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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-e0528a5cd2","isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Bidirectional Data Pipelines with pg_lake and Snowflake - External Stage"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Brian 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------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Ch3\u003EWhat is pg_lake?\u003C/h3\u003E\n","\u003Cp\u003EPostgreSQL (Postgres) extensions are loadable modules that add new functionality to the database—such as data types, functions, operators, and storage engines—without modifying the core Postgres code. For more details, see the \u003Ca href=\"https://www.postgresql.org/docs/current/extend-extensions.html\"\u003EPostgres Extensions documentation\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/pg_lake\"\u003Epg_lake\u003C/a\u003E is a powerful Postgres extension available in Snowflake Postgres that enables direct reading and writing of data lake files stored in cloud object storage like Amazon S3. With pg_lake, you can export data from Postgres tables directly to S3 in various formats (CSV, Parquet, JSON), query files in S3 using foreign tables without importing the data, and create Iceberg tables for open table format storage.\u003C/p\u003E\n","\u003Cp\u003EWhen combined with Snowflake's data platform, pg_lake enables simple bidirectional data pipelines that leverage the operational strengths of Postgres alongside Snowflake's powerful analytics and AI capabilities.\u003C/p\u003E\n","\u003Cp\u003EIn this quick start guide, we use S3 as the example, however, you can also use Azure object storage as well.  For more details see the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-storage-integration\"\u003EStorage Integration documentation\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EExternal Stage vs. Internal Stage\u003C/h3\u003E\n","\u003Cp\u003Epg_lake supports two storage options for exchanging data between Postgres and Snowflake:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E \u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExternal Stage\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EInternal Stage\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStorage location\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer-provided object storage (e.g. Amazon S3, Azure Blob)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake-managed internal object storage\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConfiguration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERequires IAM roles, trust policies, and storage integrations for both Postgres and Snowflake\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMinimal setup — automatically provisioned with the Postgres instance\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData residency\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData resides in the customer's cloud account\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData never leaves the Snowflake security perimeter\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUse when\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYou need to share data with systems outside of Snowflake, retain data in your own storage, or integrate with existing data lake architectures\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYou want the simplest setup, maximum security, and data exchange is exclusively between Snowflake Postgres and Snowflake\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThis quickstart uses the \u003Cstrong\u003Eexternal stage\u003C/strong\u003E approach with customer-provided S3 storage. For the internal stage version where data never leaves Snowflake, see \u003Ca href=\"en/developers/guides/snowflake-postgres-pg-lake-iot-internal-stage/\"\u003EBidirectional Data Pipelines with pg_lake and Snowflake - Internal Stage\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EUse Case: IoT Sensor Monitoring\u003C/h3\u003E\n","\u003Cp\u003EThis quickstart demonstrates a practical IoT sensor monitoring scenario where:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPostgres\u003C/strong\u003E handles real-time sensor data ingestion from building systems (HVAC, power meters, etc.)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ES3\u003C/strong\u003E serves as the data exchange layer between systems\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake\u003C/strong\u003E performs AI-powered anomaly detection using Cortex and returns insights back to Postgres\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis pattern is common in operational scenarios where transactional systems need to share data with analytical platforms while receiving enriched results back.\u003C/p\u003E\n","\u003Ch3\u003EArchitecture\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E┌─────────────────────────────────────────────────────────────────────────────┐\n│                         BIDIRECTIONAL DATA FLOW                             │\n├─────────────────────────────────────────────────────────────────────────────┤\n│                                                                             │\n│   ┌──────────────┐         ┌─────────────┐         ┌──────────────┐         │\n│   │  PostgreSQL  │  ────►  │     S3      │  ────►  │  Snowflake   │         │\n│   │              │         │             │         │              │         │\n│   │  • pg_lake   │  CSV    │  • export/  │  COPY   │  • Cortex AI │         │\n│   │  • Iceberg   │  Parquet│  • sync/    │  INTO   │  • Streams   │         │\n│   │              │  JSON   │  • archive/ │         │  • Tasks     │         │\n│   │              │         │             │         │              │         │\n│   │              │  ◄────  │             │  ◄────  │              │         │\n│   └──────────────┘         └─────────────┘         └──────────────┘         │\n│           Foreign Tables           Stream + COPY                            │\n│                                                                             │\n└─────────────────────────────────────────────────────────────────────────────┘\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EExport data from Postgres to S3 in multiple formats using Postgres's COPY command\u003C/li\u003E\u003Cli\u003EQuery S3 files directly from Postgres using foreign tables (no data import required)\u003C/li\u003E\u003Cli\u003ELoad data into Snowflake from S3 using COPY INTO\u003C/li\u003E\u003Cli\u003EDetect anomalies and generate AI explanations using Snowflake Cortex\u003C/li\u003E\u003Cli\u003ESync analytical results back to Postgres via S3\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EData Model\u003C/h3\u003E\n","\u003Cp\u003EThis lab simulates IoT sensor readings from 10 devices across a building complex:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESensor Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDevices\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EValue Range\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EUnit\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETemperature\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E68-80\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E°F\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHVAC systems, server rooms, freezers\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPower\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E100-500\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EkW\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMain panels, UPS, motors\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe lab generates approximately 100,000 sensor readings spanning several months of historical data at 15-minute intervals.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account with ACCOUNTADMIN access\u003C/li\u003E\u003Cli\u003EAWS account with permissions to create S3 buckets, policies, and IAM roles\u003C/li\u003E\u003Cli\u003ELocal terminal session to run \u003Ccode\u003Epsql\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Epsql\u003C/code\u003E client installed locally (install with \u003Ccode\u003Ebrew install postgresql\u003C/code\u003E on macOS)\u003C/li\u003E\u003Cli\u003EFamiliarity with SQL and basic Postgres concepts\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ES3 Storage Configuration\u003C/h2\u003E\n","\u003Cp\u003EBefore using pg_lake to move data between Postgres and Snowflake, you need to configure S3 access. Both platforms need access to the same S3 location, but they use different storage integration types.\u003C/p\u003E\n","\u003Ch3\u003EUnderstanding Storage Integrations\u003C/h3\u003E\n","\u003Cp\u003ESnowflake requires two separate storage integrations because Postgres and Snowflake access S3 through different mechanisms:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPlatform\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EIntegration Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake Postgres\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EPOSTGRES_EXTERNAL_STORAGE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnables pg_lake COPY, foreign tables, and Iceberg tables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EEXTERNAL_STAGE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnables external stages, COPY INTO, and Snowpipe\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EBoth integrations can share the same IAM role, but the trust policy must include principals from both integrations.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create S3 Bucket\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ELog into the \u003Cstrong\u003EAWS Management Console\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ES3\u003C/strong\u003E → \u003Cstrong\u003ECreate bucket\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EEnter a unique bucket name (e.g., \u003Ccode\u003Emycompany-iot-lab\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003Esame region\u003C/strong\u003E as your Snowflake account\u003C/li\u003E\u003Cli\u003EKeep default settings and click \u003Cstrong\u003ECreate bucket\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Record your bucket name - you'll need it in subsequent steps.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2: Create IAM Policy\u003C/h3\u003E\n","\u003Cp\u003EThe IAM policy grants the necessary S3 permissions for both reading and writing data.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EIAM\u003C/strong\u003E → \u003Cstrong\u003EPolicies\u003C/strong\u003E → \u003Cstrong\u003ECreate policy\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003EJSON\u003C/strong\u003E tab and paste the following (replace \u003Ccode\u003E<BUCKET_NAME>\u003C/code\u003E with your bucket):\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n    "Version": "2012-10-17",\n    "Statement": [\n        {\n            "Effect": "Allow",\n            "Action": [\n                "s3:PutObject",\n                "s3:GetObject",\n                "s3:GetObjectVersion",\n                "s3:DeleteObject",\n                "s3:DeleteObjectVersion"\n            ],\n            "Resource": "arn:aws:s3:::<BUCKET_NAME>/iot/*"\n        },\n        {\n            "Effect": "Allow",\n            "Action": [\n                "s3:ListBucket",\n                "s3:GetBucketLocation"\n            ],\n            "Resource": "arn:aws:s3:::<BUCKET_NAME>",\n            "Condition": {\n                "StringLike": {\n                    "s3:prefix": ["iot/*"]\n                }\n            }\n        }\n    ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EName the policy \u003Ccode\u003Esnowflake_iot_s3_access\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate policy\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 3: Create IAM Role\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EIAM\u003C/strong\u003E → \u003Cstrong\u003ERoles\u003C/strong\u003E → \u003Cstrong\u003ECreate role\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EAWS account\u003C/strong\u003E → \u003Cstrong\u003EAnother AWS account\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EEnter your own AWS account ID (12-digit number) as a temporary placeholder\u003C/li\u003E\u003Cli\u003ECheck \u003Cstrong\u003ERequire external ID\u003C/strong\u003E and enter \u003Ccode\u003E0000\u003C/code\u003E (temporary placeholder)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESearch for and attach the policy \u003Ccode\u003Esnowflake_iot_s3_access\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EName the role \u003Ccode\u003Esnowflake_iot_s3_role\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate role\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EOpen the new role and set \u003Cstrong\u003EMaximum session duration\u003C/strong\u003E to \u003Cstrong\u003E12 hours\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECopy the \u003Cstrong\u003ERole ARN\u003C/strong\u003E (e.g., \u003Ccode\u003Earn:aws:iam::123456789012:role/snowflake_iot_s3_role\u003C/code\u003E)\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERecord:\u003C/strong\u003E Save your Role ARN - you'll need this value in the next section.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Storage Integrations\u003C/h2\u003E\n","\u003Cp\u003ENow create the Snowflake storage integrations and generate the trust policy for AWS.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create Both Storage Integrations\u003C/h3\u003E\n","\u003Cp\u003ERun the following in Snowflake, replacing \u003Ccode\u003E<YOUR_ROLE_ARN>\u003C/code\u003E and \u003Ccode\u003E<YOUR_BUCKET>\u003C/code\u003E with your values from the previous section:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create Storage Integrations: Step 1 - Create Both Storage Integrations\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nCREATE STORAGE INTEGRATION PG_IOT_S3_INTEGRATION\n    TYPE = POSTGRES_EXTERNAL_STORAGE\n    STORAGE_PROVIDER = 'S3'\n    ENABLED = TRUE\n    STORAGE_AWS_ROLE_ARN = '<YOUR_ROLE_ARN>'\n    STORAGE_ALLOWED_LOCATIONS = ('s3://<YOUR_BUCKET>/iot/');\n\nCREATE STORAGE INTEGRATION SF_IOT_S3_INTEGRATION\n    TYPE = EXTERNAL_STAGE\n    STORAGE_PROVIDER = 'S3'\n    ENABLED = TRUE\n    STORAGE_AWS_ROLE_ARN = '<YOUR_ROLE_ARN>'\n    STORAGE_ALLOWED_LOCATIONS = ('s3://<YOUR_BUCKET>/iot/');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Get Integration Details\u003C/h3\u003E\n","\u003Cp\u003ERetrieve the IAM user ARN and external ID from each integration:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create Storage Integrations: Step 2 - Get Integration Details (Postgres)\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nDESCRIBE INTEGRATION PG_IOT_S3_INTEGRATION;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERecord the values for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ESTORAGE_AWS_IAM_USER_ARN\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTORAGE_AWS_EXTERNAL_ID\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create Storage Integrations: Step 2 - Get Integration Details (Snowflake)\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nDESCRIBE INTEGRATION SF_IOT_S3_INTEGRATION;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERecord these values as well (they will be different from the Postgres integration).\u003C/p\u003E\n","\u003Ch3\u003EStep 3: Update IAM Role Trust Policy\u003C/h3\u003E\n","\u003Cp\u003EReturn to AWS Console → \u003Cstrong\u003EIAM\u003C/strong\u003E → \u003Cstrong\u003ERoles\u003C/strong\u003E → \u003Ccode\u003Esnowflake_iot_s3_role\u003C/code\u003E → \u003Cstrong\u003ETrust relationships\u003C/strong\u003E → \u003Cstrong\u003EEdit trust policy\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EReplace the entire policy with the following, substituting the actual values from Step 2:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "Version": "2012-10-17",\n  "Statement": [\n    {\n      "Sid": "PostgresIntegration",\n      "Effect": "Allow",\n      "Principal": { "AWS": "<PG_STORAGE_AWS_IAM_USER_ARN>" },\n      "Action": "sts:AssumeRole",\n      "Condition": { "StringEquals": { "sts:ExternalId": "<PG_STORAGE_AWS_EXTERNAL_ID>" } }\n    },\n    {\n      "Sid": "SnowflakeIntegration",\n      "Effect": "Allow",\n      "Principal": { "AWS": "<SF_STORAGE_AWS_IAM_USER_ARN>" },\n      "Action": "sts:AssumeRole",\n      "Condition": { "StringEquals": { "sts:ExternalId": "<SF_STORAGE_AWS_EXTERNAL_ID>" } }\n    }\n  ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EUpdate policy\u003C/strong\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Setup\u003C/h2\u003E\n","\u003Cp\u003ECreate the Snowflake database objects that will receive data from Postgres and store anomaly detection results.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create Database and Warehouse\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Snowflake Setup: Step 1 - Create Database and Warehouse\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\n\nCREATE DATABASE IF NOT EXISTS IOT_LAB;\nCREATE SCHEMA IF NOT EXISTS IOT_LAB.SENSORS;\n\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCREATE WAREHOUSE IF NOT EXISTS IOT_WH\n    WAREHOUSE_SIZE = 'XSMALL'\n    AUTO_SUSPEND = 60\n    AUTO_RESUME = TRUE;\n\nUSE WAREHOUSE IOT_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Create File Format and Stage\u003C/h3\u003E\n","\u003Cp\u003EReplace \u003Ccode\u003E<BUCKET_NAME>\u003C/code\u003E with your bucket.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Snowflake Setup: Step 2 - Create File Format and Stage\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCREATE OR REPLACE FILE FORMAT CSV_FORMAT_IOT\n    TYPE = 'CSV'\n    FIELD_OPTIONALLY_ENCLOSED_BY = '"'\n    SKIP_HEADER = 1\n    NULL_IF = ('NULL', 'null', '');\n\nCREATE OR REPLACE STAGE IOT_STAGE\n    STORAGE_INTEGRATION = SF_IOT_S3_INTEGRATION\n    URL = 's3://<BUCKET_NAME>/iot/'\n    FILE_FORMAT = CSV_FORMAT_IOT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Create Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Snowflake Setup: Step 3 - Create Tables\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCREATE OR REPLACE TABLE SENSOR_READINGS (\n    READING_ID       INT,\n    SENSOR_NAME      VARCHAR(50),\n    SENSOR_TYPE      VARCHAR(20),\n    READING_TS       TIMESTAMP_NTZ,\n    VALUE            NUMBER(10,2),\n    UNIT             VARCHAR(10),\n    LOADED_AT        TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n);\n\nCREATE OR REPLACE TABLE SENSOR_ANOMALIES (\n    ANOMALY_ID       INT AUTOINCREMENT PRIMARY KEY,\n    SENSOR_NAME      VARCHAR(50),\n    SENSOR_TYPE      VARCHAR(20),\n    READING_TS       TIMESTAMP_NTZ,\n    PREDICTED_VALUE  NUMBER(10,2),\n    ACTUAL_VALUE     NUMBER(10,2),\n    ANOMALY_SCORE    NUMBER(5,4),\n    AI_EXPLANATION   VARCHAR(1000),\n    DETECTED_AT      TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4: Create Stream for Change Capture\u003C/h3\u003E\n","\u003Cp\u003EStreams in Snowflake track changes to tables, enabling efficient incremental processing. This stream will capture new anomalies for export back to Postgres.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Snowflake Setup: Step 4 - Create Stream for Change Capture\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCREATE OR REPLACE STREAM ANOMALIES_STREAM ON TABLE SENSOR_ANOMALIES\n    APPEND_ONLY = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 5: Verify Stage Access\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Snowflake Setup: Step 5 - Verify Stage Access\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nLIST @IOT_STAGE/;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis should return an empty result (no files yet) without errors, confirming S3 access is working.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPostgres Instance Setup\u003C/h2\u003E\n","\u003Cp\u003ECreate a Snowflake Postgres instance with pg_lake enabled.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create Network Policy\u003C/h3\u003E\n","\u003Cp\u003ESnowflake Postgres requires a network policy to allow client connections.  Replace \u003Ccode\u003Ennn.nnn.nnn.nnn/32\u003C/code\u003E with specific IP (or subnet) and CIDR for your organization.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Instance Setup: Step 1 - Create Network Policy\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS PG_NETWORK_DB;\nCREATE SCHEMA IF NOT EXISTS PG_NETWORK_DB.PG_NETWORK;\n\nUSE SCHEMA PG_NETWORK_DB.PG_NETWORK;\n\n-- Get current IP if needed for Network Rule.  \n-- This value maybe used for `nnn.nnn.nnn.nnn` to limit access to your connection.\nSELECT CURRENT_IP_ADDRESS();\n\nCREATE OR REPLACE NETWORK RULE PG_IOT_INGRESS_RULE\n    TYPE = IPV4\n    VALUE_LIST = ('nnn.nnn.nnn.nnn/32')\n    MODE = POSTGRES_INGRESS\n    COMMENT = 'Allow Postgres client connections (restrict in production)';\n\nCREATE OR REPLACE NETWORK POLICY PG_IOT_NETWORK_POLICY\n    ALLOWED_NETWORK_RULE_LIST = ('PG_IOT_INGRESS_RULE')\n    COMMENT = 'Network policy for Snowflake Postgres instances';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Create Postgres Instance\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Instance Setup: Step 2 - Create Postgres Instance\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nCREATE POSTGRES INSTANCE IOT_PG\n    AUTHENTICATION_AUTHORITY = POSTGRES\n    COMPUTE_FAMILY = 'STANDARD_L'\n    STORAGE_SIZE_GB = 200\n    POSTGRES_VERSION = 18\n    HIGH_AVAILABILITY = FALSE\n    NETWORK_POLICY = 'PG_IOT_NETWORK_POLICY'\n    STORAGE_INTEGRATION = PG_IOT_S3_INTEGRATION\n    COMMENT = 'IoT Lab Postgres instance for pg_lake demos';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Save the user and password displayed in the \u003Ccode\u003Eaccess_roles\u003C/code\u003E field and the \u003Ccode\u003Ehost\u003C/code\u003E - you'll need it to connect.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3: Monitor Instance Status\u003C/h3\u003E\n","\u003Cp\u003EWait for the instance to reach READY state:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Instance Setup: Step 3 - Monitor Instance Status\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nSHOW POSTGRES INSTANCES;\n\nDESCRIBE POSTGRES INSTANCE IOT_PG;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERecord the \u003Cstrong\u003Ehost\u003C/strong\u003E value from the DESCRIBE output.\u003C/p\u003E\n","\u003Ch3\u003EStep 4: Configure psql Connection\u003C/h3\u003E\n","\u003Cp\u003ESet environment variables in your terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport PGHOST=<hostname from DESCRIBE output>\nexport PGPORT=5432\nexport PGDATABASE=postgres\nexport PGUSER=snowflake_admin\nexport PGPASSWORD=<password from CREATE output>\nexport PGSSLMODE=require\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest the connection:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epsql -c "SELECT version();"\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPostgres Setup\u003C/h2\u003E\n","\u003Cp\u003ECreate the IoT sensor tables and generate sample data in Postgres.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Enable Extensions\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.postgresql.org/docs/current/app-psql.html\"\u003Epsql\u003C/a\u003E is the interactive terminal for PostgreSQL, allowing you to enter queries, execute SQL commands, and manage the database from the command line.\u003C/p\u003E\n","\u003Cp\u003EConnect to Postgres via \u003Ccode\u003Epsql\u003C/code\u003E and enable pg_lake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epsql\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Setup: Step 1 - Enable Extensions\n-- Execute in: psql (Postgres)\nCREATE EXTENSION IF NOT EXISTS pg_lake CASCADE;\nCREATE EXTENSION IF NOT EXISTS pg_cron;\nCREATE EXTENSION IF NOT EXISTS pg_incremental;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003ECASCADE\u003C/code\u003E option automatically creates required dependencies like the \u003Ccode\u003Epg_lake\u003C/code\u003E foreign data wrapper server.\u003C/p\u003E\n","\u003Ch3\u003EStep 2: Create Sensor Readings Table\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Setup: Step 2 - Create Sensor Readings Table\n-- Execute in: psql (Postgres)\nCREATE TABLE sensor_readings (\n    reading_id   BIGSERIAL PRIMARY KEY,\n    sensor_name  TEXT NOT NULL,\n    sensor_type  TEXT NOT NULL,\n    reading_ts   TIMESTAMP NOT NULL,\n    value        NUMERIC(10,2) NOT NULL,\n    unit         TEXT NOT NULL,\n    created_at   TIMESTAMP DEFAULT now()\n);\n\nCREATE INDEX idx_readings_ts ON sensor_readings (reading_ts);\nCREATE INDEX idx_readings_sensor ON sensor_readings (sensor_name, reading_ts);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Generate Historical Sensor Data\u003C/h3\u003E\n","\u003Cp\u003EThis generates approximately 100,000 readings from 10 sensors over several months:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Setup: Step 3 - Generate Historical Sensor Data\n-- Execute in: psql (Postgres)\nINSERT INTO sensor_readings (sensor_name, sensor_type, reading_ts, value, unit)\nSELECT   sensor_name,\n         sensor_type,\n         ts,\n         CASE sensor_type\n             WHEN 'temperature' THEN 68 + (random() * 12)::numeric(10,2)\n             WHEN 'power'       THEN 100 + (random() * 400)::numeric(10,2)\n         END AS value,\n         CASE sensor_type\n             WHEN 'temperature' THEN '°F'\n             WHEN 'power'       THEN 'kW'\n         END AS unit\nFROM     (\n             SELECT * FROM (VALUES\n                 ('HVAC-Floor1',     'temperature'),\n                 ('HVAC-Floor2',     'temperature'),\n                 ('HVAC-Floor3',     'temperature'),\n                 ('Freezer-Main',    'temperature'),\n                 ('ServerRoom-A',    'temperature'),\n                 ('MainPanel-A',     'power'),\n                 ('MainPanel-B',     'power'),\n                 ('DataCenter-UPS',  'power'),\n                 ('HVAC-Compressor', 'power'),\n                 ('Elevator-Motor',  'power')\n             ) AS s(sensor_name, sensor_type)\n         ) sensors\nCROSS JOIN generate_series(1, 10000) AS n\nCROSS JOIN LATERAL (\n    SELECT now() - (n * interval '15 minutes') AS ts\n) timestamps;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4: Verify Data Load\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Setup: Step 4 - Verify Data Load\n-- Execute in: psql (Postgres)\nSELECT   sensor_type,\n         count(*) AS readings,\n         round(avg(value), 2) AS avg_value,\n         min(reading_ts) AS earliest,\n         max(reading_ts) AS latest\nFROM     sensor_readings\nGROUP BY sensor_type\nORDER BY sensor_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see approximately 50,000 readings per sensor type.\u003C/p\u003E\n","\u003Ch3\u003EStep 5: Create Anomalies Table\u003C/h3\u003E\n","\u003Cp\u003EThis table will receive AI-enriched anomaly data from Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Setup: Step 5 - Create Anomalies Table\n-- Execute in: psql (Postgres)\nCREATE TABLE sensor_anomalies (\n    anomaly_id       BIGSERIAL PRIMARY KEY,\n    sensor_name      TEXT NOT NULL,\n    sensor_type      TEXT NOT NULL,\n    reading_ts       TIMESTAMP NOT NULL,\n    predicted_value  NUMERIC(10,2),\n    actual_value     NUMERIC(10,2),\n    anomaly_score    NUMERIC(5,4),\n    ai_explanation   TEXT,\n    detected_at      TIMESTAMP DEFAULT now()\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 6: Create Foreign Table for Sync Files\u003C/h3\u003E\n","\u003Cp\u003EThis foreign table will read anomaly files exported from Snowflake (replacing \u003Ccode\u003E<YOUR_BUCKET>\u003C/code\u003E with your value):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Postgres Setup: Step 6 - Create Foreign Table for Sync Files\n-- Execute in: psql (Postgres)\nCREATE FOREIGN TABLE sync_anomalies (\n    sensor_name      TEXT,\n    sensor_type      TEXT,\n    reading_ts       TEXT,\n    predicted_value  TEXT,\n    actual_value     TEXT,\n    anomaly_score    TEXT,\n    ai_explanation   TEXT,\n    _filename        TEXT\n)\nSERVER pg_lake\nOPTIONS (\n    header 'true',\n    format 'csv',\n    filename 'true',\n    compression 'gzip',\n    quote '"',    \n    path 's3://<YOUR_BUCKET>/iot/sync/*'\n);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExport Data from Postgres\u003C/h2\u003E\n","\u003Cp\u003EUse pg_lake to export sensor data from Postgres to S3. This demonstrates the first half of bidirectional data movement.\u003C/p\u003E\n","\u003Ch3\u003EUnderstanding pg_lake COPY\u003C/h3\u003E\n","\u003Cp\u003EThe pg_lake extension extends Postgres's standard \u003Ccode\u003ECOPY\u003C/code\u003E command to support S3 destinations. The syntax is similar to standard COPY but writes directly to cloud storage instead of local files.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Check Data to Export\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Export Data from Postgres: Step 1 - Check Data to Export\n-- Execute in: psql (Postgres)\nSELECT   sensor_type, count(1) AS cnt\nFROM     sensor_readings\nGROUP BY sensor_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Export to CSV\u003C/h3\u003E\n","\u003Cp\u003EExport sensor readings to S3 in compressed CSV format (replacing \u003Ccode\u003E<YOUR_BUCKET>\u003C/code\u003E with your value):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Export Data from Postgres: Step 2 - Export to CSV\n-- Execute in: psql (Postgres)\nCOPY (\n    SELECT   reading_id, sensor_name, sensor_type, reading_ts, value, unit\n    FROM     sensor_readings\n) TO 's3://<YOUR_BUCKET>/iot/export/sensor_readings.csv.gz'\nWITH (header true, compression gzip, format csv);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA sub select was used to show how you could be more selective in the data that was copied.  By using the sub select you could have filtered specific rows, columns, or even joined to another table.  As an alternative, you could have left out the sub select and just used the table name to copy the entire table.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFormat Note:\u003C/strong\u003E pg_lake supports multiple output formats. You can specify the format either through the file extension (\u003Ccode\u003E.csv\u003C/code\u003E, \u003Ccode\u003E.parquet\u003C/code\u003E, \u003Ccode\u003E.json\u003C/code\u003E) or explicitly in the \u003Ccode\u003EWITH\u003C/code\u003E clause using \u003Ccode\u003Eformat csv|json|parquet\u003C/code\u003E. Additional options like \u003Ccode\u003Ecompression gzip\u003C/code\u003E can reduce file sizes significantly.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3: Verify Export\u003C/h3\u003E\n","\u003Cp\u003EUse \u003Ccode\u003Elake_file.list()\u003C/code\u003E to browse S3 contents directly from Postgres (replacing \u003Ccode\u003E<YOUR_BUCKET>\u003C/code\u003E with your value):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Export Data from Postgres: Step 3 - Verify Export\n-- Execute in: psql (Postgres)\nSELECT * FROM lake_file.list('s3://<YOUR_BUCKET>/iot/export/*');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the exported CSV file with its size and modification timestamp.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery S3 with Foreign Tables\u003C/h2\u003E\n","\u003Cp\u003Epg_lake enables querying S3 files directly without importing data into Postgres tables. This is useful for ad-hoc analysis of exported data or for building data pipelines.\u003C/p\u003E\n","\u003Ch3\u003EUnderstanding Foreign Tables\u003C/h3\u003E\n","\u003Cp\u003EForeign tables in pg_lake create a virtual table that reads from S3 on demand. The data stays in S3 - queries execute by fetching and processing the data at query time. This is ideal for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EVerifying exported data before loading elsewhere\u003C/li\u003E\u003Cli\u003EQuerying archived data without storage overhead\u003C/li\u003E\u003Cli\u003EBuilding lightweight data pipelines\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 1: Create CSV Foreign Table\u003C/h3\u003E\n","\u003Cp\u003ECreate the \u003Ccode\u003Ereadings_csv\u003C/code\u003E foreign table (replacing \u003Ccode\u003E<YOUR_BUCKET>\u003C/code\u003E with your value):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Query S3 with Foreign Tables: Step 1 - Create CSV Foreign Table\n-- Execute in: psql (Postgres)\nCREATE FOREIGN TABLE readings_csv()\nSERVER pg_lake\nOPTIONS (\n    header 'true',\n    format 'csv',\n    filename 'true',\n    path 's3://<YOUR_BUCKET>/iot/export/sensor_readings.csv.gz'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe empty parentheses \u003Ccode\u003E()\u003C/code\u003E tell pg_lake to infer the schema from the file. The \u003Ccode\u003Efilename 'true'\u003C/code\u003E option adds a \u003Ccode\u003E_filename\u003C/code\u003E column showing the source file.\u003C/p\u003E\n","\u003Ch3\u003EStep 2: Query the Foreign Table\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Query S3 with Foreign Tables: Step 2 - Query the Foreign Table\n-- Execute in: psql (Postgres)\nSELECT * FROM readings_csv LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis reads directly from S3 - no data is stored in Postgres.\u003C/p\u003E\n","\u003Ch3\u003EStep 3: Aggregate Data from S3\u003C/h3\u003E\n","\u003Cp\u003EForeign tables support standard SQL operations:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Query S3 with Foreign Tables: Step 3 - Aggregate Data from S3\n-- Execute in: psql (Postgres)\nSELECT   sensor_type, \n         count(*) AS readings, \n         round(avg(value::numeric), 2) AS avg_value\nFROM     readings_csv\nGROUP BY sensor_type;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad Data into Snowflake\u003C/h2\u003E\n","\u003Cp\u003ELoad the exported sensor data from S3 into Snowflake for analytics processing.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Verify Files in Stage\u003C/h3\u003E\n","\u003Cp\u003EIn Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Load Data into Snowflake: Step 1 - Verify Files in Stage\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\nUSE WAREHOUSE IOT_WH;\n\nLIST @IOT_STAGE/export/;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the CSV file exported from Postgres.\u003C/p\u003E\n","\u003Ch3\u003EStep 2: Preview Data\u003C/h3\u003E\n","\u003Cp\u003EVerify the data format before loading:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Load Data into Snowflake: Step 2 - Preview Data\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nSELECT   $1, $2, $3, $4, $5, $6\nFROM     @IOT_STAGE/export/sensor_readings.csv.gz\n         (FILE_FORMAT => 'CSV_FORMAT_IOT')\nLIMIT    10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Load CSV Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Load Data into Snowflake: Step 3 - Load CSV Data\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCOPY INTO SENSOR_READINGS (READING_ID, SENSOR_NAME, SENSOR_TYPE, READING_TS, VALUE, UNIT)\nFROM @IOT_STAGE/export/\nFILE_FORMAT = 'CSV_FORMAT_IOT'\nPATTERN = '.*readings.*\\\\.csv.gz'\nON_ERROR = 'CONTINUE';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAutomation Note:\u003C/strong\u003E For production pipelines, this COPY command can be automated in several ways:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESnowpipe\u003C/strong\u003E: Configure auto-ingest to automatically load new files as they arrive in S3\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETasks\u003C/strong\u003E: Schedule periodic COPY operations using Snowflake Tasks\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreams + Tasks\u003C/strong\u003E: Combine change data capture with scheduled processing for event-driven pipelines\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESee the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro\"\u003ESnowpipe Documentation\u003C/a\u003E for auto-ingest configuration.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 4: Verify Load\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Load Data into Snowflake: Step 4 - Verify Load\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nSELECT   SENSOR_TYPE,\n         COUNT(*) AS READINGS,\n         ROUND(AVG(VALUE), 2) AS AVG_VALUE,\n         MIN(READING_TS) AS EARLIEST,\n         MAX(READING_TS) AS LATEST\nFROM     SENSOR_READINGS\nGROUP BY SENSOR_TYPE\nORDER BY SENSOR_TYPE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAnomaly Detection with Cortex AI\u003C/h2\u003E\n","\u003Cp\u003EUse Snowflake Cortex to detect anomalies and generate AI-powered explanations. This demonstrates enriching operational data with AI capabilities.\u003C/p\u003E\n","\u003Ch3\u003EUnderstanding Cortex Complete\u003C/h3\u003E\n","\u003Cp\u003ESnowflake Cortex provides access to large language models (LLMs) directly within SQL queries. The \u003Ccode\u003ECOMPLETE\u003C/code\u003E function sends prompts to an LLM and returns generated text. This enables AI enrichment without external API calls or data movement.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Detect Anomalies and Generate AI Explanations\u003C/h3\u003E\n","\u003Cp\u003EThis single statement simulates anomalies (using a simplified scoring approach for this lab) and generates natural language explanations using Cortex:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Anomaly Detection with Cortex AI: Step 1 - Detect Anomalies and Generate AI Explanations\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nINSERT INTO SENSOR_ANOMALIES (SENSOR_NAME, SENSOR_TYPE, READING_TS, PREDICTED_VALUE, ACTUAL_VALUE, ANOMALY_SCORE, AI_EXPLANATION)\nSELECT   SENSOR_NAME, \n         SENSOR_TYPE, \n         READING_TS, \n         ROUND(VALUE * 0.9, 2) AS PREDICTED_VALUE,\n         VALUE AS ACTUAL_VALUE,\n         0.75 AS ANOMALY_SCORE,\n         SNOWFLAKE.CORTEX.COMPLETE('llama3.1-8b',\n             'In 1 sentence, explain this IoT anomaly: ' || SENSOR_NAME || \n             ' (' || SENSOR_TYPE || ') read ' || VALUE || ' ' || UNIT\n         )\nFROM     SENSOR_READINGS\nORDER BY RANDOM()\nLIMIT    100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis generates 100 sample anomalies with AI explanations. In production, you would use statistical methods (Z-score, IQR, ML models) for actual anomaly detection.\u003C/p\u003E\n","\u003Ch3\u003EStep 2: View AI-Explained Anomalies\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Anomaly Detection with Cortex AI: Step 2 - View AI-Explained Anomalies\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nSELECT   ANOMALY_ID, \n         SENSOR_NAME, \n         SENSOR_TYPE, \n         ANOMALY_SCORE, \n         AI_EXPLANATION, \n         DETECTED_AT\nFROM     SENSOR_ANOMALIES\nORDER BY DETECTED_AT DESC\nLIMIT    10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach anomaly now has a human-readable explanation generated by the LLM, making it easier for operators to understand and act on alerts.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESync Anomalies to Postgres\u003C/h2\u003E\n","\u003Cp\u003EExport the AI-enriched anomalies back to S3 for Postgres to consume, completing the bidirectional data flow.\u003C/p\u003E\n","\u003Ch3\u003EUnderstanding Streams for Change Capture\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EANOMALIES_STREAM\u003C/code\u003E created earlier tracks new rows inserted into \u003Ccode\u003ESENSOR_ANOMALIES\u003C/code\u003E. When you query the stream, it returns only the changes since the last consumption, enabling efficient incremental processing.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Check Stream for Changes\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Sync Anomalies to Postgres: Step 1 - Check Stream for Changes\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nSELECT COUNT(1) FROM ANOMALIES_STREAM;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis shows how many new anomalies are ready for export.\u003C/p\u003E\n","\u003Ch3\u003EStep 2: Export to S3\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Sync Anomalies to Postgres: Step 2 - Export to S3\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCOPY INTO @IOT_STAGE/sync/\nFROM (\n        SELECT SENSOR_NAME, SENSOR_TYPE, READING_TS, PREDICTED_VALUE, \n               ACTUAL_VALUE, ANOMALY_SCORE, AI_EXPLANATION\n        FROM   ANOMALIES_STREAM\n    )\n    FILE_FORMAT = 'CSV_FORMAT_IOT'\n    HEADER = TRUE\n    INCLUDE_QUERY_ID = TRUE\n    SINGLE = FALSE\n    MAX_FILE_SIZE = 16000000\n    OVERWRITE = FALSE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAutomation Note:\u003C/strong\u003E This COPY command can be wrapped in a Snowflake Task to run automatically on a schedule or when the stream contains data. Example:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TASK EXPORT_ANOMALIES_TASK\n    WAREHOUSE = IOT_WH\n    SCHEDULE = '5 MINUTE'\nWHEN\n    SYSTEM$STREAM_HAS_DATA('ANOMALIES_STREAM')\nAS\n    COPY INTO @IOT_STAGE/sync/ FROM (...);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates an event-driven pipeline that exports anomalies as soon as they're detected.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3: Verify Export\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Sync Anomalies to Postgres: Step 3 - Verify Export\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nLIST @IOT_STAGE/sync/;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see one or more CSV files containing the anomaly data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EReceive Anomalies in Postgres\u003C/h2\u003E\n","\u003Cp\u003EImport the AI-enriched anomaly data back into Postgres, completing the round trip.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Check for Sync Files\u003C/h3\u003E\n","\u003Cp\u003EIn Postgres, use the list function to check for files from Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Receive Anomalies in Postgres: Step 1 - Check for Sync Files\n-- Execute in: psql (Postgres)\nSELECT * FROM lake_file.list('s3://<YOUR_BUCKET>/iot/sync/*');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Import Anomalies\u003C/h3\u003E\n","\u003Cp\u003ELoad the anomaly data using the pre-configured foreign table (replacing \u003Ccode\u003E<YOUR_BUCKET>\u003C/code\u003E with your value):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Receive Anomalies in Postgres: Step 2 - Import Anomalies\n-- Execute in: psql (Postgres)\nINSERT INTO sensor_anomalies (sensor_name, sensor_type, reading_ts, predicted_value, actual_value, anomaly_score, ai_explanation)\n(SELECT   sensor_name::text, \n          sensor_type::text, \n          reading_ts::timestamp, \n          predicted_value::numeric(10,2),\n          actual_value::numeric(10,2), \n          anomaly_score::numeric(5,4), \n          ai_explanation::text\nFROM     sync_anomalies\nWHERE    _filename IN (SELECT path FROM lake_file.list('s3://<YOUR_BUCKET>/iot/sync/*')))\nON CONFLICT DO NOTHING;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EON CONFLICT DO NOTHING\u003C/code\u003E clause ensures idempotent processing - running this multiple times won't create duplicates.\u003C/p\u003E\n","\u003Ch3\u003EStep 3: View AI-Enriched Anomalies in Postgres\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Receive Anomalies in Postgres: Step 3 - View AI-Enriched Anomalies in Postgres\n-- Execute in: psql (Postgres)\nSELECT   sensor_name, \n         sensor_type, \n         reading_ts, \n         anomaly_score, \n         ai_explanation, \n         detected_at\nFROM     sensor_anomalies\nORDER BY detected_at DESC\nLIMIT    10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe anomalies detected in Snowflake with AI explanations are now available in Postgres for operational use.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Ch3\u003EPostgres Cleanup\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Cleanup: Postgres Cleanup\n-- Execute in: psql (Postgres)\nDROP TABLE IF EXISTS sensor_anomalies CASCADE;\nDROP TABLE IF EXISTS sensor_readings CASCADE;\n\nDROP FOREIGN TABLE IF EXISTS sync_anomalies CASCADE;\nDROP FOREIGN TABLE IF EXISTS readings_csv CASCADE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESnowflake Cleanup\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Cleanup: Snowflake Cleanup\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nDROP STREAM IF EXISTS ANOMALIES_STREAM;\n\nDROP TABLE IF EXISTS SENSOR_ANOMALIES;\nDROP TABLE IF EXISTS SENSOR_READINGS;\n\nDROP STAGE IF EXISTS IOT_STAGE;\nDROP FILE FORMAT IF EXISTS CSV_FORMAT_IOT;\n\nDROP SCHEMA IF EXISTS IOT_LAB.SENSORS;\nDROP DATABASE IF EXISTS IOT_LAB;\nDROP WAREHOUSE IF EXISTS IOT_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInfrastructure Cleanup (Optional)\u003C/h3\u003E\n","\u003Cp\u003ETo fully remove all infrastructure including the Postgres instance:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Cleanup: Infrastructure Cleanup (Optional)\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nDROP POSTGRES INSTANCE IF EXISTS IOT_PG;\n\nUSE SCHEMA PG_NETWORK_DB.PG_NETWORK;\n\nDROP NETWORK POLICY IF EXISTS PG_IOT_NETWORK_POLICY;\nDROP NETWORK RULE IF EXISTS PG_IOT_INGRESS_RULE;\n\nDROP STORAGE INTEGRATION IF EXISTS PG_IOT_S3_INTEGRATION;\nDROP STORAGE INTEGRATION IF EXISTS SF_IOT_S3_INTEGRATION;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ES3 Cleanup\u003C/h3\u003E\n","\u003Cp\u003EDelete the following S3 prefixes (replacing \u003Ccode\u003E<YOUR_BUCKET>\u003C/code\u003E with your value):\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Es3://<YOUR_BUCKET>/iot/export/*\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Es3://<YOUR_BUCKET>/iot/sync/*\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOptionally delete the S3 bucket and IAM resources.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003ECongratulations! You have successfully:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EConfigured S3 storage integration for both pg_lake and Snowflake\u003C/li\u003E\u003Cli\u003ECreated a Snowflake Postgres instance with pg_lake enabled\u003C/li\u003E\u003Cli\u003EExported data from Postgres to S3 in CSV format using pg_lake\u003C/li\u003E\u003Cli\u003EQueried S3 files directly using pg_lake foreign tables\u003C/li\u003E\u003Cli\u003ELoaded data into Snowflake using COPY INTO\u003C/li\u003E\u003Cli\u003EGenerated AI-powered anomaly explanations using Snowflake Cortex\u003C/li\u003E\u003Cli\u003ESynchronized analytical results back to Postgres via S3\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Capabilities Demonstrated\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPostgres (pg_lake)\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESnowflake\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECOPY TO S3 (CSV, Parquet, JSON)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExternal Stage with S3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EForeign Tables for S3 queries\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECOPY INTO for data loading\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Elake_file.list() for S3 browsing\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStreams for change capture\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro\"\u003ESnowpipe Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003ESnowflake Tasks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/about\"\u003ESnowflake Postgres Documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build bidirectional data pipelines between Snowflake Postgres (pg_lake) and Snowflake","title":"Bidirectional Data Pipelines with pg_lake and Snowflake - External Stage","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\n### What is pg_lake?\n\nPostgreSQL (Postgres) extensions are loadable modules that add new functionality to the database—such as data types, functions, operators, and storage engines—without modifying the core Postgres code. For more details, see the [Postgres Extensions documentation](https://www.postgresql.org/docs/current/extend-extensions.html).\n\n[pg_lake](https://github.com/Snowflake-Labs/pg_lake) is a powerful Postgres extension available in Snowflake Postgres that enables direct reading and writing of data lake files stored in cloud object storage like Amazon S3. With pg_lake, you can export data from Postgres tables directly to S3 in various formats (CSV, Parquet, JSON), query files in S3 using foreign tables without importing the data, and create Iceberg tables for open table format storage.\n\nWhen combined with Snowflake's data platform, pg_lake enables simple bidirectional data pipelines that leverage the operational strengths of Postgres alongside Snowflake's powerful analytics and AI capabilities. \n\nIn this quick start guide, we use S3 as the example, however, you can also use Azure object storage as well.  For more details see the [Storage Integration documentation](https://docs.snowflake.com/en/sql-reference/sql/create-storage-integration).\n\n### External Stage vs. Internal Stage\n\npg_lake supports two storage options for exchanging data between Postgres and Snowflake:\n\n| | External Stage | Internal Stage |\n|---|---|---|\n| Storage location | Customer-provided object storage (e.g. Amazon S3, Azure Blob) | Snowflake-managed internal object storage |\n| Configuration | Requires IAM roles, trust policies, and storage integrations for both Postgres and Snowflake | Minimal setup — automatically provisioned with the Postgres instance |\n| Data residency | Data resides in the customer's cloud account | Data never leaves the Snowflake security perimeter |\n| Use when | You need to share data with systems outside of Snowflake, retain data in your own storage, or integrate with existing data lake architectures | You want the simplest setup, maximum security, and data exchange is exclusively between Snowflake Postgres and Snowflake |\n\nThis quickstart uses the **external stage** approach with customer-provided S3 storage. For the internal stage version where data never leaves Snowflake, see [Bidirectional Data Pipelines with pg_lake and Snowflake - Internal Stage](en/developers/guides/snowflake-postgres-pg-lake-iot-internal-stage/).\n\n### Use Case: IoT Sensor Monitoring\n\nThis quickstart demonstrates a practical IoT sensor monitoring scenario where:\n- **Postgres** handles real-time sensor data ingestion from building systems (HVAC, power meters, etc.)\n- **S3** serves as the data exchange layer between systems\n- **Snowflake** performs AI-powered anomaly detection using Cortex and returns insights back to Postgres\n\nThis pattern is common in operational scenarios where transactional systems need to share data with analytical platforms while receiving enriched results back.\n\n### Architecture\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│                         BIDIRECTIONAL DATA FLOW                             │\n├─────────────────────────────────────────────────────────────────────────────┤\n│                                                                             │\n│   ┌──────────────┐         ┌─────────────┐         ┌──────────────┐         │\n│   │  PostgreSQL  │  ────►  │     S3      │  ────►  │  Snowflake   │         │\n│   │              │         │             │         │              │         │\n│   │  • pg_lake   │  CSV    │  • export/  │  COPY   │  • Cortex AI │         │\n│   │  • Iceberg   │  Parquet│  • sync/    │  INTO   │  • Streams   │         │\n│   │              │  JSON   │  • archive/ │         │  • Tasks     │         │\n│   │              │         │             │         │              │         │\n│   │              │  ◄────  │             │  ◄────  │              │         │\n│   └──────────────┘         └─────────────┘         └──────────────┘         │\n│           Foreign Tables           Stream + COPY                            │\n│                                                                             │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### What You Will Learn \n\n- Export data from Postgres to S3 in multiple formats using Postgres's COPY command\n- Query S3 files directly from Postgres using foreign tables (no data import required)\n- Load data into Snowflake from S3 using COPY INTO\n- Detect anomalies and generate AI explanations using Snowflake Cortex\n- Sync analytical results back to Postgres via S3\n\n### Data Model\n\nThis lab simulates IoT sensor readings from 10 devices across a building complex:\n\n| Sensor Type  | Devices | Value Range | Unit | Description |\n|--------------|---------|-------------|------|-------------|\n| Temperature  | 5       | 68-80       | °F   | HVAC systems, server rooms, freezers |\n| Power        | 5       | 100-500     | kW   | Main panels, UPS, motors |\n\nThe lab generates approximately 100,000 sensor readings spanning several months of historical data at 15-minute intervals.\n\n### Prerequisites\n\n- Snowflake account with ACCOUNTADMIN access\n- AWS account with permissions to create S3 buckets, policies, and IAM roles\n- Local terminal session to run `psql`\n- `psql` client installed locally (install with `brew install postgresql` on macOS)\n- Familiarity with SQL and basic Postgres concepts\n\n\u003C!-- ------------------------ --\u003E\n## S3 Storage Configuration\n\nBefore using pg_lake to move data between Postgres and Snowflake, you need to configure S3 access. Both platforms need access to the same S3 location, but they use different storage integration types.\n\n### Understanding Storage Integrations\n\nSnowflake requires two separate storage integrations because Postgres and Snowflake access S3 through different mechanisms:\n\n| Platform | Integration Type | Purpose |\n|----------|-----------------|---------|\n| Snowflake Postgres | `POSTGRES_EXTERNAL_STORAGE` | Enables pg_lake COPY, foreign tables, and Iceberg tables |\n| Snowflake | `EXTERNAL_STAGE` | Enables external stages, COPY INTO, and Snowpipe |\n\nBoth integrations can share the same IAM role, but the trust policy must include principals from both integrations.\n\n### Step 1: Create S3 Bucket\n\n1. Log into the **AWS Management Console**\n2. Navigate to **S3** → **Create bucket**\n3. Enter a unique bucket name (e.g., `mycompany-iot-lab`)\n4. Select the **same region** as your Snowflake account\n5. Keep default settings and click **Create bucket**\n\n\u003E **Note:** Record your bucket name - you'll need it in subsequent steps.\n\n### Step 2: Create IAM Policy\n\nThe IAM policy grants the necessary S3 permissions for both reading and writing data.\n\n1. Navigate to **IAM** → **Policies** → **Create policy**\n2. Click the **JSON** tab and paste the following (replace `\u003CBUCKET_NAME\u003E` with your bucket):\n\n```json\n{\n    \"Version\": \"2012-10-17\",\n    \"Statement\": [\n        {\n            \"Effect\": \"Allow\",\n            \"Action\": [\n                \"s3:PutObject\",\n                \"s3:GetObject\",\n                \"s3:GetObjectVersion\",\n                \"s3:DeleteObject\",\n                \"s3:DeleteObjectVersion\"\n            ],\n            \"Resource\": \"arn:aws:s3:::\u003CBUCKET_NAME\u003E/iot/*\"\n        },\n        {\n            \"Effect\": \"Allow\",\n            \"Action\": [\n                \"s3:ListBucket\",\n                \"s3:GetBucketLocation\"\n            ],\n            \"Resource\": \"arn:aws:s3:::\u003CBUCKET_NAME\u003E\",\n            \"Condition\": {\n                \"StringLike\": {\n                    \"s3:prefix\": [\"iot/*\"]\n                }\n            }\n        }\n    ]\n}\n```\n\n3. Click **Next**\n4. Name the policy `snowflake_iot_s3_access`\n5. Click **Create policy**\n\n### Step 3: Create IAM Role\n\n1. Navigate to **IAM** → **Roles** → **Create role**\n2. Select **AWS account** → **Another AWS account**\n3. Enter your own AWS account ID (12-digit number) as a temporary placeholder\n4. Check **Require external ID** and enter `0000` (temporary placeholder)\n5. Click **Next**\n6. Search for and attach the policy `snowflake_iot_s3_access`\n7. Click **Next**\n8. Name the role `snowflake_iot_s3_role`\n9. Click **Create role**\n10. Open the new role and set **Maximum session duration** to **12 hours**\n11. Copy the **Role ARN** (e.g., `arn:aws:iam::123456789012:role/snowflake_iot_s3_role`)\n\n\u003E **Record:** Save your Role ARN - you'll need this value in the next section.\n\n\u003C!-- ------------------------ --\u003E\n## Create Storage Integrations\n\nNow create the Snowflake storage integrations and generate the trust policy for AWS.\n\n### Step 1: Create Both Storage Integrations\n\nRun the following in Snowflake, replacing `\u003CYOUR_ROLE_ARN\u003E` and `\u003CYOUR_BUCKET\u003E` with your values from the previous section:\n\n```sql\n-- Create Storage Integrations: Step 1 - Create Both Storage Integrations\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nCREATE STORAGE INTEGRATION PG_IOT_S3_INTEGRATION\n    TYPE = POSTGRES_EXTERNAL_STORAGE\n    STORAGE_PROVIDER = 'S3'\n    ENABLED = TRUE\n    STORAGE_AWS_ROLE_ARN = '\u003CYOUR_ROLE_ARN\u003E'\n    STORAGE_ALLOWED_LOCATIONS = ('s3://\u003CYOUR_BUCKET\u003E/iot/');\n\nCREATE STORAGE INTEGRATION SF_IOT_S3_INTEGRATION\n    TYPE = EXTERNAL_STAGE\n    STORAGE_PROVIDER = 'S3'\n    ENABLED = TRUE\n    STORAGE_AWS_ROLE_ARN = '\u003CYOUR_ROLE_ARN\u003E'\n    STORAGE_ALLOWED_LOCATIONS = ('s3://\u003CYOUR_BUCKET\u003E/iot/');\n```\n\n### Step 2: Get Integration Details\n\nRetrieve the IAM user ARN and external ID from each integration:\n\n```sql\n-- Create Storage Integrations: Step 2 - Get Integration Details (Postgres)\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nDESCRIBE INTEGRATION PG_IOT_S3_INTEGRATION;\n```\n\nRecord the values for:\n- `STORAGE_AWS_IAM_USER_ARN`\n- `STORAGE_AWS_EXTERNAL_ID`\n\n```sql\n-- Create Storage Integrations: Step 2 - Get Integration Details (Snowflake)\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nDESCRIBE INTEGRATION SF_IOT_S3_INTEGRATION;\n```\n\nRecord these values as well (they will be different from the Postgres integration).\n\n### Step 3: Update IAM Role Trust Policy\n\nReturn to AWS Console → **IAM** → **Roles** → `snowflake_iot_s3_role` → **Trust relationships** → **Edit trust policy**.\n\nReplace the entire policy with the following, substituting the actual values from Step 2:\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    {\n      \"Sid\": \"PostgresIntegration\",\n      \"Effect\": \"Allow\",\n      \"Principal\": { \"AWS\": \"\u003CPG_STORAGE_AWS_IAM_USER_ARN\u003E\" },\n      \"Action\": \"sts:AssumeRole\",\n      \"Condition\": { \"StringEquals\": { \"sts:ExternalId\": \"\u003CPG_STORAGE_AWS_EXTERNAL_ID\u003E\" } }\n    },\n    {\n      \"Sid\": \"SnowflakeIntegration\",\n      \"Effect\": \"Allow\",\n      \"Principal\": { \"AWS\": \"\u003CSF_STORAGE_AWS_IAM_USER_ARN\u003E\" },\n      \"Action\": \"sts:AssumeRole\",\n      \"Condition\": { \"StringEquals\": { \"sts:ExternalId\": \"\u003CSF_STORAGE_AWS_EXTERNAL_ID\u003E\" } }\n    }\n  ]\n}\n```\n\nClick **Update policy**.\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake Setup\n\nCreate the Snowflake database objects that will receive data from Postgres and store anomaly detection results.\n\n### Step 1: Create Database and Warehouse\n\n```sql\n-- Snowflake Setup: Step 1 - Create Database and Warehouse\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\n\nCREATE DATABASE IF NOT EXISTS IOT_LAB;\nCREATE SCHEMA IF NOT EXISTS IOT_LAB.SENSORS;\n\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCREATE WAREHOUSE IF NOT EXISTS IOT_WH\n    WAREHOUSE_SIZE = 'XSMALL'\n    AUTO_SUSPEND = 60\n    AUTO_RESUME = TRUE;\n\nUSE WAREHOUSE IOT_WH;\n```\n\n### Step 2: Create File Format and Stage\n\nReplace `\u003CBUCKET_NAME\u003E` with your bucket.\n\n```sql\n-- Snowflake Setup: Step 2 - Create File Format and Stage\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCREATE OR REPLACE FILE FORMAT CSV_FORMAT_IOT\n    TYPE = 'CSV'\n    FIELD_OPTIONALLY_ENCLOSED_BY = '\"'\n    SKIP_HEADER = 1\n    NULL_IF = ('NULL', 'null', '');\n\nCREATE OR REPLACE STAGE IOT_STAGE\n    STORAGE_INTEGRATION = SF_IOT_S3_INTEGRATION\n    URL = 's3://\u003CBUCKET_NAME\u003E/iot/'\n    FILE_FORMAT = CSV_FORMAT_IOT;\n```\n\n### Step 3: Create Tables\n\n```sql\n-- Snowflake Setup: Step 3 - Create Tables\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCREATE OR REPLACE TABLE SENSOR_READINGS (\n    READING_ID       INT,\n    SENSOR_NAME      VARCHAR(50),\n    SENSOR_TYPE      VARCHAR(20),\n    READING_TS       TIMESTAMP_NTZ,\n    VALUE            NUMBER(10,2),\n    UNIT             VARCHAR(10),\n    LOADED_AT        TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n);\n\nCREATE OR REPLACE TABLE SENSOR_ANOMALIES (\n    ANOMALY_ID       INT AUTOINCREMENT PRIMARY KEY,\n    SENSOR_NAME      VARCHAR(50),\n    SENSOR_TYPE      VARCHAR(20),\n    READING_TS       TIMESTAMP_NTZ,\n    PREDICTED_VALUE  NUMBER(10,2),\n    ACTUAL_VALUE     NUMBER(10,2),\n    ANOMALY_SCORE    NUMBER(5,4),\n    AI_EXPLANATION   VARCHAR(1000),\n    DETECTED_AT      TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n);\n```\n\n### Step 4: Create Stream for Change Capture\n\nStreams in Snowflake track changes to tables, enabling efficient incremental processing. This stream will capture new anomalies for export back to Postgres.\n\n```sql\n-- Snowflake Setup: Step 4 - Create Stream for Change Capture\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCREATE OR REPLACE STREAM ANOMALIES_STREAM ON TABLE SENSOR_ANOMALIES\n    APPEND_ONLY = TRUE;\n```\n\n### Step 5: Verify Stage Access\n\n```sql\n-- Snowflake Setup: Step 5 - Verify Stage Access\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nLIST @IOT_STAGE/;\n```\n\nThis should return an empty result (no files yet) without errors, confirming S3 access is working.\n\n\u003C!-- ------------------------ --\u003E\n## Postgres Instance Setup\n\nCreate a Snowflake Postgres instance with pg_lake enabled.\n\n### Step 1: Create Network Policy\n\nSnowflake Postgres requires a network policy to allow client connections.  Replace `nnn.nnn.nnn.nnn/32` with specific IP (or subnet) and CIDR for your organization.\n\n```sql\n-- Postgres Instance Setup: Step 1 - Create Network Policy\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS PG_NETWORK_DB;\nCREATE SCHEMA IF NOT EXISTS PG_NETWORK_DB.PG_NETWORK;\n\nUSE SCHEMA PG_NETWORK_DB.PG_NETWORK;\n\n-- Get current IP if needed for Network Rule.  \n-- This value maybe used for `nnn.nnn.nnn.nnn` to limit access to your connection.\nSELECT CURRENT_IP_ADDRESS();\n\nCREATE OR REPLACE NETWORK RULE PG_IOT_INGRESS_RULE\n    TYPE = IPV4\n    VALUE_LIST = ('nnn.nnn.nnn.nnn/32')\n    MODE = POSTGRES_INGRESS\n    COMMENT = 'Allow Postgres client connections (restrict in production)';\n\nCREATE OR REPLACE NETWORK POLICY PG_IOT_NETWORK_POLICY\n    ALLOWED_NETWORK_RULE_LIST = ('PG_IOT_INGRESS_RULE')\n    COMMENT = 'Network policy for Snowflake Postgres instances';\n```\n\n### Step 2: Create Postgres Instance\n\n```sql\n-- Postgres Instance Setup: Step 2 - Create Postgres Instance\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nCREATE POSTGRES INSTANCE IOT_PG\n    AUTHENTICATION_AUTHORITY = POSTGRES\n    COMPUTE_FAMILY = 'STANDARD_L'\n    STORAGE_SIZE_GB = 200\n    POSTGRES_VERSION = 18\n    HIGH_AVAILABILITY = FALSE\n    NETWORK_POLICY = 'PG_IOT_NETWORK_POLICY'\n    STORAGE_INTEGRATION = PG_IOT_S3_INTEGRATION\n    COMMENT = 'IoT Lab Postgres instance for pg_lake demos';\n```\n\n\u003E **Important:** Save the user and password displayed in the `access_roles` field and the `host` - you'll need it to connect.\n\n### Step 3: Monitor Instance Status\n\nWait for the instance to reach READY state:\n\n```sql\n-- Postgres Instance Setup: Step 3 - Monitor Instance Status\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nSHOW POSTGRES INSTANCES;\n\nDESCRIBE POSTGRES INSTANCE IOT_PG;\n```\n\nRecord the **host** value from the DESCRIBE output.\n\n### Step 4: Configure psql Connection\n\nSet environment variables in your terminal:\n\n```bash\nexport PGHOST=\u003Chostname from DESCRIBE output\u003E\nexport PGPORT=5432\nexport PGDATABASE=postgres\nexport PGUSER=snowflake_admin\nexport PGPASSWORD=\u003Cpassword from CREATE output\u003E\nexport PGSSLMODE=require\n```\n\nTest the connection:\n\n```bash\npsql -c \"SELECT version();\"\n```\n\n\u003C!-- ------------------------ --\u003E\n## Postgres Setup\n\nCreate the IoT sensor tables and generate sample data in Postgres.\n\n### Step 1: Enable Extensions\n\n[psql](https://www.postgresql.org/docs/current/app-psql.html) is the interactive terminal for PostgreSQL, allowing you to enter queries, execute SQL commands, and manage the database from the command line.\n\nConnect to Postgres via `psql` and enable pg_lake:\n\n```bash\npsql\n```\n\n```sql\n-- Postgres Setup: Step 1 - Enable Extensions\n-- Execute in: psql (Postgres)\nCREATE EXTENSION IF NOT EXISTS pg_lake CASCADE;\nCREATE EXTENSION IF NOT EXISTS pg_cron;\nCREATE EXTENSION IF NOT EXISTS pg_incremental;\n```\n\nThe `CASCADE` option automatically creates required dependencies like the `pg_lake` foreign data wrapper server.\n\n### Step 2: Create Sensor Readings Table\n\n```sql\n-- Postgres Setup: Step 2 - Create Sensor Readings Table\n-- Execute in: psql (Postgres)\nCREATE TABLE sensor_readings (\n    reading_id   BIGSERIAL PRIMARY KEY,\n    sensor_name  TEXT NOT NULL,\n    sensor_type  TEXT NOT NULL,\n    reading_ts   TIMESTAMP NOT NULL,\n    value        NUMERIC(10,2) NOT NULL,\n    unit         TEXT NOT NULL,\n    created_at   TIMESTAMP DEFAULT now()\n);\n\nCREATE INDEX idx_readings_ts ON sensor_readings (reading_ts);\nCREATE INDEX idx_readings_sensor ON sensor_readings (sensor_name, reading_ts);\n```\n\n### Step 3: Generate Historical Sensor Data\n\nThis generates approximately 100,000 readings from 10 sensors over several months:\n\n```sql\n-- Postgres Setup: Step 3 - Generate Historical Sensor Data\n-- Execute in: psql (Postgres)\nINSERT INTO sensor_readings (sensor_name, sensor_type, reading_ts, value, unit)\nSELECT   sensor_name,\n         sensor_type,\n         ts,\n         CASE sensor_type\n             WHEN 'temperature' THEN 68 + (random() * 12)::numeric(10,2)\n             WHEN 'power'       THEN 100 + (random() * 400)::numeric(10,2)\n         END AS value,\n         CASE sensor_type\n             WHEN 'temperature' THEN '°F'\n             WHEN 'power'       THEN 'kW'\n         END AS unit\nFROM     (\n             SELECT * FROM (VALUES\n                 ('HVAC-Floor1',     'temperature'),\n                 ('HVAC-Floor2',     'temperature'),\n                 ('HVAC-Floor3',     'temperature'),\n                 ('Freezer-Main',    'temperature'),\n                 ('ServerRoom-A',    'temperature'),\n                 ('MainPanel-A',     'power'),\n                 ('MainPanel-B',     'power'),\n                 ('DataCenter-UPS',  'power'),\n                 ('HVAC-Compressor', 'power'),\n                 ('Elevator-Motor',  'power')\n             ) AS s(sensor_name, sensor_type)\n         ) sensors\nCROSS JOIN generate_series(1, 10000) AS n\nCROSS JOIN LATERAL (\n    SELECT now() - (n * interval '15 minutes') AS ts\n) timestamps;\n```\n\n### Step 4: Verify Data Load\n\n```sql\n-- Postgres Setup: Step 4 - Verify Data Load\n-- Execute in: psql (Postgres)\nSELECT   sensor_type,\n         count(*) AS readings,\n         round(avg(value), 2) AS avg_value,\n         min(reading_ts) AS earliest,\n         max(reading_ts) AS latest\nFROM     sensor_readings\nGROUP BY sensor_type\nORDER BY sensor_type;\n```\n\nYou should see approximately 50,000 readings per sensor type.\n\n### Step 5: Create Anomalies Table\n\nThis table will receive AI-enriched anomaly data from Snowflake:\n\n```sql\n-- Postgres Setup: Step 5 - Create Anomalies Table\n-- Execute in: psql (Postgres)\nCREATE TABLE sensor_anomalies (\n    anomaly_id       BIGSERIAL PRIMARY KEY,\n    sensor_name      TEXT NOT NULL,\n    sensor_type      TEXT NOT NULL,\n    reading_ts       TIMESTAMP NOT NULL,\n    predicted_value  NUMERIC(10,2),\n    actual_value     NUMERIC(10,2),\n    anomaly_score    NUMERIC(5,4),\n    ai_explanation   TEXT,\n    detected_at      TIMESTAMP DEFAULT now()\n);\n```\n\n### Step 6: Create Foreign Table for Sync Files\n\nThis foreign table will read anomaly files exported from Snowflake (replacing `\u003CYOUR_BUCKET\u003E` with your value):\n\n```sql\n-- Postgres Setup: Step 6 - Create Foreign Table for Sync Files\n-- Execute in: psql (Postgres)\nCREATE FOREIGN TABLE sync_anomalies (\n    sensor_name      TEXT,\n    sensor_type      TEXT,\n    reading_ts       TEXT,\n    predicted_value  TEXT,\n    actual_value     TEXT,\n    anomaly_score    TEXT,\n    ai_explanation   TEXT,\n    _filename        TEXT\n)\nSERVER pg_lake\nOPTIONS (\n    header 'true',\n    format 'csv',\n    filename 'true',\n    compression 'gzip',\n    quote '\"',    \n    path 's3://\u003CYOUR_BUCKET\u003E/iot/sync/*'\n);\n```\n\n\u003C!-- ------------------------ --\u003E\n## Export Data from Postgres\n\nUse pg_lake to export sensor data from Postgres to S3. This demonstrates the first half of bidirectional data movement.\n\n### Understanding pg_lake COPY\n\nThe pg_lake extension extends Postgres's standard `COPY` command to support S3 destinations. The syntax is similar to standard COPY but writes directly to cloud storage instead of local files.\n\n### Step 1: Check Data to Export\n\n```sql\n-- Export Data from Postgres: Step 1 - Check Data to Export\n-- Execute in: psql (Postgres)\nSELECT   sensor_type, count(1) AS cnt\nFROM     sensor_readings\nGROUP BY sensor_type;\n```\n\n### Step 2: Export to CSV\n\nExport sensor readings to S3 in compressed CSV format (replacing `\u003CYOUR_BUCKET\u003E` with your value):\n\n```sql\n-- Export Data from Postgres: Step 2 - Export to CSV\n-- Execute in: psql (Postgres)\nCOPY (\n    SELECT   reading_id, sensor_name, sensor_type, reading_ts, value, unit\n    FROM     sensor_readings\n) TO 's3://\u003CYOUR_BUCKET\u003E/iot/export/sensor_readings.csv.gz'\nWITH (header true, compression gzip, format csv);\n```\n\nA sub select was used to show how you could be more selective in the data that was copied.  By using the sub select you could have filtered specific rows, columns, or even joined to another table.  As an alternative, you could have left out the sub select and just used the table name to copy the entire table.\n\n\u003E **Format Note:** pg_lake supports multiple output formats. You can specify the format either through the file extension (`.csv`, `.parquet`, `.json`) or explicitly in the `WITH` clause using `format csv|json|parquet`. Additional options like `compression gzip` can reduce file sizes significantly.\n\n### Step 3: Verify Export\n\nUse `lake_file.list()` to browse S3 contents directly from Postgres (replacing `\u003CYOUR_BUCKET\u003E` with your value):\n\n```sql\n-- Export Data from Postgres: Step 3 - Verify Export\n-- Execute in: psql (Postgres)\nSELECT * FROM lake_file.list('s3://\u003CYOUR_BUCKET\u003E/iot/export/*');\n```\n\nYou should see the exported CSV file with its size and modification timestamp.\n\n\u003C!-- ------------------------ --\u003E\n## Query S3 with Foreign Tables\n\npg_lake enables querying S3 files directly without importing data into Postgres tables. This is useful for ad-hoc analysis of exported data or for building data pipelines.\n\n### Understanding Foreign Tables\n\nForeign tables in pg_lake create a virtual table that reads from S3 on demand. The data stays in S3 - queries execute by fetching and processing the data at query time. This is ideal for:\n- Verifying exported data before loading elsewhere\n- Querying archived data without storage overhead\n- Building lightweight data pipelines\n\n### Step 1: Create CSV Foreign Table\n\nCreate the `readings_csv` foreign table (replacing `\u003CYOUR_BUCKET\u003E` with your value):\n\n```sql\n-- Query S3 with Foreign Tables: Step 1 - Create CSV Foreign Table\n-- Execute in: psql (Postgres)\nCREATE FOREIGN TABLE readings_csv()\nSERVER pg_lake\nOPTIONS (\n    header 'true',\n    format 'csv',\n    filename 'true',\n    path 's3://\u003CYOUR_BUCKET\u003E/iot/export/sensor_readings.csv.gz'\n);\n```\n\nThe empty parentheses `()` tell pg_lake to infer the schema from the file. The `filename 'true'` option adds a `_filename` column showing the source file.\n\n### Step 2: Query the Foreign Table\n\n```sql\n-- Query S3 with Foreign Tables: Step 2 - Query the Foreign Table\n-- Execute in: psql (Postgres)\nSELECT * FROM readings_csv LIMIT 10;\n```\n\nThis reads directly from S3 - no data is stored in Postgres.\n\n### Step 3: Aggregate Data from S3\n\nForeign tables support standard SQL operations:\n\n```sql\n-- Query S3 with Foreign Tables: Step 3 - Aggregate Data from S3\n-- Execute in: psql (Postgres)\nSELECT   sensor_type, \n         count(*) AS readings, \n         round(avg(value::numeric), 2) AS avg_value\nFROM     readings_csv\nGROUP BY sensor_type;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Load Data into Snowflake\n\nLoad the exported sensor data from S3 into Snowflake for analytics processing.\n\n### Step 1: Verify Files in Stage\n\nIn Snowflake:\n\n```sql\n-- Load Data into Snowflake: Step 1 - Verify Files in Stage\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\nUSE WAREHOUSE IOT_WH;\n\nLIST @IOT_STAGE/export/;\n```\n\nYou should see the CSV file exported from Postgres.\n\n### Step 2: Preview Data\n\nVerify the data format before loading:\n\n```sql\n-- Load Data into Snowflake: Step 2 - Preview Data\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nSELECT   $1, $2, $3, $4, $5, $6\nFROM     @IOT_STAGE/export/sensor_readings.csv.gz\n         (FILE_FORMAT =\u003E 'CSV_FORMAT_IOT')\nLIMIT    10;\n```\n\n### Step 3: Load CSV Data\n\n```sql\n-- Load Data into Snowflake: Step 3 - Load CSV Data\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCOPY INTO SENSOR_READINGS (READING_ID, SENSOR_NAME, SENSOR_TYPE, READING_TS, VALUE, UNIT)\nFROM @IOT_STAGE/export/\nFILE_FORMAT = 'CSV_FORMAT_IOT'\nPATTERN = '.*readings.*\\\\.csv.gz'\nON_ERROR = 'CONTINUE';\n```\n\n\u003E **Automation Note:** For production pipelines, this COPY command can be automated in several ways:\n\u003E - **Snowpipe**: Configure auto-ingest to automatically load new files as they arrive in S3\n\u003E - **Tasks**: Schedule periodic COPY operations using Snowflake Tasks\n\u003E - **Streams + Tasks**: Combine change data capture with scheduled processing for event-driven pipelines\n\u003E\n\u003E See the [Snowpipe Documentation](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro) for auto-ingest configuration.\n\n### Step 4: Verify Load\n\n```sql\n-- Load Data into Snowflake: Step 4 - Verify Load\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nSELECT   SENSOR_TYPE,\n         COUNT(*) AS READINGS,\n         ROUND(AVG(VALUE), 2) AS AVG_VALUE,\n         MIN(READING_TS) AS EARLIEST,\n         MAX(READING_TS) AS LATEST\nFROM     SENSOR_READINGS\nGROUP BY SENSOR_TYPE\nORDER BY SENSOR_TYPE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Anomaly Detection with Cortex AI\n\nUse Snowflake Cortex to detect anomalies and generate AI-powered explanations. This demonstrates enriching operational data with AI capabilities.\n\n### Understanding Cortex Complete\n\nSnowflake Cortex provides access to large language models (LLMs) directly within SQL queries. The `COMPLETE` function sends prompts to an LLM and returns generated text. This enables AI enrichment without external API calls or data movement.\n\n### Step 1: Detect Anomalies and Generate AI Explanations\n\nThis single statement simulates anomalies (using a simplified scoring approach for this lab) and generates natural language explanations using Cortex:\n\n```sql\n-- Anomaly Detection with Cortex AI: Step 1 - Detect Anomalies and Generate AI Explanations\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nINSERT INTO SENSOR_ANOMALIES (SENSOR_NAME, SENSOR_TYPE, READING_TS, PREDICTED_VALUE, ACTUAL_VALUE, ANOMALY_SCORE, AI_EXPLANATION)\nSELECT   SENSOR_NAME, \n         SENSOR_TYPE, \n         READING_TS, \n         ROUND(VALUE * 0.9, 2) AS PREDICTED_VALUE,\n         VALUE AS ACTUAL_VALUE,\n         0.75 AS ANOMALY_SCORE,\n         SNOWFLAKE.CORTEX.COMPLETE('llama3.1-8b',\n             'In 1 sentence, explain this IoT anomaly: ' || SENSOR_NAME || \n             ' (' || SENSOR_TYPE || ') read ' || VALUE || ' ' || UNIT\n         )\nFROM     SENSOR_READINGS\nORDER BY RANDOM()\nLIMIT    100;\n```\n\nThis generates 100 sample anomalies with AI explanations. In production, you would use statistical methods (Z-score, IQR, ML models) for actual anomaly detection.\n\n### Step 2: View AI-Explained Anomalies\n\n```sql\n-- Anomaly Detection with Cortex AI: Step 2 - View AI-Explained Anomalies\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nSELECT   ANOMALY_ID, \n         SENSOR_NAME, \n         SENSOR_TYPE, \n         ANOMALY_SCORE, \n         AI_EXPLANATION, \n         DETECTED_AT\nFROM     SENSOR_ANOMALIES\nORDER BY DETECTED_AT DESC\nLIMIT    10;\n```\n\nEach anomaly now has a human-readable explanation generated by the LLM, making it easier for operators to understand and act on alerts.\n\n\u003C!-- ------------------------ --\u003E\n## Sync Anomalies to Postgres\n\nExport the AI-enriched anomalies back to S3 for Postgres to consume, completing the bidirectional data flow.\n\n### Understanding Streams for Change Capture\n\nThe `ANOMALIES_STREAM` created earlier tracks new rows inserted into `SENSOR_ANOMALIES`. When you query the stream, it returns only the changes since the last consumption, enabling efficient incremental processing.\n\n### Step 1: Check Stream for Changes\n\n```sql\n-- Sync Anomalies to Postgres: Step 1 - Check Stream for Changes\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nSELECT COUNT(1) FROM ANOMALIES_STREAM;\n```\n\nThis shows how many new anomalies are ready for export.\n\n### Step 2: Export to S3\n\n```sql\n-- Sync Anomalies to Postgres: Step 2 - Export to S3\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nCOPY INTO @IOT_STAGE/sync/\nFROM (\n        SELECT SENSOR_NAME, SENSOR_TYPE, READING_TS, PREDICTED_VALUE, \n               ACTUAL_VALUE, ANOMALY_SCORE, AI_EXPLANATION\n        FROM   ANOMALIES_STREAM\n    )\n    FILE_FORMAT = 'CSV_FORMAT_IOT'\n    HEADER = TRUE\n    INCLUDE_QUERY_ID = TRUE\n    SINGLE = FALSE\n    MAX_FILE_SIZE = 16000000\n    OVERWRITE = FALSE;\n```\n\n\u003E **Automation Note:** This COPY command can be wrapped in a Snowflake Task to run automatically on a schedule or when the stream contains data. Example:\n\u003E ```sql\n\u003E CREATE TASK EXPORT_ANOMALIES_TASK\n\u003E     WAREHOUSE = IOT_WH\n\u003E     SCHEDULE = '5 MINUTE'\n\u003E WHEN\n\u003E     SYSTEM$STREAM_HAS_DATA('ANOMALIES_STREAM')\n\u003E AS\n\u003E     COPY INTO @IOT_STAGE/sync/ FROM (...);\n\u003E ```\n\u003E This creates an event-driven pipeline that exports anomalies as soon as they're detected.\n\n### Step 3: Verify Export\n\n```sql\n-- Sync Anomalies to Postgres: Step 3 - Verify Export\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nLIST @IOT_STAGE/sync/;\n```\n\nYou should see one or more CSV files containing the anomaly data.\n\n\u003C!-- ------------------------ --\u003E\n## Receive Anomalies in Postgres\n\nImport the AI-enriched anomaly data back into Postgres, completing the round trip.\n\n### Step 1: Check for Sync Files\n\nIn Postgres, use the list function to check for files from Snowflake:\n\n```sql\n-- Receive Anomalies in Postgres: Step 1 - Check for Sync Files\n-- Execute in: psql (Postgres)\nSELECT * FROM lake_file.list('s3://\u003CYOUR_BUCKET\u003E/iot/sync/*');\n```\n\n### Step 2: Import Anomalies\n\nLoad the anomaly data using the pre-configured foreign table (replacing `\u003CYOUR_BUCKET\u003E` with your value):\n\n```sql\n-- Receive Anomalies in Postgres: Step 2 - Import Anomalies\n-- Execute in: psql (Postgres)\nINSERT INTO sensor_anomalies (sensor_name, sensor_type, reading_ts, predicted_value, actual_value, anomaly_score, ai_explanation)\n(SELECT   sensor_name::text, \n          sensor_type::text, \n          reading_ts::timestamp, \n          predicted_value::numeric(10,2),\n          actual_value::numeric(10,2), \n          anomaly_score::numeric(5,4), \n          ai_explanation::text\nFROM     sync_anomalies\nWHERE    _filename IN (SELECT path FROM lake_file.list('s3://\u003CYOUR_BUCKET\u003E/iot/sync/*')))\nON CONFLICT DO NOTHING;\n```\n\nThe `ON CONFLICT DO NOTHING` clause ensures idempotent processing - running this multiple times won't create duplicates.\n\n### Step 3: View AI-Enriched Anomalies in Postgres\n\n```sql\n-- Receive Anomalies in Postgres: Step 3 - View AI-Enriched Anomalies in Postgres\n-- Execute in: psql (Postgres)\nSELECT   sensor_name, \n         sensor_type, \n         reading_ts, \n         anomaly_score, \n         ai_explanation, \n         detected_at\nFROM     sensor_anomalies\nORDER BY detected_at DESC\nLIMIT    10;\n```\n\nThe anomalies detected in Snowflake with AI explanations are now available in Postgres for operational use.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\n### Postgres Cleanup\n\n```sql\n-- Cleanup: Postgres Cleanup\n-- Execute in: psql (Postgres)\nDROP TABLE IF EXISTS sensor_anomalies CASCADE;\nDROP TABLE IF EXISTS sensor_readings CASCADE;\n\nDROP FOREIGN TABLE IF EXISTS sync_anomalies CASCADE;\nDROP FOREIGN TABLE IF EXISTS readings_csv CASCADE;\n```\n\n### Snowflake Cleanup\n\n```sql\n-- Cleanup: Snowflake Cleanup\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE SYSADMIN;\nUSE SCHEMA IOT_LAB.SENSORS;\n\nDROP STREAM IF EXISTS ANOMALIES_STREAM;\n\nDROP TABLE IF EXISTS SENSOR_ANOMALIES;\nDROP TABLE IF EXISTS SENSOR_READINGS;\n\nDROP STAGE IF EXISTS IOT_STAGE;\nDROP FILE FORMAT IF EXISTS CSV_FORMAT_IOT;\n\nDROP SCHEMA IF EXISTS IOT_LAB.SENSORS;\nDROP DATABASE IF EXISTS IOT_LAB;\nDROP WAREHOUSE IF EXISTS IOT_WH;\n```\n\n### Infrastructure Cleanup (Optional)\n\nTo fully remove all infrastructure including the Postgres instance:\n\n```sql\n-- Cleanup: Infrastructure Cleanup (Optional)\n-- Execute in: Snowsight (Snowflake)\nUSE ROLE ACCOUNTADMIN;\n\nDROP POSTGRES INSTANCE IF EXISTS IOT_PG;\n\nUSE SCHEMA PG_NETWORK_DB.PG_NETWORK;\n\nDROP NETWORK POLICY IF EXISTS PG_IOT_NETWORK_POLICY;\nDROP NETWORK RULE IF EXISTS PG_IOT_INGRESS_RULE;\n\nDROP STORAGE INTEGRATION IF EXISTS PG_IOT_S3_INTEGRATION;\nDROP STORAGE INTEGRATION IF EXISTS SF_IOT_S3_INTEGRATION;\n```\n\n### S3 Cleanup\n\nDelete the following S3 prefixes (replacing `\u003CYOUR_BUCKET\u003E` with your value):\n- `s3://\u003CYOUR_BUCKET\u003E/iot/export/*`\n- `s3://\u003CYOUR_BUCKET\u003E/iot/sync/*`\n\nOptionally delete the S3 bucket and IAM resources.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\n### What You Learned\n\nCongratulations! You have successfully:\n\n- Configured S3 storage integration for both pg_lake and Snowflake\n- Created a Snowflake Postgres instance with pg_lake enabled\n- Exported data from Postgres to S3 in CSV format using pg_lake\n- Queried S3 files directly using pg_lake foreign tables\n- Loaded data into Snowflake using COPY INTO\n- Generated AI-powered anomaly explanations using Snowflake Cortex\n- Synchronized analytical results back to Postgres via S3\n\n### Key Capabilities Demonstrated\n\n| Postgres (pg_lake) | Snowflake |\n|---------------------|-----------|\n| COPY TO S3 (CSV, Parquet, JSON) | External Stage with S3 |\n| Foreign Tables for S3 queries | COPY INTO for data loading |\n| lake_file.list() for S3 browsing | Streams for change capture |\n\n### Related Resources\n\n- [Snowpipe Documentation](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro)\n- [Snowflake Tasks](https://docs.snowflake.com/en/user-guide/tasks-intro)\n- [Snowflake Postgres Documentation](https://docs.snowflake.com/en/user-guide/snowflake-postgres/about)\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-ae8e53f640","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-6a93316f6d",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0aa11a949b","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-19",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-051958fac6","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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Storage Configuration\u003C/h2\u003E","\u003Ch2\u003ECreate Storage Integrations\u003C/h2\u003E","\u003Ch2\u003ESnowflake Setup\u003C/h2\u003E","\u003Ch2\u003EPostgres Instance Setup\u003C/h2\u003E","\u003Ch2\u003EPostgres Setup\u003C/h2\u003E","\u003Ch2\u003EExport Data from Postgres\u003C/h2\u003E","\u003Ch2\u003EQuery S3 with Foreign Tables\u003C/h2\u003E","\u003Ch2\u003ELoad Data into Snowflake\u003C/h2\u003E","\u003Ch2\u003EAnomaly Detection with Cortex AI\u003C/h2\u003E","\u003Ch2\u003ESync Anomalies to Postgres\u003C/h2\u003E","\u003Ch2\u003EReceive Anomalies in Postgres\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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