SETUP.ipynb\u003C/code\u003E file from the assets folder\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ERun on container\u003C/strong\u003E for the runtime\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat the Notebook Creates\u003C/h3\u003E\n","\u003Cp\u003EThe notebook will guide you through creating:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase and Schema\u003C/strong\u003E: \u003Ccode\u003EKAFKA_INTERACTIVE.STREAMING\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKafka Connector Role\u003C/strong\u003E: \u003Ccode\u003EKAFKA_CONNECTOR_ROLE\u003C/code\u003E with necessary privileges\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKafka User\u003C/strong\u003E: \u003Ccode\u003EKAFKA_USER\u003C/code\u003E for the Kafka connector (with RSA key pair authentication)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive Table\u003C/strong\u003E: \u003Ccode\u003ESENSOR_DATA\u003C/code\u003E with clustering on \u003Ccode\u003Edevice_id\u003C/code\u003E and \u003Ccode\u003Etimestamp\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive Warehouse\u003C/strong\u003E: \u003Ccode\u003ESENSOR_IWH\u003C/code\u003E for low-latency queries\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EGenerate RSA Key Pair\u003C/h3\u003E\n","\u003Cp\u003EBefore running the key pair authentication section of the notebook, generate an RSA key pair in your terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Generate private key (unencrypted for simplicity in this demo)\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\n\n# Generate public key\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n\n# Display the public key (you'll need this for the notebook)\ncat rsa_key.pub | grep -v "BEGIN\\|END" | tr -d '\\n'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy the public key content (without the \u003Ccode\u003E-----BEGIN PUBLIC KEY-----\u003C/code\u003E and \u003Ccode\u003E-----END PUBLIC KEY-----\u003C/code\u003E lines) and paste it into the notebook when prompted.\u003C/p\u003E\n","\u003Ch3\u003ERun Setup Cells\u003C/h3\u003E\n","\u003Cp\u003ERun the notebook cells in \u003Cstrong\u003EParts 1-7\u003C/strong\u003E to complete the Snowflake setup. The notebook includes detailed explanations for each step.\u003C/p\u003E\n","\u003Ch2\u003ESetup Local Kafka Environment\u003C/h2\u003E\n","\u003Cp\u003EThe assets folder contains the Docker configuration files needed to run Kafka locally.\u003C/p\u003E\n","\u003Ch3\u003EUnderstanding the Docker Files\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Edocker-compose.yml\u003C/code\u003E\u003C/strong\u003E - Defines two services:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Ekafka\u003C/strong\u003E: Confluent Platform Kafka 7.6.0 running in KRaft mode (no Zookeeper required). Exposes ports 9092 (internal) and 29092 (external/host access).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Ekafka-connect\u003C/strong\u003E: Kafka Connect with the Snowflake connector pre-installed. Exposes port 8083 for the REST API.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003EDockerfile.connect\u003C/code\u003E\u003C/strong\u003E - Builds a custom Kafka Connect image that includes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Kafka Connector 4.0.0-rc6\u003C/li\u003E\u003Cli\u003EBouncyCastle FIPS libraries for cryptographic operations\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrepare the Environment\u003C/h3\u003E\n","\u003Cp\u003ENavigate to the assets directory and create the required directories:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd assets\nmkdir -p keys connect-config\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECopy Your Private Key\u003C/h3\u003E\n","\u003Cp\u003ECopy the RSA private key you generated earlier to the keys directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecp rsa_key.p8 keys/\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStart Kafka Cluster\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker compose up -d\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWait for the services to be healthy:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker compose ps\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see both \u003Ccode\u003Ekafka\u003C/code\u003E and \u003Ccode\u003Ekafka-connect\u003C/code\u003E services running.\u003C/p\u003E\n","\u003Ch2\u003ECreate Kafka Topic\u003C/h2\u003E\n","\u003Ch3\u003ECreate the Sensor Data Topic\u003C/h3\u003E\n","\u003Cp\u003EUse the Kafka CLI tools inside the container to create a topic:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker exec -it kafka kafka-topics --create \\\n  --topic sensor_data \\\n  --bootstrap-server localhost:9092 \\\n  --partitions 3 \\\n  --replication-factor 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Topic Creation\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker exec -it kafka kafka-topics --describe \\\n  --topic sensor_data \\\n  --bootstrap-server localhost:9092\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see output showing the topic with 3 partitions. The output will look something like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003ETopic: sensor_data\tTopicId: h4tQAyduQm-TD6L4qyLOew\tPartitionCount: 3\tReplicationFactor: 1\tConfigs:\n\tTopic: sensor_data\tPartition: 0\tLeader: 1\tReplicas: 1\tIsr: 1\n\tTopic: sensor_data\tPartition: 1\tLeader: 1\tReplicas: 1\tIsr: 1\n\tTopic: sensor_data\tPartition: 2\tLeader: 1\tReplicas: 1\tIsr: 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConfigure Kafka Connect for Snowpipe Streaming v2\u003C/h2\u003E\n","\u003Ch3\u003ESet Environment Variables\u003C/h3\u003E\n","\u003Cp\u003ESet your Snowflake account identifier as an environment variable. This is the part before \u003Ccode\u003E.snowflakecomputing.com\u003C/code\u003E (e.g., \u003Ccode\u003Exyz12345\u003C/code\u003E or \u003Ccode\u003Esomeorg-someacctname\u003C/code\u003E):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport SNOWFLAKE_ACCOUNT="your-account-identifier"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDeploy the Snowflake Connector\u003C/h3\u003E\n","\u003Cp\u003ECreate the connector using the Kafka Connect REST API:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -X POST http://localhost:8083/connectors \\\n  -H "Content-Type: application/json" \\\n  -d '{\n    "name": "snowflake-sensor-data",\n    "config": {\n        "connector.class": "com.snowflake.kafka.connector.SnowflakeStreamingSinkConnector",\n        "tasks.max": "3",\n        "topics": "sensor_data",\n        "snowflake.url.name": "'"${SNOWFLAKE_ACCOUNT}"'.snowflakecomputing.com",\n        "snowflake.user.name": "KAFKA_USER",\n        "snowflake.private.key": "'$(grep -v "BEGIN\\|END" keys/rsa_key.p8 | tr -d '\\n')'",\n        "snowflake.database.name": "KAFKA_INTERACTIVE",\n        "snowflake.schema.name": "STREAMING",\n        "snowflake.role.name": "KAFKA_CONNECTOR_ROLE",\n        "snowflake.ingestion.method": "SNOWPIPE_STREAMING",\n        "snowflake.enable.schematization": "TRUE",\n        "snowflake.topic2table.map": "sensor_data:SENSOR_DATA",\n        "key.converter": "org.apache.kafka.connect.storage.StringConverter",\n        "value.converter": "org.apache.kafka.connect.json.JsonConverter",\n        "value.converter.schemas.enable": "false",\n        "buffer.flush.time": "10",\n        "buffer.count.records": "10000",\n        "buffer.size.bytes": "20000000",\n        "errors.tolerance": "all",\n        "errors.log.enable": "true",\n        "snowflake.streaming.enable.altering.target.pipes.tables": "false",\n        "snowflake.streaming.v2.enabled": "true"\n    }\n}'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Connector Status\u003C/h3\u003E\n","\u003Cp\u003ECheck that the connector is running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -s http://localhost:8083/connectors/snowflake-sensor-data/status | python3 -m json.tool\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see output similar to:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n    "name": "snowflake-sensor-data",\n    "connector": {\n        "state": "RUNNING",\n        "worker_id": "kafka-connect:8083"\n    },\n    "tasks": [\n        {\n            "id": 0,\n            "state": "RUNNING",\n            "worker_id": "kafka-connect:8083"\n        },\n        {\n            "id": 1,\n            "state": "RUNNING",\n            "worker_id": "kafka-connect:8083"\n        },\n        {\n            "id": 2,\n            "state": "RUNNING",\n            "worker_id": "kafka-connect:8083"\n        }\n    ],\n    "type": "sink"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll tasks should show \u003Ccode\u003E"state": "RUNNING"\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ETroubleshooting\u003C/h3\u003E\n","\u003Cp\u003EIf the connector fails, check the logs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker logs kafka-connect 2>&1 | grep -i snowflake | tail -50\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECommon issues:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAuthentication errors\u003C/strong\u003E: Verify the RSA key pair is correctly configured\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPermission errors\u003C/strong\u003E: Ensure the KAFKA_CONNECTOR_ROLE has all necessary privileges\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENetwork errors\u003C/strong\u003E: Verify your Snowflake account URL is correct\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EStart Streaming Data\u003C/h2\u003E\n","\u003Cp\u003ENow let's start generating sensor data and watch it flow into Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EPython Data Generator Scripts\u003C/h3\u003E\n","\u003Cp\u003EThe assets include two Python scripts for sending data to Kafka:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Esend_message.py\u003C/code\u003E\u003C/strong\u003E - Sends a single sensor reading to Kafka. Accepts a JSON string as a command-line argument.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Egenerate_sensor_data.py\u003C/code\u003E\u003C/strong\u003E - Generates random IoT sensor data continuously at a configurable rate. Simulates 20 devices across 5 buildings with 5 sensor types (temperature, humidity, pressure, CO2, and light).\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ETest with a Single Message\u003C/h3\u003E\n","\u003Cp\u003EFirst, send a single sensor reading to verify end-to-end connectivity:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython send_message.py '{"device_id": "DEVICE_001", "sensor_type": "temperature", "value": 23.5, "unit": "celsius", "timestamp": "2024-01-15T10:30:00Z", "location": {"building": "HQ", "floor": 2, "zone": "A"}}'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see output confirming the message was sent:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EMessage sent successfully!\n  Topic: sensor_data\n  Partition: 1\n  Offset: 0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Data in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EGo back to the \u003Cstrong\u003E\u003Ccode\u003EINTERACTIVE_TABLES_SETUP.ipynb\u003C/code\u003E\u003C/strong\u003E notebook in Snowsight and run the cells in \u003Cstrong\u003EPart 8: Querying Streaming Data\u003C/strong\u003E. The first query should show your test message:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE SENSOR_IWH;\n\nSELECT * FROM SENSOR_DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see one row with your test data (it may take 5-10 seconds for the data to appear due to Snowpipe Streaming latency).\u003C/p\u003E\n","\u003Ch3\u003EGenerate Continuous Data\u003C/h3\u003E\n","\u003Cp\u003ENow start the data generator to send continuous sensor data. The script accepts the following arguments:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003E--duration, -d\u003C/code\u003E: Duration to run in minutes (default: 1)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E--rate, -r\u003C/code\u003E: Messages per second (default: 10)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EStart generating 10 messages per second for 2 minutes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython generate_sensor_data.py --duration 2 --rate 10\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou'll see output like:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EStarting data generation:\n  Duration: 2.0 minute(s)\n  Rate: 10 messages/second\n  Topic: sensor_data\nPress Ctrl+C to stop early\n\nMessages sent: 1,200 | Elapsed: 120.0s | Rate: 10.0 msg/s\n\nGeneration complete!\n  Total messages sent: 1,200\n  Total time: 120.0 seconds\n  Average rate: 10.0 messages/second\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMonitor Data Flow\u003C/h3\u003E\n","\u003Cp\u003EWhile the generator is running, you can monitor:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKafka topic offsets:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker exec -it kafka kafka-consumer-groups \\\n  --bootstrap-server localhost:9092 \\\n  --describe \\\n  --group connect-snowflake-sensor-data\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGROUP                          TOPIC           PARTITION  CURRENT-OFFSET  LOG-END-OFFSET  LAG\nconnect-snowflake-sensor-data  sensor_data     0          412             412             0\nconnect-snowflake-sensor-data  sensor_data     1          398             398             0\nconnect-snowflake-sensor-data  sensor_data     2          390             390             0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConnector status:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -s http://localhost:8083/connectors/snowflake-sensor-data/status | python3 -m json.tool\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n    "name": "snowflake-sensor-data",\n    "connector": {\n        "state": "RUNNING",\n        "worker_id": "kafka-connect:8083"\n    },\n    "tasks": [\n        {"id": 0, "state": "RUNNING", "worker_id": "kafka-connect:8083"},\n        {"id": 1, "state": "RUNNING", "worker_id": "kafka-connect:8083"},\n        {"id": 2, "state": "RUNNING", "worker_id": "kafka-connect:8083"}\n    ],\n    "type": "sink"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EQuery Data with Interactive Warehouse\u003C/h2\u003E\n","\u003Cp\u003ENow let's query the data using the Interactive Warehouse for low-latency results.\u003C/p\u003E\n","\u003Ch3\u003ESwitch to Interactive Warehouse\u003C/h3\u003E\n","\u003Cp\u003EIn Snowsight or the notebook, run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE DATABASE KAFKA_INTERACTIVE;\nUSE SCHEMA STREAMING;\n\n-- Switch to the Interactive Warehouse\nUSE WAREHOUSE SENSOR_IWH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EQuery Recent Sensor Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Get the most recent readings\nSELECT \n    device_id,\n    sensor_type,\n    value,\n    unit,\n    timestamp,\n    location:building::STRING as building,\n    location:floor::INTEGER as floor,\n    location:zone::STRING as zone\nFROM SENSOR_DATA\nWHERE timestamp >= DATEADD(minute, -5, CURRENT_TIMESTAMP())\nORDER BY timestamp DESC\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAggregate Queries\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Average readings by sensor type in last minute\nSELECT \n    sensor_type,\n    COUNT(*) as reading_count,\n    AVG(value) as avg_value,\n    MIN(value) as min_value,\n    MAX(value) as max_value\nFROM SENSOR_DATA\nWHERE timestamp >= DATEADD(minute, -1, CURRENT_TIMESTAMP())\nGROUP BY sensor_type\nORDER BY reading_count DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDevice Activity Dashboard Query\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Active devices in last 30 seconds\nSELECT \n    device_id,\n    COUNT(*) as readings,\n    MAX(timestamp) as last_reading\nFROM SENSOR_DATA\nWHERE timestamp >= DATEADD(second, -30, CURRENT_TIMESTAMP())\nGROUP BY device_id\nORDER BY readings DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECompare Query Performance\u003C/h3\u003E\n","\u003Cp\u003ENotice how queries on the Interactive Warehouse complete quickly (typically under 1 second) even while data is actively streaming in.\u003C/p\u003E\n","\u003Ch2\u003EMonitor and Observe\u003C/h2\u003E\n","\u003Ch3\u003EView Streaming Channel Status\u003C/h3\u003E\n","\u003Cp\u003EIn Snowflake, you can monitor the Snowpipe Streaming channels:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW CHANNELS IN SCHEMA KAFKA_INTERACTIVE.STREAMING;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECheck Default Pipe\u003C/h3\u003E\n","\u003Cp\u003EWith Snowpipe Streaming v2, a default pipe is automatically created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW PIPES IN SCHEMA KAFKA_INTERACTIVE.STREAMING;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EView Recent Ingestion Metrics\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT *\nFROM TABLE(INFORMATION_SCHEMA.PIPE_USAGE_HISTORY(\n    DATE_RANGE_START => DATEADD(hour, -1, CURRENT_TIMESTAMP()),\n    DATE_RANGE_END => CURRENT_TIMESTAMP()\n));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMonitor Table Growth\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check table size and row count\nSELECT \n    TABLE_NAME,\n    ROW_COUNT,\n    BYTES,\n    LAST_ALTERED\nFROM INFORMATION_SCHEMA.TABLES\nWHERE TABLE_NAME = 'SENSOR_DATA';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EWhen you're done with the demo, clean up the resources.\u003C/p\u003E\n","\u003Ch3\u003EStop the Data Generator\u003C/h3\u003E\n","\u003Cp\u003EPress \u003Ccode\u003ECtrl+C\u003C/code\u003E in the terminal where the generator is running.\u003C/p\u003E\n","\u003Ch3\u003ERemove Kafka Connector\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -X DELETE http://localhost:8083/connectors/snowflake-sensor-data\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStop Kafka Cluster\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker compose down -v\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EClean Up Snowflake Objects\u003C/h3\u003E\n","\u003Cp\u003EGo back to the \u003Cstrong\u003E\u003Ccode\u003EINTERACTIVE_TABLES_SETUP.ipynb\u003C/code\u003E\u003C/strong\u003E notebook in Snowsight and run the cleanup cells in \u003Cstrong\u003EPart 9: Cleanup\u003C/strong\u003E. This will drop all the Snowflake objects created during the demo.\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built a real-time data pipeline using:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EApache Kafka\u003C/strong\u003E with KRaft mode (no Zookeeper)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Kafka Connector\u003C/strong\u003E with Snowpipe Streaming v2\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive Tables\u003C/strong\u003E optimized for low-latency queries\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive Warehouses\u003C/strong\u003E providing sub-second query response\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESetting up Kafka locally with Docker using the modern KRaft consensus protocol\u003C/li\u003E\u003Cli\u003EConfiguring the Snowflake Kafka Connector for high-performance streaming\u003C/li\u003E\u003Cli\u003ECreating and configuring Interactive Tables and Interactive Warehouses\u003C/li\u003E\u003Cli\u003EBuilding Python data generators for IoT simulation\u003C/li\u003E\u003Cli\u003EQuerying streaming data with consistent low latency\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Takeaways\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESnowpipe Streaming v2\u003C/strong\u003E provides near real-time data ingestion (5-10 second latency)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive Tables\u003C/strong\u003E are optimized for simple, selective queries with low latency\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive Warehouses\u003C/strong\u003E never auto-suspend and provide consistent performance\u003C/li\u003E\u003Cli\u003EThe combination enables real-time dashboards and operational analytics\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/interactive\"\u003EInteractive Tables Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowpipe-streaming/snowpipe-streaming-high-performance-overview\"\u003ESnowpipe Streaming High-Performance Architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/kafka-connector\"\u003ESnowflake Kafka Connector\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://kafka.apache.org/documentation/\"\u003EApache Kafka Documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Learn how to use Snowflake's Interactive Tables and Interactive Warehouses with real-time data from Kafka using Snowpipe Streaming v2.","title":"Real-Time Analytics with Kafka, Interactive Tables, and Snowpipe Streaming v2","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nIn this quickstart, you will learn how to build a real-time data pipeline that ingests IoT sensor data from Apache Kafka into Snowflake using the new Snowpipe Streaming v2 (High-Performance Architecture). You will then use Snowflake's Interactive Tables and Interactive Warehouses to achieve low-latency queries on this streaming data.\n\n### What You'll Learn\n- How to set up Apache Kafka locally using Docker with KRaft (no Zookeeper required)\n- How to create Interactive Tables optimized for low-latency queries\n- How to create and configure Interactive Warehouses\n- How to configure the Snowflake Kafka Connector with Snowpipe Streaming v2 to stream data from Kafka into an Interactive Table\n- How to query streaming data with sub-second latency\n\n### What You'll Need\n- A Snowflake account in AWS us-west-2 region (Interactive Tables are available in select regions)\n- Docker Desktop installed on your machine\n- Python 3.9 or later\n- Basic knowledge of Kafka and SQL\n\n### What You'll Build\n- A local Kafka environment with Kafka Connect\n- A data generator that produces simulated IoT sensor data\n- An Interactive Table receiving real-time data via Snowpipe Streaming v2\n- An Interactive Warehouse optimized for low-latency queries\n\n### Architecture Overview\n\n```\n┌─────────────────┐     ┌─────────────────┐     ┌─────────────────────────────────┐\n│  Python Data    │────▶│  Apache Kafka   │────▶│  Snowflake Kafka Connector      │\n│  Generator      │     │  (KRaft Mode)   │     │  (Snowpipe Streaming v2)        │\n└─────────────────┘     └─────────────────┘     └───────────────┬─────────────────┘\n                                                                │\n                                                                ▼\n                        ┌─────────────────┐     ┌─────────────────────────────────┐\n                        │  Interactive    │◀────│  Interactive Table              │\n                        │  Warehouse      │     │  (SENSOR_DATA)                  │\n                        │  (Low Latency)  │     │                                 │\n                        └─────────────────┘     └─────────────────────────────────┘\n```\n\n## Prerequisites\n\n### Verify Docker Installation\n\nOpen a terminal and verify Docker is installed:\n\n```bash\ndocker --version\ndocker compose version\n```\n\n### Verify Python Installation\n\n```bash\npython3 --version\n```\n\n### Install Required Python Packages\n\n```bash\npip install kafka-python confluent-kafka\n```\n\n### Clone or Download the Assets\n\nDownload the assets from this quickstart to a local directory from this [GitHub repository](https://github.com/Snowflake-Labs/sfguide-kafka-interactive-tables-streaming). \nThe assets include:\n- `docker-compose.yml` - Kafka cluster configuration\n- `Dockerfile.connect` - Kafka Connect with Snowflake connector\n- `generate_sensor_data.py` - Script to generate continuous sensor data\n- `send_message.py` - Script to send a single message\n- `INTERACTIVE_TABLES_SETUP.ipynb` - Snowflake notebook for setup\n\n## Setup Snowflake Objects\n\nBefore we start the Kafka cluster, we need to create the necessary Snowflake objects and configure authentication. All SQL commands for Snowflake setup are in the **`INTERACTIVE_TABLES_SETUP.ipynb`** notebook.\n\n### Import and Run the Setup Notebook\n\n1. In Snowsight, click on **Projects** → **Notebooks**\n2. Click the dropdown arrow next to **+ Notebook** and select **Import .ipynb file**\n3. Upload the `INTERACTIVE_TABLES_SETUP.ipynb` file from the assets folder\n4. Select **Run on container** for the runtime\n5. Click **Create**\n\n### What the Notebook Creates\n\nThe notebook will guide you through creating:\n\n- **Database and Schema**: `KAFKA_INTERACTIVE.STREAMING`\n- **Kafka Connector Role**: `KAFKA_CONNECTOR_ROLE` with necessary privileges\n- **Kafka User**: `KAFKA_USER` for the Kafka connector (with RSA key pair authentication)\n- **Interactive Table**: `SENSOR_DATA` with clustering on `device_id` and `timestamp`\n- **Interactive Warehouse**: `SENSOR_IWH` for low-latency queries\n\n### Generate RSA Key Pair\n\nBefore running the key pair authentication section of the notebook, generate an RSA key pair in your terminal:\n\n```bash\n# Generate private key (unencrypted for simplicity in this demo)\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\n\n# Generate public key\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n\n# Display the public key (you'll need this for the notebook)\ncat rsa_key.pub | grep -v \"BEGIN\\|END\" | tr -d '\\n'\n```\n\nCopy the public key content (without the `-----BEGIN PUBLIC KEY-----` and `-----END PUBLIC KEY-----` lines) and paste it into the notebook when prompted.\n\n### Run Setup Cells\n\nRun the notebook cells in **Parts 1-7** to complete the Snowflake setup. The notebook includes detailed explanations for each step.\n\n## Setup Local Kafka Environment\n\nThe assets folder contains the Docker configuration files needed to run Kafka locally.\n\n### Understanding the Docker Files\n\n**`docker-compose.yml`** - Defines two services:\n- **kafka**: Confluent Platform Kafka 7.6.0 running in KRaft mode (no Zookeeper required). Exposes ports 9092 (internal) and 29092 (external/host access).\n- **kafka-connect**: Kafka Connect with the Snowflake connector pre-installed. Exposes port 8083 for the REST API.\n\n**`Dockerfile.connect`** - Builds a custom Kafka Connect image that includes:\n- Snowflake Kafka Connector 4.0.0-rc6\n- BouncyCastle FIPS libraries for cryptographic operations\n\n### Prepare the Environment\n\nNavigate to the assets directory and create the required directories:\n\n```bash\ncd assets\nmkdir -p keys connect-config\n```\n\n### Copy Your Private Key\n\nCopy the RSA private key you generated earlier to the keys directory:\n\n```bash\ncp rsa_key.p8 keys/\n```\n\n### Start Kafka Cluster\n\n```bash\ndocker compose up -d\n```\n\nWait for the services to be healthy:\n\n```bash\ndocker compose ps\n```\n\nYou should see both `kafka` and `kafka-connect` services running.\n\n## Create Kafka Topic\n\n### Create the Sensor Data Topic\n\nUse the Kafka CLI tools inside the container to create a topic:\n\n```bash\ndocker exec -it kafka kafka-topics --create \\\n  --topic sensor_data \\\n  --bootstrap-server localhost:9092 \\\n  --partitions 3 \\\n  --replication-factor 1\n```\n\n### Verify Topic Creation\n\n```bash\ndocker exec -it kafka kafka-topics --describe \\\n  --topic sensor_data \\\n  --bootstrap-server localhost:9092\n```\n\nYou should see output showing the topic with 3 partitions. The output will look something like this:\n```bash\nTopic: sensor_data\tTopicId: h4tQAyduQm-TD6L4qyLOew\tPartitionCount: 3\tReplicationFactor: 1\tConfigs:\n\tTopic: sensor_data\tPartition: 0\tLeader: 1\tReplicas: 1\tIsr: 1\n\tTopic: sensor_data\tPartition: 1\tLeader: 1\tReplicas: 1\tIsr: 1\n\tTopic: sensor_data\tPartition: 2\tLeader: 1\tReplicas: 1\tIsr: 1\n```\n\n## Configure Kafka Connect for Snowpipe Streaming v2\n\n### Set Environment Variables\n\nSet your Snowflake account identifier as an environment variable. This is the part before `.snowflakecomputing.com` (e.g., `xyz12345` or `someorg-someacctname`):\n\n```bash\nexport SNOWFLAKE_ACCOUNT=\"your-account-identifier\"\n```\n\n### Deploy the Snowflake Connector\n\nCreate the connector using the Kafka Connect REST API:\n\n```bash\ncurl -X POST http://localhost:8083/connectors \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"name\": \"snowflake-sensor-data\",\n    \"config\": {\n        \"connector.class\": \"com.snowflake.kafka.connector.SnowflakeStreamingSinkConnector\",\n        \"tasks.max\": \"3\",\n        \"topics\": \"sensor_data\",\n        \"snowflake.url.name\": \"'\"${SNOWFLAKE_ACCOUNT}\"'.snowflakecomputing.com\",\n        \"snowflake.user.name\": \"KAFKA_USER\",\n        \"snowflake.private.key\": \"'$(grep -v \"BEGIN\\|END\" keys/rsa_key.p8 | tr -d '\\n')'\",\n        \"snowflake.database.name\": \"KAFKA_INTERACTIVE\",\n        \"snowflake.schema.name\": \"STREAMING\",\n        \"snowflake.role.name\": \"KAFKA_CONNECTOR_ROLE\",\n        \"snowflake.ingestion.method\": \"SNOWPIPE_STREAMING\",\n        \"snowflake.enable.schematization\": \"TRUE\",\n        \"snowflake.topic2table.map\": \"sensor_data:SENSOR_DATA\",\n        \"key.converter\": \"org.apache.kafka.connect.storage.StringConverter\",\n        \"value.converter\": \"org.apache.kafka.connect.json.JsonConverter\",\n        \"value.converter.schemas.enable\": \"false\",\n        \"buffer.flush.time\": \"10\",\n        \"buffer.count.records\": \"10000\",\n        \"buffer.size.bytes\": \"20000000\",\n        \"errors.tolerance\": \"all\",\n        \"errors.log.enable\": \"true\",\n        \"snowflake.streaming.enable.altering.target.pipes.tables\": \"false\",\n        \"snowflake.streaming.v2.enabled\": \"true\"\n    }\n}'\n```\n\n### Verify Connector Status\n\nCheck that the connector is running:\n\n```bash\ncurl -s http://localhost:8083/connectors/snowflake-sensor-data/status | python3 -m json.tool\n```\n\nYou should see output similar to:\n\n```json\n{\n    \"name\": \"snowflake-sensor-data\",\n    \"connector\": {\n        \"state\": \"RUNNING\",\n        \"worker_id\": \"kafka-connect:8083\"\n    },\n    \"tasks\": [\n        {\n            \"id\": 0,\n            \"state\": \"RUNNING\",\n            \"worker_id\": \"kafka-connect:8083\"\n        },\n        {\n            \"id\": 1,\n            \"state\": \"RUNNING\",\n            \"worker_id\": \"kafka-connect:8083\"\n        },\n        {\n            \"id\": 2,\n            \"state\": \"RUNNING\",\n            \"worker_id\": \"kafka-connect:8083\"\n        }\n    ],\n    \"type\": \"sink\"\n}\n```\n\nAll tasks should show `\"state\": \"RUNNING\"`.\n\n### Troubleshooting\n\nIf the connector fails, check the logs:\n\n```bash\ndocker logs kafka-connect 2\u003E&1 | grep -i snowflake | tail -50\n```\n\nCommon issues:\n- **Authentication errors**: Verify the RSA key pair is correctly configured\n- **Permission errors**: Ensure the KAFKA_CONNECTOR_ROLE has all necessary privileges\n- **Network errors**: Verify your Snowflake account URL is correct\n\n## Start Streaming Data\n\nNow let's start generating sensor data and watch it flow into Snowflake.\n\n### Python Data Generator Scripts\n\nThe assets include two Python scripts for sending data to Kafka:\n\n- **`send_message.py`** - Sends a single sensor reading to Kafka. Accepts a JSON string as a command-line argument.\n- **`generate_sensor_data.py`** - Generates random IoT sensor data continuously at a configurable rate. Simulates 20 devices across 5 buildings with 5 sensor types (temperature, humidity, pressure, CO2, and light).\n\n### Test with a Single Message\n\nFirst, send a single sensor reading to verify end-to-end connectivity:\n\n```bash\npython send_message.py '{\"device_id\": \"DEVICE_001\", \"sensor_type\": \"temperature\", \"value\": 23.5, \"unit\": \"celsius\", \"timestamp\": \"2024-01-15T10:30:00Z\", \"location\": {\"building\": \"HQ\", \"floor\": 2, \"zone\": \"A\"}}'\n```\n\nYou should see output confirming the message was sent:\n\n```\nMessage sent successfully!\n  Topic: sensor_data\n  Partition: 1\n  Offset: 0\n```\n\n### Verify Data in Snowflake\n\nGo back to the **`INTERACTIVE_TABLES_SETUP.ipynb`** notebook in Snowsight and run the cells in **Part 8: Querying Streaming Data**. The first query should show your test message:\n\n```sql\nUSE WAREHOUSE SENSOR_IWH;\n\nSELECT * FROM SENSOR_DATA;\n```\n\nYou should see one row with your test data (it may take 5-10 seconds for the data to appear due to Snowpipe Streaming latency).\n\n### Generate Continuous Data\n\nNow start the data generator to send continuous sensor data. The script accepts the following arguments:\n\n- `--duration, -d`: Duration to run in minutes (default: 1)\n- `--rate, -r`: Messages per second (default: 10)\n\nStart generating 10 messages per second for 2 minutes:\n\n```bash\npython generate_sensor_data.py --duration 2 --rate 10\n```\n\nYou'll see output like:\n\n```\nStarting data generation:\n  Duration: 2.0 minute(s)\n  Rate: 10 messages/second\n  Topic: sensor_data\nPress Ctrl+C to stop early\n\nMessages sent: 1,200 | Elapsed: 120.0s | Rate: 10.0 msg/s\n\nGeneration complete!\n  Total messages sent: 1,200\n  Total time: 120.0 seconds\n  Average rate: 10.0 messages/second\n```\n\n### Monitor Data Flow\n\nWhile the generator is running, you can monitor:\n\n**Kafka topic offsets:**\n```bash\ndocker exec -it kafka kafka-consumer-groups \\\n  --bootstrap-server localhost:9092 \\\n  --describe \\\n  --group connect-snowflake-sensor-data\n```\n\nExample output:\n```\nGROUP                          TOPIC           PARTITION  CURRENT-OFFSET  LOG-END-OFFSET  LAG\nconnect-snowflake-sensor-data  sensor_data     0          412             412             0\nconnect-snowflake-sensor-data  sensor_data     1          398             398             0\nconnect-snowflake-sensor-data  sensor_data     2          390             390             0\n```\n\n**Connector status:**\n```bash\ncurl -s http://localhost:8083/connectors/snowflake-sensor-data/status | python3 -m json.tool\n```\n\nExample output:\n```json\n{\n    \"name\": \"snowflake-sensor-data\",\n    \"connector\": {\n        \"state\": \"RUNNING\",\n        \"worker_id\": \"kafka-connect:8083\"\n    },\n    \"tasks\": [\n        {\"id\": 0, \"state\": \"RUNNING\", \"worker_id\": \"kafka-connect:8083\"},\n        {\"id\": 1, \"state\": \"RUNNING\", \"worker_id\": \"kafka-connect:8083\"},\n        {\"id\": 2, \"state\": \"RUNNING\", \"worker_id\": \"kafka-connect:8083\"}\n    ],\n    \"type\": \"sink\"\n}\n```\n\n## Query Data with Interactive Warehouse\n\nNow let's query the data using the Interactive Warehouse for low-latency results.\n\n### Switch to Interactive Warehouse\n\nIn Snowsight or the notebook, run:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE DATABASE KAFKA_INTERACTIVE;\nUSE SCHEMA STREAMING;\n\n-- Switch to the Interactive Warehouse\nUSE WAREHOUSE SENSOR_IWH;\n```\n\n### Query Recent Sensor Data\n\n```sql\n-- Get the most recent readings\nSELECT \n    device_id,\n    sensor_type,\n    value,\n    unit,\n    timestamp,\n    location:building::STRING as building,\n    location:floor::INTEGER as floor,\n    location:zone::STRING as zone\nFROM SENSOR_DATA\nWHERE timestamp \u003E= DATEADD(minute, -5, CURRENT_TIMESTAMP())\nORDER BY timestamp DESC\nLIMIT 100;\n```\n\n### Aggregate Queries\n\n```sql\n-- Average readings by sensor type in last minute\nSELECT \n    sensor_type,\n    COUNT(*) as reading_count,\n    AVG(value) as avg_value,\n    MIN(value) as min_value,\n    MAX(value) as max_value\nFROM SENSOR_DATA\nWHERE timestamp \u003E= DATEADD(minute, -1, CURRENT_TIMESTAMP())\nGROUP BY sensor_type\nORDER BY reading_count DESC;\n```\n\n### Device Activity Dashboard Query\n\n```sql\n-- Active devices in last 30 seconds\nSELECT \n    device_id,\n    COUNT(*) as readings,\n    MAX(timestamp) as last_reading\nFROM SENSOR_DATA\nWHERE timestamp \u003E= DATEADD(second, -30, CURRENT_TIMESTAMP())\nGROUP BY device_id\nORDER BY readings DESC;\n```\n\n### Compare Query Performance\n\nNotice how queries on the Interactive Warehouse complete quickly (typically under 1 second) even while data is actively streaming in.\n\n## Monitor and Observe\n\n### View Streaming Channel Status\n\nIn Snowflake, you can monitor the Snowpipe Streaming channels:\n\n```sql\nSHOW CHANNELS IN SCHEMA KAFKA_INTERACTIVE.STREAMING;\n```\n\n### Check Default Pipe\n\nWith Snowpipe Streaming v2, a default pipe is automatically created:\n\n```sql\nSHOW PIPES IN SCHEMA KAFKA_INTERACTIVE.STREAMING;\n```\n\n### View Recent Ingestion Metrics\n\n```sql\nSELECT *\nFROM TABLE(INFORMATION_SCHEMA.PIPE_USAGE_HISTORY(\n    DATE_RANGE_START =\u003E DATEADD(hour, -1, CURRENT_TIMESTAMP()),\n    DATE_RANGE_END =\u003E CURRENT_TIMESTAMP()\n));\n```\n\n### Monitor Table Growth\n\n```sql\n-- Check table size and row count\nSELECT \n    TABLE_NAME,\n    ROW_COUNT,\n    BYTES,\n    LAST_ALTERED\nFROM INFORMATION_SCHEMA.TABLES\nWHERE TABLE_NAME = 'SENSOR_DATA';\n```\n\n## Cleanup\n\nWhen you're done with the demo, clean up the resources.\n\n### Stop the Data Generator\n\nPress `Ctrl+C` in the terminal where the generator is running.\n\n### Remove Kafka Connector\n\n```bash\ncurl -X DELETE http://localhost:8083/connectors/snowflake-sensor-data\n```\n\n### Stop Kafka Cluster\n\n```bash\ndocker compose down -v\n```\n\n### Clean Up Snowflake Objects\n\nGo back to the **`INTERACTIVE_TABLES_SETUP.ipynb`** notebook in Snowsight and run the cleanup cells in **Part 9: Cleanup**. This will drop all the Snowflake objects created during the demo.\n\n## Conclusion and Resources\n\nCongratulations! You've successfully built a real-time data pipeline using:\n\n- **Apache Kafka** with KRaft mode (no Zookeeper)\n- **Snowflake Kafka Connector** with Snowpipe Streaming v2\n- **Interactive Tables** optimized for low-latency queries\n- **Interactive Warehouses** providing sub-second query response\n\n### What You Learned\n\n- Setting up Kafka locally with Docker using the modern KRaft consensus protocol\n- Configuring the Snowflake Kafka Connector for high-performance streaming\n- Creating and configuring Interactive Tables and Interactive Warehouses\n- Building Python data generators for IoT simulation\n- Querying streaming data with consistent low latency\n\n### Key Takeaways\n\n1. **Snowpipe Streaming v2** provides near real-time data ingestion (5-10 second latency)\n2. **Interactive Tables** are optimized for simple, selective queries with low latency\n3. **Interactive Warehouses** never auto-suspend and provide consistent performance\n4. The combination enables real-time dashboards and operational analytics\n\n### Resources\n\n- [Interactive Tables Documentation](https://docs.snowflake.com/en/user-guide/interactive)\n- [Snowpipe Streaming High-Performance Architecture](https://docs.snowflake.com/en/user-guide/snowpipe-streaming/snowpipe-streaming-high-performance-overview)\n- [Snowflake Kafka Connector](https://docs.snowflake.com/en/user-guide/kafka-connector)\n- [Apache Kafka Documentation](https://kafka.apache.org/documentation/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-3cb079578e","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-0dbc9360da",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ec2c14f241","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-01-16",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-c040318f6e","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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