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-->\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\nPostgres Mirror features are in preview and under rapid development. Some features may not be available.\u003C/strong\u003E<br>\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIf you have operational data in Snowflake Postgres, you can use \u003Cstrong\u003EPostgres Mirror\u003C/strong\u003E to automatically replicate tables into Snowflake for analytics. Mirror uses change data capture (CDC) under the hood to keep Snowflake tables in sync with Postgres — no ETL pipelines, no external tooling, and no manual data movement.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, you will create an operational Postgres database with a simple IoT schema, set up Postgres Mirror to replicate those tables into Snowflake, and verify that new data flows through automatically.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Postgres instance with an operational IoT database (devices, sensors, and readings)\u003C/li\u003E\u003Cli\u003EA Postgres Mirror that replicates selected tables into Snowflake on a schedule\u003C/li\u003E\u003Cli\u003EAn end-to-end sync pipeline that picks up new rows, schema changes, and deletes automatically\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create and connect to a Snowflake Postgres instance\u003C/li\u003E\u003Cli\u003EHow to configure grants required for Postgres Mirror replication\u003C/li\u003E\u003Cli\u003EHow to enable the required extensions (\u003Ccode\u003Epg_lake\u003C/code\u003E and \u003Ccode\u003Esnowflake_cdc\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EHow to create a mirror using SQL or the Snowflake UI\u003C/li\u003E\u003Cli\u003EHow to verify data replication and monitor ongoing sync\u003C/li\u003E\u003Cli\u003EHow schema changes (DDL) propagate automatically through the mirror\u003C/li\u003E\u003Cli\u003EHow deletes replicate from Postgres to Snowflake\u003C/li\u003E\u003Cli\u003EHow to use \u003Ccode\u003E$live\u003C/code\u003E views for sub-minute read latency and \u003Ccode\u003E$changes\u003C/code\u003E for a 7-day audit trail\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a Snowflake account with Snowflake Postgres enabled\u003C/li\u003E\u003Cli\u003EA SQL client capable of connecting to Postgres (e.g., \u003Ccode\u003Epsql\u003C/code\u003E)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate a Postgres Instance\u003C/h2\u003E\n","\u003Cp\u003EStart by creating a new Snowflake Postgres instance. You will need the instance name in a later step when creating the mirror.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Instance\u003C/h3\u003E\n","\u003Cp\u003ECreate a new Snowflake Postgres instance from the Snowflake UI or SQL. If this is your first time, follow the \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-snowflake-postgres/\"\u003EGetting Started with Snowflake Postgres\u003C/a\u003E guide for detailed instructions. Copy and save the \u003Cstrong\u003Einstance name\u003C/strong\u003E — you will need it when setting up the mirror.\u003C/p\u003E\n","\u003Ch3\u003EConnect to the Instance\u003C/h3\u003E\n","\u003Cp\u003EConnect to your Snowflake Postgres instance using \u003Ccode\u003Epsql\u003C/code\u003E or your preferred SQL client:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epsql postgres://<user>:<password>@<instance-host>:5432/postgres\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExisting instances\u003C/h3\u003E\n","\u003Cp\u003EIf you already have an instance created prior to the mirroring feature, you will need to do an instance refresh from the Postgres -- Manage options.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Grants for Replication\u003C/h2\u003E\n","\u003Cp\u003EBefore creating a mirror, you need to grant the required permissions in Snowflake. This grant allow the Snowflake application to administer mirrors and access your Postgres instance.\u003C/p\u003E\n","\u003Cp\u003ERun the following in \u003Cstrong\u003ESnowflake\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT USAGE ON POSTGRES INSTANCE "my-instance" TO APPLICATION snowflake;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReplace \u003Ccode\u003E"my-instance"\u003C/code\u003E with the name of your Postgres instance.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Postgres Tables\u003C/h2\u003E\n","\u003Cp\u003ENow switch to your \u003Cstrong\u003EPostgres\u003C/strong\u003E connection. Create a simple IoT schema with three related tables: devices, sensors, and readings.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TABLE devices (\n    device_id SERIAL PRIMARY KEY,\n    device_name TEXT NOT NULL,\n    location TEXT,\n    created_at TIMESTAMP DEFAULT NOW()\n);\n\nCREATE TABLE sensors (\n    sensor_id SERIAL PRIMARY KEY,\n    device_id INT REFERENCES devices(device_id),\n    sensor_type TEXT NOT NULL, -- e.g., 'Temperature', 'Humidity'\n    unit TEXT\n);\n\nCREATE TABLE readings (\n    reading_id SERIAL PRIMARY KEY,\n    sensor_id INT REFERENCES sensors(sensor_id),\n    value NUMERIC(10, 2),\n    ts TIMESTAMP DEFAULT NOW()\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESeed Sample Data\u003C/h3\u003E\n","\u003Cp\u003EPopulate the tables with sample IoT data — 5 devices, 2 sensors per device, and 485 sensor readings spread over the past several hours.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- 2. Insert 5 Devices\nINSERT INTO devices (device_name, location)\nSELECT \n    'IoT-Gateway-' || i,\n    CASE WHEN i % 2 = 0 THEN 'Warehouse-A' ELSE 'Loading-Dock' END\nFROM generate_series(1, 5) AS i;\n\n-- 3. Insert 10 Sensors (2 for each device)\nINSERT INTO sensors (device_id, sensor_type, unit)\nSELECT \n    d.device_id,\n    s.type,\n    CASE WHEN s.type = 'Temperature' THEN 'Celsius' ELSE 'Percent' END\nFROM devices d\nCROSS JOIN (SELECT unnest(ARRAY['Temperature', 'Humidity']) AS type) AS s;\n\n-- 4. Insert 485 Readings\nINSERT INTO readings (sensor_id, value, ts)\nSELECT \n    (sample_id % 10) + 1, -- Cycles through the 10 sensors\n    (random() * 40 + 10)::numeric(10,2), -- Generates a value between 10 and 50\n    NOW() - (sample_id || ' minutes')::interval -- Offsets time into the past\nFROM generate_series(1, 485) AS sample_id;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify the Data\u003C/h3\u003E\n","\u003Cp\u003EConfirm the data was inserted:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect COUNT(*) from readings;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see 485 rows.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEnable Extensions and Create the Mirror\u003C/h2\u003E\n","\u003Ch3\u003EEnable Extensions\u003C/h3\u003E\n","\u003Cp\u003EInstall \u003Ccode\u003Epg_lake\u003C/code\u003E and \u003Ccode\u003Esnowflake_cdc\u003C/code\u003E on your Postgres instance. These extensions provide the change data capture and object storage capabilities that Postgres Mirror relies on.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE EXTENSION snowflake_cdc CASCADE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Mirror via SQL\u003C/h3\u003E\n","\u003Cp\u003EYou can create a mirror using the \u003Ccode\u003Esnowflake.postgres.create_mirror\u003C/code\u003E procedure. This tells Snowflake which Postgres tables to replicate and how often to sync.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The target database in Snowflake must not already exist — the mirror will create it automatically.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ESwitch to \u003Cstrong\u003ESnowflake\u003C/strong\u003E and run the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL snowflake.postgres.create_mirror(\n    mirror_name         => 'iot_mirror',\n    postgres_instance   => 'mirror-test-sql',\n    postgres_database   => 'postgres',\n    target_database     => 'POSTGRESMIRRORTOSNOWFLAKE',\n    postgres_tables     => ['public.devices', 'public.sensors', 'public.readings'],\n    postgres_schemas    => NULL,\n    refresh_interval    => '1 minute'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReplace \u003Ccode\u003Epostgres_instance\u003C/code\u003E with your instance. Mirror name and target database are names you choose.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Mirror via UI\u003C/h3\u003E\n","\u003Cp\u003EYou can also create a mirror from the Snowflake UI. Navigate to your Postgres instance and select \u003Cstrong\u003EManage\u003C/strong\u003E to configure mirroring.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-mirror-to-snowflake/postgres-mirror-creation-screen.png?v=07faadcb\" alt=\"Postgres Mirror Creation Screen\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThere is also a Mirroring tab on the Postgres landing page . This may take a few minutes to refresh after your initial mirror creation, you can do a hard refresh.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfirm Data in Snowflake\u003C/h2\u003E\n","\u003Cp\u003EOnce the mirror is created and the initial sync completes, run the following in \u003Cstrong\u003ESnowflake\u003C/strong\u003E to verify that the data has arrived.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see 485 rows, matching the count from Postgres.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdd More Data and Monitor Sync\u003C/h2\u003E\n","\u003Cp\u003ENow test that ongoing changes replicate automatically. Switch back to \u003Cstrong\u003EPostgres\u003C/strong\u003E and insert 500 additional readings with varied sensor data spread over the last 24 hours.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Insert 500 additional readings with varied logic\nINSERT INTO readings (sensor_id, value, ts)\nSELECT \n    s.sensor_id,\n    CASE \n        WHEN s.sensor_type = 'Temperature' THEN (20 + (random() * 15))::numeric(10,2) -- Temp: 20-35°C\n        ELSE (40 + (random() * 50))::numeric(10,2)                                -- Humidity: 40-90%\n    END as value,\n    -- Spreads the data over the last 24 hours\n    NOW() - (random() * (24 * 60) * '1 minute'::interval) as ts\nFROM sensors s\nCROSS JOIN generate_series(1, 50) -- 10 sensors * 50 iterations = 500 rows\nORDER BY random();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify in Postgres\u003C/h3\u003E\n","\u003Cp\u003EConfirm the new total in Postgres:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect COUNT(*) from readings;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see 985 rows (485 original + 500 new).\u003C/p\u003E\n","\u003Ch3\u003EVerify in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EWait about a minute for the mirror to sync, then check the count in Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe count should match 985. From this point on, any inserts, updates, or deletes in Postgres will automatically replicate to Snowflake on the configured refresh interval.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESchema Changes Through the Mirror\u003C/h2\u003E\n","\u003Cp\u003EPostgres Mirror supports \u003Cstrong\u003Eschema evolution\u003C/strong\u003E — DDL changes on the source automatically propagate to the Snowflake target without reconfiguring the mirror.\u003C/p\u003E\n","\u003Ch3\u003EAdd a Column\u003C/h3\u003E\n","\u003Cp\u003EAdd a \u003Ccode\u003Estatus\u003C/code\u003E column to the \u003Ccode\u003Edevices\u003C/code\u003E table in \u003Cstrong\u003EPostgres\u003C/strong\u003E. Tables tracked by \u003Ccode\u003Esnowflake_cdc\u003C/code\u003E don't allow \u003Ccode\u003EADD COLUMN\u003C/code\u003E with a \u003Ccode\u003EDEFAULT\u003C/code\u003E clause, so add the column first, then populate existing rows:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE devices ADD COLUMN status TEXT;\nUPDATE devices SET status = 'active';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERename a Column\u003C/h3\u003E\n","\u003Cp\u003ERename the \u003Ccode\u003Elocation\u003C/code\u003E column to \u003Ccode\u003Esite_location\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE devices RENAME COLUMN location TO site_location;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Schema Changes in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EWait about a minute, then confirm the updated schema in \u003Cstrong\u003ESnowflake\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDESCRIBE TABLE POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.DEVICES;\nSELECT * FROM IOT_TEST3_MIRROR.PUBLIC.DEVICES LIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the new \u003Ccode\u003ESTATUS\u003C/code\u003E column and the renamed \u003Ccode\u003ESITE_LOCATION\u003C/code\u003E column. The mirror handles these DDL changes automatically — no need to drop and recreate anything.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Deletes\u003C/h2\u003E\n","\u003Cp\u003EPostgres Mirror replicates deletes in addition to inserts and updates. Test this by removing some rows from the source.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Tables must have a primary key to support \u003Ccode\u003EUPDATE\u003C/code\u003E and \u003Ccode\u003EDELETE\u003C/code\u003E replication. The tables in this quickstart already have primary keys defined.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EDelete Rows in Postgres\u003C/h3\u003E\n","\u003Cp\u003ERemove all readings older than 12 hours in \u003Cstrong\u003EPostgres\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDELETE FROM readings WHERE ts < NOW() - INTERVAL '12 hours';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck the remaining count:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM readings;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Deletes in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EAfter the next refresh cycle, the Snowflake target table should reflect the same count:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe deleted rows are gone from the target table. If you need to see what was deleted, the \u003Ccode\u003E$changes\u003C/code\u003E table retains a 7-day history of all changes including deletes (covered in the next section).\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery $live and $changes\u003C/h2\u003E\n","\u003Cp\u003EEvery mirrored table has two companion objects that give you more visibility into the data:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003E$live\u003C/code\u003E\u003C/strong\u003E — A view that combines the target table with not-yet-merged changes, giving sub-minute read lag regardless of the \u003Ccode\u003Erefresh_interval\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003E$changes\u003C/code\u003E\u003C/strong\u003E — A rolling 7-day change feed showing every insert, update, and delete as queryable rows.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EUse $live for Low-Latency Reads\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E$live\u003C/code\u003E view lets you query the latest committed state from Postgres without waiting for the next apply run. Insert a new device in \u003Cstrong\u003EPostgres\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO devices (device_name, site_location, status)\nVALUES ('IoT-Gateway-Urgent', 'Emergency-Bay', 'active');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EImmediately query \u003Ccode\u003E$live\u003C/code\u003E in \u003Cstrong\u003ESnowflake\u003C/strong\u003E — you should see the new row within ~30 seconds, even before the next scheduled refresh:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.DEVICES$live\nORDER BY CREATED_AT DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECompare this with the target table, which only updates on the refresh interval:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.DEVICES;\nSELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.DEVICES$live;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA gap between these counts indicates changes are pending and will be merged on the next apply run.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E \u003Ccode\u003E$live\u003C/code\u003E views are not transactional. Because \u003Ccode\u003E$changes\u003C/code\u003E rows arrive incrementally, \u003Ccode\u003E$live\u003C/code\u003E can expose a partial transaction. For transactional consistency, query the target tables directly.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EUse $changes for Audit and Change History\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E$changes\u003C/code\u003E table exposes every row-level change with metadata columns. Query recent changes to the \u003Ccode\u003Ereadings\u003C/code\u003E table in \u003Cstrong\u003ESnowflake\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    _commit_time,\n    _change_type,\n    _is_update,\n    READING_ID,\n    SENSOR_ID,\n    VALUE\nFROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS$changes\nORDER BY _commit_lsn DESC, _lsn DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey columns in \u003Ccode\u003E$changes\u003C/code\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003E_change_type\u003C/code\u003E: \u003Ccode\u003EI\u003C/code\u003E for insert, \u003Ccode\u003ED\u003C/code\u003E for delete. Updates appear as a \u003Ccode\u003ED\u003C/code\u003E/\u003Ccode\u003EI\u003C/code\u003E pair.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E_is_update\u003C/code\u003E: \u003Ccode\u003Etrue\u003C/code\u003E on the insert half of an update, \u003Ccode\u003Efalse\u003C/code\u003E on pure inserts.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E_commit_time\u003C/code\u003E: Timestamp of the source transaction commit.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E_data_version\u003C/code\u003E: Increments on \u003Ccode\u003ETRUNCATE\u003C/code\u003E or primary key changes — a signal to reset any downstream bookmarks.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESee the Deletes from Earlier\u003C/h3\u003E\n","\u003Cp\u003EYou can confirm the deletes you ran earlier show up in the change feed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT _change_type, COUNT(*)\nFROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS$changes\nGROUP BY _change_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see \u003Ccode\u003ED\u003C/code\u003E rows corresponding to the readings you deleted, and \u003Ccode\u003EI\u003C/code\u003E rows for all the inserts.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EWhen you're done experimenting, clean up the resources created in this quickstart.\u003C/p\u003E\n","\u003Ch3\u003EDrop the Mirror\u003C/h3\u003E\n","\u003Cp\u003EDrop the mirror from \u003Cstrong\u003ESnowflake\u003C/strong\u003E. Use \u003Ccode\u003ECASCADE\u003C/code\u003E to also delete the target database:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SNOWFLAKE.POSTGRES.DROP_MIRROR('iot_mirror', CASCADE => TRUE);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you omit \u003Ccode\u003ECASCADE\u003C/code\u003E, the target database is left in place and remains queryable, but it will no longer receive updates.\u003C/p\u003E\n","\u003Ch3\u003EDelete the Postgres Instance\u003C/h3\u003E\n","\u003Cp\u003EDelete the Snowflake Postgres instance from Snowsight or SQL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP POSTGRES INSTANCE "my-instance";\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReplace \u003Ccode\u003E"my-instance"\u003C/code\u003E with the name of your instance.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003ECongratulations!\u003C/h3\u003E\n","\u003Cp\u003EYou have successfully set up Postgres Mirror to replicate operational data from Snowflake Postgres into Snowflake — with no external ETL pipeline required. New changes in Postgres are automatically captured and synced on a schedule, including schema changes and deletes.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create a Snowflake Postgres instance and connect to it\u003C/li\u003E\u003Cli\u003EHow to configure the grants required for Postgres Mirror\u003C/li\u003E\u003Cli\u003EHow to enable the \u003Ccode\u003Epg_lake\u003C/code\u003E and \u003Ccode\u003Esnowflake_cdc\u003C/code\u003E extensions\u003C/li\u003E\u003Cli\u003EHow to create a mirror using SQL or the Snowflake UI\u003C/li\u003E\u003Cli\u003EHow to verify initial replication and monitor ongoing sync\u003C/li\u003E\u003Cli\u003EHow schema changes (add/rename/alter columns) propagate automatically\u003C/li\u003E\u003Cli\u003EHow deletes replicate through the mirror\u003C/li\u003E\u003Cli\u003EHow to use \u003Ccode\u003E$live\u003C/code\u003E for sub-minute read latency\u003C/li\u003E\u003Cli\u003EHow to query \u003Ccode\u003E$changes\u003C/code\u003E for a 7-day audit trail of all row-level changes\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-snowflake-postgres/\"\u003EGetting Started with Snowflake Postgres\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-pg_lake\"\u003Epg_lake for Snowflake Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/pg-lake-postgres-lakehouse-integration/\"\u003EIntroducing pg_lake: Integrate Your Data Lakehouse with Postgres\u003C/a\u003E (blog)\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Mirror Data from Snowflake Postgres to Snowflake",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n\u003E**Note:\n\u003EPostgres Mirror features are in preview and under rapid development. Some features may not be available.**\u003Cbr\u003E\n\nIf you have operational data in Snowflake Postgres, you can use **Postgres Mirror** to automatically replicate tables into Snowflake for analytics. Mirror uses change data capture (CDC) under the hood to keep Snowflake tables in sync with Postgres — no ETL pipelines, no external tooling, and no manual data movement.\n\nIn this quickstart, you will create an operational Postgres database with a simple IoT schema, set up Postgres Mirror to replicate those tables into Snowflake, and verify that new data flows through automatically.\n\n### What You Will Build\n- A Snowflake Postgres instance with an operational IoT database (devices, sensors, and readings)\n- A Postgres Mirror that replicates selected tables into Snowflake on a schedule\n- An end-to-end sync pipeline that picks up new rows, schema changes, and deletes automatically\n\n### What You Will Learn\n- How to create and connect to a Snowflake Postgres instance\n- How to configure grants required for Postgres Mirror replication\n- How to enable the required extensions (`pg_lake` and `snowflake_cdc`)\n- How to create a mirror using SQL or the Snowflake UI\n- How to verify data replication and monitor ongoing sync\n- How schema changes (DDL) propagate automatically through the mirror\n- How deletes replicate from Postgres to Snowflake\n- How to use `$live` views for sub-minute read latency and `$changes` for a 7-day audit trail\n\n### Prerequisites\n- Access to a Snowflake account with Snowflake Postgres enabled\n- A SQL client capable of connecting to Postgres (e.g., `psql`)\n\n\u003C!-- ------------------------ --\u003E\n## Create a Postgres Instance\n\nStart by creating a new Snowflake Postgres instance. You will need the instance name in a later step when creating the mirror.\n\n### Create the Instance\nCreate a new Snowflake Postgres instance from the Snowflake UI or SQL. If this is your first time, follow the [Getting Started with Snowflake Postgres](https://www.snowflake.com/en/developers/guides/getting-started-with-snowflake-postgres/) guide for detailed instructions. Copy and save the **instance name** — you will need it when setting up the mirror.\n\n### Connect to the Instance\nConnect to your Snowflake Postgres instance using `psql` or your preferred SQL client:\n\n```bash\npsql postgres://\u003Cuser\u003E:\u003Cpassword\u003E@\u003Cinstance-host\u003E:5432/postgres\n```\n\n### Existing instances\n\nIf you already have an instance created prior to the mirroring feature, you will need to do an instance refresh from the Postgres -- Manage options. \n\n\u003C!-- ------------------------ --\u003E\n## Set Up Grants for Replication\n\nBefore creating a mirror, you need to grant the required permissions in Snowflake. This grant allow the Snowflake application to administer mirrors and access your Postgres instance.\n\nRun the following in **Snowflake**:\n\n```sql\nGRANT USAGE ON POSTGRES INSTANCE \"my-instance\" TO APPLICATION snowflake;\n```\n\nReplace `\"my-instance\"` with the name of your Postgres instance.\n\n\u003C!-- ------------------------ --\u003E\n## Create Postgres Tables\n\nNow switch to your **Postgres** connection. Create a simple IoT schema with three related tables: devices, sensors, and readings.\n\n```sql\nCREATE TABLE devices (\n    device_id SERIAL PRIMARY KEY,\n    device_name TEXT NOT NULL,\n    location TEXT,\n    created_at TIMESTAMP DEFAULT NOW()\n);\n\nCREATE TABLE sensors (\n    sensor_id SERIAL PRIMARY KEY,\n    device_id INT REFERENCES devices(device_id),\n    sensor_type TEXT NOT NULL, -- e.g., 'Temperature', 'Humidity'\n    unit TEXT\n);\n\nCREATE TABLE readings (\n    reading_id SERIAL PRIMARY KEY,\n    sensor_id INT REFERENCES sensors(sensor_id),\n    value NUMERIC(10, 2),\n    ts TIMESTAMP DEFAULT NOW()\n);\n```\n\n### Seed Sample Data\nPopulate the tables with sample IoT data — 5 devices, 2 sensors per device, and 485 sensor readings spread over the past several hours.\n\n```sql\n-- 2. Insert 5 Devices\nINSERT INTO devices (device_name, location)\nSELECT \n    'IoT-Gateway-' || i,\n    CASE WHEN i % 2 = 0 THEN 'Warehouse-A' ELSE 'Loading-Dock' END\nFROM generate_series(1, 5) AS i;\n\n-- 3. Insert 10 Sensors (2 for each device)\nINSERT INTO sensors (device_id, sensor_type, unit)\nSELECT \n    d.device_id,\n    s.type,\n    CASE WHEN s.type = 'Temperature' THEN 'Celsius' ELSE 'Percent' END\nFROM devices d\nCROSS JOIN (SELECT unnest(ARRAY['Temperature', 'Humidity']) AS type) AS s;\n\n-- 4. Insert 485 Readings\nINSERT INTO readings (sensor_id, value, ts)\nSELECT \n    (sample_id % 10) + 1, -- Cycles through the 10 sensors\n    (random() * 40 + 10)::numeric(10,2), -- Generates a value between 10 and 50\n    NOW() - (sample_id || ' minutes')::interval -- Offsets time into the past\nFROM generate_series(1, 485) AS sample_id;\n```\n\n### Verify the Data\nConfirm the data was inserted:\n\n```sql\nselect COUNT(*) from readings;\n```\n\nYou should see 485 rows.\n\n\u003C!-- ------------------------ --\u003E\n## Enable Extensions and Create the Mirror\n\n### Enable Extensions\nInstall `pg_lake` and `snowflake_cdc` on your Postgres instance. These extensions provide the change data capture and object storage capabilities that Postgres Mirror relies on.\n\n```sql\nCREATE EXTENSION snowflake_cdc CASCADE;\n```\n\n### Create the Mirror via SQL\nYou can create a mirror using the `snowflake.postgres.create_mirror` procedure. This tells Snowflake which Postgres tables to replicate and how often to sync.\n\n\u003E **Note:** The target database in Snowflake must not already exist — the mirror will create it automatically.\n\nSwitch to **Snowflake** and run the following:\n\n```sql\nCALL snowflake.postgres.create_mirror(\n    mirror_name         =\u003E 'iot_mirror',\n    postgres_instance   =\u003E 'mirror-test-sql',\n    postgres_database   =\u003E 'postgres',\n    target_database     =\u003E 'POSTGRESMIRRORTOSNOWFLAKE',\n    postgres_tables     =\u003E ['public.devices', 'public.sensors', 'public.readings'],\n    postgres_schemas    =\u003E NULL,\n    refresh_interval    =\u003E '1 minute'\n);\n```\n\nReplace `postgres_instance` with your instance. Mirror name and target database are names you choose. \n\n### Create the Mirror via UI\nYou can also create a mirror from the Snowflake UI. Navigate to your Postgres instance and select **Manage** to configure mirroring.\n\n![Postgres Mirror Creation Screen](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-mirror-to-snowflake/postgres-mirror-creation-screen.png?v=07faadcb)\n\nThere is also a Mirroring tab on the Postgres landing page . This may take a few minutes to refresh after your initial mirror creation, you can do a hard refresh.\n\n\u003C!-- ------------------------ --\u003E\n## Confirm Data in Snowflake\n\nOnce the mirror is created and the initial sync completes, run the following in **Snowflake** to verify that the data has arrived.\n\n```sql\nSELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS;\n```\n\nYou should see 485 rows, matching the count from Postgres.\n\n\u003C!-- ------------------------ --\u003E\n## Add More Data and Monitor Sync\n\nNow test that ongoing changes replicate automatically. Switch back to **Postgres** and insert 500 additional readings with varied sensor data spread over the last 24 hours.\n\n```sql\n-- Insert 500 additional readings with varied logic\nINSERT INTO readings (sensor_id, value, ts)\nSELECT \n    s.sensor_id,\n    CASE \n        WHEN s.sensor_type = 'Temperature' THEN (20 + (random() * 15))::numeric(10,2) -- Temp: 20-35°C\n        ELSE (40 + (random() * 50))::numeric(10,2)                                -- Humidity: 40-90%\n    END as value,\n    -- Spreads the data over the last 24 hours\n    NOW() - (random() * (24 * 60) * '1 minute'::interval) as ts\nFROM sensors s\nCROSS JOIN generate_series(1, 50) -- 10 sensors * 50 iterations = 500 rows\nORDER BY random();\n```\n\n### Verify in Postgres\nConfirm the new total in Postgres:\n\n```sql\nselect COUNT(*) from readings;\n```\n\nYou should see 985 rows (485 original + 500 new).\n\n### Verify in Snowflake\nWait about a minute for the mirror to sync, then check the count in Snowflake:\n\n```sql\nSELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS;\n```\n\nThe count should match 985. From this point on, any inserts, updates, or deletes in Postgres will automatically replicate to Snowflake on the configured refresh interval.\n\n\u003C!-- ------------------------ --\u003E\n## Schema Changes Through the Mirror\n\nPostgres Mirror supports **schema evolution** — DDL changes on the source automatically propagate to the Snowflake target without reconfiguring the mirror.\n\n### Add a Column\n\nAdd a `status` column to the `devices` table in **Postgres**. Tables tracked by `snowflake_cdc` don't allow `ADD COLUMN` with a `DEFAULT` clause, so add the column first, then populate existing rows:\n\n```sql\nALTER TABLE devices ADD COLUMN status TEXT;\nUPDATE devices SET status = 'active';\n```\n\n### Rename a Column\n\nRename the `location` column to `site_location`:\n\n```sql\nALTER TABLE devices RENAME COLUMN location TO site_location;\n```\n\n### Verify Schema Changes in Snowflake\n\nWait about a minute, then confirm the updated schema in **Snowflake**:\n\n```sql\nDESCRIBE TABLE POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.DEVICES;\nSELECT * FROM IOT_TEST3_MIRROR.PUBLIC.DEVICES LIMIT 20;\n```\n\nYou should see the new `STATUS` column and the renamed `SITE_LOCATION` column. The mirror handles these DDL changes automatically — no need to drop and recreate anything.\n\n\u003C!-- ------------------------ --\u003E\n## Data Deletes\n\nPostgres Mirror replicates deletes in addition to inserts and updates. Test this by removing some rows from the source.\n\n\u003E **Note:** Tables must have a primary key to support `UPDATE` and `DELETE` replication. The tables in this quickstart already have primary keys defined.\n\n### Delete Rows in Postgres\n\nRemove all readings older than 12 hours in **Postgres**:\n\n```sql\nDELETE FROM readings WHERE ts \u003C NOW() - INTERVAL '12 hours';\n```\n\nCheck the remaining count:\n\n```sql\nSELECT COUNT(*) FROM readings;\n```\n\n### Verify Deletes in Snowflake\n\nAfter the next refresh cycle, the Snowflake target table should reflect the same count:\n\n```sql\nSELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS;\n```\n\nThe deleted rows are gone from the target table. If you need to see what was deleted, the `$changes` table retains a 7-day history of all changes including deletes (covered in the next section).\n\n\u003C!-- ------------------------ --\u003E\n## Query $live and $changes\n\nEvery mirrored table has two companion objects that give you more visibility into the data:\n\n- **`$live`** — A view that combines the target table with not-yet-merged changes, giving sub-minute read lag regardless of the `refresh_interval`.\n- **`$changes`** — A rolling 7-day change feed showing every insert, update, and delete as queryable rows.\n\n### Use $live for Low-Latency Reads\n\nThe `$live` view lets you query the latest committed state from Postgres without waiting for the next apply run. Insert a new device in **Postgres**:\n\n```sql\nINSERT INTO devices (device_name, site_location, status)\nVALUES ('IoT-Gateway-Urgent', 'Emergency-Bay', 'active');\n```\n\nImmediately query `$live` in **Snowflake** — you should see the new row within ~30 seconds, even before the next scheduled refresh:\n\n```sql\nSELECT * FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.DEVICES$live\nORDER BY CREATED_AT DESC\nLIMIT 5;\n```\n\nCompare this with the target table, which only updates on the refresh interval:\n\n```sql\nSELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.DEVICES;\nSELECT COUNT(*) FROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.DEVICES$live;\n```\n\nA gap between these counts indicates changes are pending and will be merged on the next apply run.\n\n\u003E **Note:** `$live` views are not transactional. Because `$changes` rows arrive incrementally, `$live` can expose a partial transaction. For transactional consistency, query the target tables directly.\n\n### Use $changes for Audit and Change History\n\nThe `$changes` table exposes every row-level change with metadata columns. Query recent changes to the `readings` table in **Snowflake**:\n\n```sql\nSELECT\n    _commit_time,\n    _change_type,\n    _is_update,\n    READING_ID,\n    SENSOR_ID,\n    VALUE\nFROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS$changes\nORDER BY _commit_lsn DESC, _lsn DESC\nLIMIT 20;\n```\n\nKey columns in `$changes`:\n- `_change_type`: `I` for insert, `D` for delete. Updates appear as a `D`/`I` pair.\n- `_is_update`: `true` on the insert half of an update, `false` on pure inserts.\n- `_commit_time`: Timestamp of the source transaction commit.\n- `_data_version`: Increments on `TRUNCATE` or primary key changes — a signal to reset any downstream bookmarks.\n\n### See the Deletes from Earlier\n\nYou can confirm the deletes you ran earlier show up in the change feed:\n\n```sql\nSELECT _change_type, COUNT(*)\nFROM POSTGRESMIRRORTOSNOWFLAKE.PUBLIC.READINGS$changes\nGROUP BY _change_type;\n```\n\nYou should see `D` rows corresponding to the readings you deleted, and `I` rows for all the inserts.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nWhen you're done experimenting, clean up the resources created in this quickstart.\n\n### Drop the Mirror\n\nDrop the mirror from **Snowflake**. Use `CASCADE` to also delete the target database:\n\n```sql\nCALL SNOWFLAKE.POSTGRES.DROP_MIRROR('iot_mirror', CASCADE =\u003E TRUE);\n```\n\nIf you omit `CASCADE`, the target database is left in place and remains queryable, but it will no longer receive updates.\n\n### Delete the Postgres Instance\n\nDelete the Snowflake Postgres instance from Snowsight or SQL:\n\n```sql\nDROP POSTGRES INSTANCE \"my-instance\";\n```\n\nReplace `\"my-instance\"` with the name of your instance.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\n### Congratulations!\nYou have successfully set up Postgres Mirror to replicate operational data from Snowflake Postgres into Snowflake — with no external ETL pipeline required. New changes in Postgres are automatically captured and synced on a schedule, including schema changes and deletes.\n\n### What You Learned\n- How to create a Snowflake Postgres instance and connect to it\n- How to configure the grants required for Postgres Mirror\n- How to enable the `pg_lake` and `snowflake_cdc` extensions\n- How to create a mirror using SQL or the Snowflake UI\n- How to verify initial replication and monitor ongoing sync\n- How schema changes (add/rename/alter columns) propagate automatically\n- How deletes replicate through the mirror\n- How to use `$live` for sub-minute read latency\n- How to query `$changes` for a 7-day audit trail of all row-level changes\n\n### Related Resources\n- [Getting Started with Snowflake Postgres](https://www.snowflake.com/en/developers/guides/getting-started-with-snowflake-postgres/)\n- [pg_lake for Snowflake Documentation](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-pg_lake)\n- [Introducing pg_lake: Integrate Your Data Lakehouse with Postgres](https://www.snowflake.com/en/engineering-blog/pg-lake-postgres-lakehouse-integration/) (blog)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b155dcb3c4","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-97de66e9c5",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-11b0c841ee","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-07-13",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-f204d92306","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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a Postgres Instance\u003C/h2\u003E","\u003Ch2\u003ESet Up Grants for Replication\u003C/h2\u003E","\u003Ch2\u003ECreate Postgres Tables\u003C/h2\u003E","\u003Ch2\u003EEnable Extensions and Create the Mirror\u003C/h2\u003E","\u003Ch2\u003EConfirm Data in Snowflake\u003C/h2\u003E","\u003Ch2\u003EAdd More Data and Monitor Sync\u003C/h2\u003E","\u003Ch2\u003ESchema Changes Through the Mirror\u003C/h2\u003E","\u003Ch2\u003EData Deletes\u003C/h2\u003E","\u003Ch2\u003EQuery $live and $changes\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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