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Replicate operational data into Snowflake for analytics, AI, reporting, and downstream applications. \u003Cstrong\u003EGenerally available.\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake to Postgres:\u003C/strong\u003E Replicate Snowflake tables into Snowflake Postgres for low-latency application reads and Postgres-compatible workloads. \u003Cstrong\u003EPrivate Preview.\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EChoose the direction that matches where your source data lives and where you need to use it.\u003C/p\u003E\n","\u003Ch2\u003EPostgres to Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EKeep analytics and AI current with operational data\u003C/h3\u003E\n","\u003Cp\u003EPostgres data mirroring continuously replicates selected tables or schemas from a Snowflake Postgres instance into a Snowflake database. Inserts, updates, deletes, and supported schema changes flow automatically to the target, so your analytical data stays close to the source without a pipeline to build, monitor, or maintain.\u003C/p\u003E\n","\u003Cp\u003EUse Postgres-to-Snowflake mirroring when you want to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERun analytics and AI workloads on current application data.\u003C/li\u003E\u003Cli\u003EReplace custom sync code, batch jobs, or third-party CDC connectors.\u003C/li\u003E\u003Cli\u003EMake operational data available to Snowflake tools while keeping the application workload in Postgres.\u003C/li\u003E\u003Cli\u003EPreserve transaction boundaries across related tables.\u003C/li\u003E\u003Cli\u003EQuery recent row-level changes through the \u003Ccode\u003E$changes\u003C/code\u003E feed.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey capabilities\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENo separate replication service.\u003C/strong\u003E Mirroring is built into Snowflake Postgres and Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELow-lag reads.\u003C/strong\u003E Each mirrored table includes a \u003Ccode\u003E$live\u003C/code\u003E view that combines applied data with pending changes, so committed source changes can be queried without waiting for the next table-apply cycle.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETransactional consistency.\u003C/strong\u003E Changes from a source transaction become visible together on the Snowflake side, helping preserve relationships such as foreign keys across mirrored tables.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema evolution.\u003C/strong\u003E Supported schema changes and additions to schema-level replication are reflected automatically.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EQueryable change history.\u003C/strong\u003E Each mirrored table has a \u003Ccode\u003E$changes\u003C/code\u003E companion that exposes inserts, updates, and deletes for the previous seven days.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFlexible targets.\u003C/strong\u003E Mirror into standard Snowflake tables or Snowflake-managed Iceberg tables.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOpen foundations.\u003C/strong\u003E Change data is written to Apache Iceberg tables through the open source \u003Ccode\u003Epg_lake\u003C/code\u003E extension.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EHow it works\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/postgres/data-mirroring/image1.png?v=a3909f79\" alt=\"Overview of Postgres data mirroring\"\u003E\u003C/p\u003E\n","\u003Cp\u003EA CDC worker on the Snowflake Postgres source captures changes using standard Postgres logical replication. It writes change records to Iceberg change logs, which Snowflake refreshes and applies to the target database using serverless compute. The target tables and \u003Ccode\u003E$live\u003C/code\u003E views are maintained automatically.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/postgres/data-mirroring/image2.png?v=a3909f79\" alt=\"Postgres-to-Snowflake mirroring architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can create a mirror in Snowsight, with SQL, or by asking Snowflake CoCo. Select individual tables for a curated replication set, or replicate an entire schema so new tables are included automatically.\u003C/p\u003E\n","\u003Cp\u003EUnder the hood, a mirror is built from standard Postgres pieces:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003Epublication\u003C/strong\u003E on the source, created by the \u003Ccode\u003Esnowflake_cdc\u003C/code\u003E extension.\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003Elogical replication slot\u003C/strong\u003E, read by a background CDC worker that decodes the WAL.\u003C/li\u003E\u003Cli\u003EPer-table \u003Cstrong\u003E\u003Ccode\u003E$changes\u003C/code\u003E Iceberg tables\u003C/strong\u003E and a per-mirror \u003Cstrong\u003Emetalog\u003C/strong\u003E, written by the CDC worker through \u003Ccode\u003Epg_lake\u003C/code\u003E. These are the same Iceberg objects on both sides, so you can read them from Postgres or Snowflake.\u003C/li\u003E\u003Cli\u003EOn the Snowflake side, a \u003Cstrong\u003Etarget database\u003C/strong\u003E, a serverless task named \u003Ccode\u003EAPPLY_MIRROR_<mirror_name>\u003C/code\u003E, and per-table \u003Cstrong\u003Etarget tables\u003C/strong\u003E with \u003Cstrong\u003E\u003Ccode\u003E$live\u003C/code\u003E views\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe apply task reads the metalog in commit order and applies each batch in a single Snowflake transaction. If a run fails, it rolls back and the next run resumes from the same position, which gives you exactly-once delivery. Apply uses delete-then-append rather than \u003Ccode\u003EMERGE\u003C/code\u003E, so insert-only batches skip scanning the target table.\u003C/p\u003E\n","\u003Ch3\u003ESet up a mirror with SQL\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Prepare the source instance.\u003C/strong\u003E New Snowflake Postgres instances include the required extensions. Older instances need a refresh from \u003Cstrong\u003EManage\u003C/strong\u003E in Snowsight to pick up the latest software patch. The BURSTABLE tier isn't supported; use STANDARD or HIGH MEMORY.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Grant permissions.\u003C/strong\u003E The Snowsight UI handles this for you. With SQL, grant the mirror admin application role and give the \u003Ccode\u003Esnowflake\u003C/code\u003E application usage on your instance:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT APPLICATION ROLE snowflake.postgres_mirror_admin TO ROLE "my_role";\nGRANT USAGE ON POSTGRES INSTANCE "my_instance" TO APPLICATION snowflake;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Create the mirror.\u003C/strong\u003E List tables with \u003Ccode\u003Epostgres_tables\u003C/code\u003E, whole schemas with \u003Ccode\u003Epostgres_schemas\u003C/code\u003E, or both:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SNOWFLAKE.POSTGRES.CREATE_MIRROR(\n    mirror_name         => 'orders_mirror',\n    postgres_instance   => 'my_instance',\n    postgres_database   => 'postgres',\n    target_database     => 'ORDERS_ANALYTICS',\n    postgres_tables     => ['public.customers', 'public.orders', 'public.order_items'],\n    postgres_schemas    => NULL,\n    refresh_interval    => '1 minute'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Erefresh_interval\u003C/code\u003E defaults to \u003Ccode\u003E'10 minutes'\u003C/code\u003E and can go up to \u003Ccode\u003E'1 day'\u003C/code\u003E. The target database must not already exist; the mirror creates it. To mirror into Snowflake-managed Iceberg tables instead of standard tables, add \u003Ccode\u003Etarget_table_type => 'ICEBERG'\u003C/code\u003E. Add \u003Ccode\u003Eexternal_volume\u003C/code\u003E as well to write those tables to your own storage.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4. Check status and query the data.\u003C/strong\u003E Tables show \u003Ccode\u003ESNAPSHOTTING\u003C/code\u003E during the initial copy and \u003Ccode\u003EREPLICATING\u003C/code\u003E once caught up:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SNOWFLAKE.POSTGRES.LIST_MIRRORED_TABLES('orders_mirror');\n\n-- Applied data, updated every refresh_interval\nSELECT COUNT(*) FROM ORDERS_ANALYTICS.PUBLIC.ORDERS;\n\n-- Applied data plus pending changes, about 30 seconds behind the source\nSELECT COUNT(*) FROM ORDERS_ANALYTICS.PUBLIC.ORDERS$live;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo add or remove tables later, use \u003Ccode\u003EALTER_MIRROR\u003C/code\u003E with \u003Ccode\u003Eadd_tables\u003C/code\u003E/\u003Ccode\u003Eremove_tables\u003C/code\u003E or \u003Ccode\u003Eadd_schemas\u003C/code\u003E/\u003Ccode\u003Eremove_schemas\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EData type mapping\u003C/h3\u003E\n","\u003Cp\u003ESchema, table, and column names are uppercased on the target, so \u003Ccode\u003Ecreated_at\u003C/code\u003E becomes \u003Ccode\u003ECREATED_AT\u003C/code\u003E. Tables whose names differ only by case can't both be mirrored.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPostgres type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELands in Snowflake as\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENotes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eboolean\u003C/code\u003E, \u003Ccode\u003Esmallint\u003C/code\u003E, \u003Ccode\u003Einteger\u003C/code\u003E, \u003Ccode\u003Ebigint\u003C/code\u003E, \u003Ccode\u003Ereal\u003C/code\u003E, \u003Ccode\u003Edouble precision\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENative boolean, integer, and float types\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDirect mapping\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Enumeric(p,s)\u003C/code\u003E with p and s ≤ 38\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EDECIMAL(p,s)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDirect mapping\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnbounded \u003Ccode\u003Enumeric\u003C/code\u003E, or p/s > 38\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDouble\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EValues beyond the \u003Ccode\u003Efloat8\u003C/code\u003E range become ±Infinity\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edate\u003C/code\u003E, \u003Ccode\u003Etime\u003C/code\u003E, \u003Ccode\u003Etimestamp\u003C/code\u003E, \u003Ccode\u003Etimestamptz\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENative date and time types\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etimetz\u003C/code\u003E is normalized to UTC; \u003Ccode\u003Einfinity\u003C/code\u003E values are clamped to the nearest boundary\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Einterval\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStruct (months, days, microseconds)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etext\u003C/code\u003E, \u003Ccode\u003Evarchar\u003C/code\u003E, \u003Ccode\u003Echar\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EString\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EValues over 16 MiB are truncated\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ebytea\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBinary\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EValues over 8 MiB are truncated\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Euuid\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUUID\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETop-level columns only; \u003Ccode\u003Euuid[]\u003C/code\u003E or \u003Ccode\u003Euuid\u003C/code\u003E inside a composite is rejected\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EArrays, composites of supported types\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARRAY\u003C/code\u003E, \u003Ccode\u003EOBJECT\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMultidimensional array values become NULL\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ejson\u003C/code\u003E, \u003Ccode\u003Ejsonb\u003C/code\u003E, \u003Ccode\u003Ehstore\u003C/code\u003E, \u003Ccode\u003Evector\u003C/code\u003E, and other types\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EString or binary\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOversize \u003Ccode\u003Ejson\u003C/code\u003E/\u003Ccode\u003Ejsonb\u003C/code\u003E values become NULL\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPostGIS \u003Ccode\u003Egeometry\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBinary (WKB)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENeeds \u003Ccode\u003Epg_lake_spatial\u003C/code\u003E; use \u003Ccode\u003ETO_GEOMETRY\u003C/code\u003E at query time\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERange and multirange types\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EString\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETop-level only\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ESee the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-type-mapping\"\u003Etype mapping reference\u003C/a\u003E for the full list, including blocked types.\u003C/p\u003E\n","\u003Ch3\u003EThings to know before you mirror\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPrimary keys matter.\u003C/strong\u003E Tables without a primary key are mirrored insert-only: the extension sets \u003Ccode\u003EREPLICA IDENTITY NOTHING\u003C/code\u003E, and Postgres then rejects \u003Ccode\u003EUPDATE\u003C/code\u003E and \u003Ccode\u003EDELETE\u003C/code\u003E on the source. Add a primary key first if the table needs updates or deletes.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENot every table qualifies.\u003C/strong\u003E Unlogged tables, views, materialized views, foreign tables, tables with virtual generated columns, and tables with ANSI-reserved column names can't be mirrored.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETable vs. schema scope.\u003C/strong\u003E Table-level mirrors only include what you name, so a new table stays unmirrored until you add it. Schema-level mirrors pick up every new table automatically, including ones you might not mean to expose.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOne replication slot per mirror.\u003C/strong\u003E The default \u003Ccode\u003Emax_replication_slots\u003C/code\u003E is 10, which is shared with any other logical replication subscribers.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWatch WAL retention.\u003C/strong\u003E \u003Ccode\u003Emax_slot_wal_keep_size\u003C/code\u003E defaults to one-tenth of the disk. If lag pushes the WAL backlog past that, Postgres invalidates the slot and the mirror re-snapshots every table. For sustained high write volume, use STANDARD_L or larger with extra disk.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECost tradeoff.\u003C/strong\u003E Compute is the serverless apply task. A longer \u003Ccode\u003Erefresh_interval\u003C/code\u003E batches more changes per run and costs less, and \u003Ccode\u003E$live\u003C/code\u003E still gives you fresh reads in between.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPostgres-to-Snowflake documentation\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring\"\u003EData mirroring overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-create\"\u003ECreate a mirror\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-query\"\u003EQuery mirrored data\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-manage\"\u003EManage and monitor mirrors\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-reference\"\u003EMirror procedures reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-type-mapping\"\u003EType mapping reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/snowflake-postgres-mirror-to-snowflake/\"\u003EPostgres mirroring quickstart\u003C/a\u003E: an end-to-end walkthrough with sample data\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/data-mirroring-ga-snowflake-postgres/\"\u003EData mirroring GA announcement\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESnowflake to Postgres\u003C/h2\u003E\n","\u003Ch3\u003EBring Snowflake data closer to applications\u003C/h3\u003E\n","\u003Cp\u003ESnowflake-to-Postgres mirroring—also called reverse mirroring—continuously replicates Snowflake tables into a Snowflake Postgres instance. Changes made in Snowflake flow to Postgres within seconds, giving applications and Postgres-compatible tools access to data managed in Snowflake.\u003C/p\u003E\n","\u003Cp\u003EUse Snowflake-to-Postgres mirroring when you want to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EServe Snowflake data through a Postgres interface.\u003C/li\u003E\u003Cli\u003ECreate a low-latency read path for application workloads.\u003C/li\u003E\u003Cli\u003EUse Postgres-compatible drivers, SQL, and tooling against selected Snowflake tables.\u003C/li\u003E\u003Cli\u003EKeep an operational Postgres representation synchronized with Snowflake-managed data.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESnowflake-to-Postgres mirroring is currently in \u003Cstrong\u003EPrivate Preview\u003C/strong\u003E. Access requires enablement through your Snowflake account team. Preview setup uses CDC-based replication from Snowflake source tables into a Snowflake Postgres target instance.\u003C/p\u003E\n","\u003Ch3\u003EHow reverse mirroring works\u003C/h3\u003E\n","\u003Cp\u003EReverse mirroring runs the CDC flow in the other direction. Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/streams-intro\"\u003Echange tracking\u003C/a\u003E on the source table records inserts, updates, and deletes. The \u003Ccode\u003Esnowflake\u003C/code\u003E application reads those changes on a schedule you set and applies them to a matching table in your Snowflake Postgres database. After an initial copy of the full table, only changed rows move.\u003C/p\u003E\n","\u003Cp\u003EBecause the target is a regular Postgres table, your application reads it with any Postgres driver or ORM, joins it with your own tables, and adds indexes that suit its queries.\u003C/p\u003E\n","\u003Ch3\u003ESet up a reverse mirror\u003C/h3\u003E\n","\u003Cp\u003EThe steps below show the Private Preview flow. Manual grants are expected to be automated at Public Preview.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Grant the \u003Ccode\u003Esnowflake\u003C/code\u003E application access to your Postgres instance:\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\u003E\u003Cstrong\u003E2. Turn on change tracking for each source table.\u003C/strong\u003E Reverse mirroring reads changes from the table's change tracking data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE analytics_db.public.customer_scores SET CHANGE_TRACKING = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Give the \u003Ccode\u003Esnowflake\u003C/code\u003E application read access to the source:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT USAGE ON DATABASE analytics_db TO APPLICATION snowflake;\nGRANT USAGE ON SCHEMA analytics_db.public TO APPLICATION snowflake;\nGRANT SELECT ON TABLE analytics_db.public.customer_scores TO APPLICATION snowflake;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4. Create the reverse mirror.\u003C/strong\u003E The arguments are the mirror name, the target Postgres instance, the target Postgres database, an array of fully qualified source tables, and the refresh interval:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SNOWFLAKE.POSTGRES.CREATE_REVERSE_MIRROR(\n    'scores_reverse_mirror',\n    'my_instance',\n    'postgres',\n    ARRAY_CONSTRUCT('ANALYTICS_DB.PUBLIC.CUSTOMER_SCORES'),\n    '10 seconds'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5. Query the table from Postgres.\u003C/strong\u003E Connect with \u003Ccode\u003Epsql\u003C/code\u003E using the host from \u003Ccode\u003EDESCRIBE POSTGRES INSTANCE\u003C/code\u003E. The mirrored table appears in the target database once the initial copy completes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM public.customer_scores;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EThings to know about reverse mirroring\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EChange tracking is required\u003C/strong\u003E on every source table. Turning it on adds a small amount of storage on the Snowflake side.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EThe target table is read-only for your application.\u003C/strong\u003E The mirror owns its contents, so writes from Postgres would be overwritten or conflict with incoming changes. Keep application-owned data in separate tables and join to the mirrored ones.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIndexes aren't carried over.\u003C/strong\u003E Snowflake tables don't have Postgres-style secondary indexes, so you build them on the Postgres side yourself (see below).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EShort refresh intervals give low lag.\u003C/strong\u003E Intervals in seconds keep Postgres close to Snowflake for application reads.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPreview behavior can change.\u003C/strong\u003E Syntax and setup steps may change before Public Preview.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAdd indexes after the initial load\u003C/h3\u003E\n","\u003Cp\u003EReverse-mirrored tables arrive in Postgres without the secondary indexes your application queries need. Bulk-loading into an unindexed table is faster, so let the initial copy finish, then add indexes that match your read patterns. Use \u003Ccode\u003ECONCURRENTLY\u003C/code\u003E so the build doesn't block the ongoing replication writes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX CONCURRENTLY idx_customer_scores_customer_id\n    ON public.customer_scores (customer_id);\n\nCREATE INDEX CONCURRENTLY idx_customer_scores_segment_score\n    ON public.customer_scores (segment, score DESC);\n\nANALYZE public.customer_scores;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESee the \u003Ca href=\"https://www.snowflake.com/en/developers/postgres/indexing/\"\u003Eindexing guide\u003C/a\u003E for choosing index types.\u003C/p\u003E\n","\u003Ch3\u003ESnowflake-to-Postgres documentation\u003C/h3\u003E\n","\u003Cp\u003EPreview documentation is available from your Snowflake account team once your account is enabled.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/contact-sales/\"\u003EContact your Snowflake account team about Snowflake-to-Postgres mirroring\u003C/a\u003E\u003C/p\u003E\n","\u003Ch2\u003EOne platform, two directions\u003C/h2\u003E\n","\u003Cp\u003EData mirroring gives teams a consistent way to move data between operational Postgres workloads and Snowflake analytics and AI workloads:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EBuild applications in Snowflake Postgres, analyze in Snowflake.\u003C/strong\u003E Move application data into Snowflake without maintaining a separate CDC pipeline.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBuild analytical and AI workflows in Snowflake, serve data through Postgres.\u003C/strong\u003E Bring selected Snowflake tables into Snowflake Postgres for application access.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUse each system for what it does best.\u003C/strong\u003E Keep transactional workloads close to applications while making data available wherever it is needed across the Snowflake platform.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe two directions are separate mirroring capabilities. Design each flow around the system that owns the source data and the freshness, access, and governance requirements of the target workload.\u003C/p\u003E"],"description":"","title":"Data Mirroring",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"Move data between Snowflake Postgres and Snowflake with a fully managed, continuously updated pipeline—without deploying a separate ETL service or CDC connector.\n\nData mirroring supports two directions:\n\n* **Postgres to Snowflake:** Replicate operational data into Snowflake for analytics, AI, reporting, and downstream applications. **Generally available.**\n* **Snowflake to Postgres:** Replicate Snowflake tables into Snowflake Postgres for low-latency application reads and Postgres-compatible workloads. **Private Preview.**\n\nChoose the direction that matches where your source data lives and where you need to use it.\n\n## Postgres to Snowflake\n\n### Keep analytics and AI current with operational data\n\nPostgres data mirroring continuously replicates selected tables or schemas from a Snowflake Postgres instance into a Snowflake database. Inserts, updates, deletes, and supported schema changes flow automatically to the target, so your analytical data stays close to the source without a pipeline to build, monitor, or maintain.\n\nUse Postgres-to-Snowflake mirroring when you want to:\n\n* Run analytics and AI workloads on current application data.\n* Replace custom sync code, batch jobs, or third-party CDC connectors.\n* Make operational data available to Snowflake tools while keeping the application workload in Postgres.\n* Preserve transaction boundaries across related tables.\n* Query recent row-level changes through the `$changes` feed.\n\n### Key capabilities\n\n* **No separate replication service.** Mirroring is built into Snowflake Postgres and Snowflake.\n* **Low-lag reads.** Each mirrored table includes a `$live` view that combines applied data with pending changes, so committed source changes can be queried without waiting for the next table-apply cycle.\n* **Transactional consistency.** Changes from a source transaction become visible together on the Snowflake side, helping preserve relationships such as foreign keys across mirrored tables.\n* **Schema evolution.** Supported schema changes and additions to schema-level replication are reflected automatically.\n* **Queryable change history.** Each mirrored table has a `$changes` companion that exposes inserts, updates, and deletes for the previous seven days.\n* **Flexible targets.** Mirror into standard Snowflake tables or Snowflake-managed Iceberg tables.\n* **Open foundations.** Change data is written to Apache Iceberg tables through the open source `pg_lake` extension.\n\n### How it works\n\n![Overview of Postgres data mirroring](https://www.snowflake.com/content/dam/snowflake-site/developers/postgres/data-mirroring/image1.png?v=a3909f79)\n\nA CDC worker on the Snowflake Postgres source captures changes using standard Postgres logical replication. It writes change records to Iceberg change logs, which Snowflake refreshes and applies to the target database using serverless compute. The target tables and `$live` views are maintained automatically.\n\n![Postgres-to-Snowflake mirroring architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/postgres/data-mirroring/image2.png?v=a3909f79)\n\nYou can create a mirror in Snowsight, with SQL, or by asking Snowflake CoCo. Select individual tables for a curated replication set, or replicate an entire schema so new tables are included automatically.\n\nUnder the hood, a mirror is built from standard Postgres pieces:\n\n* A **publication** on the source, created by the `snowflake_cdc` extension.\n* A **logical replication slot**, read by a background CDC worker that decodes the WAL.\n* Per-table **`$changes` Iceberg tables** and a per-mirror **metalog**, written by the CDC worker through `pg_lake`. These are the same Iceberg objects on both sides, so you can read them from Postgres or Snowflake.\n* On the Snowflake side, a **target database**, a serverless task named `APPLY_MIRROR_\u003Cmirror_name\u003E`, and per-table **target tables** with **`$live` views**.\n\nThe apply task reads the metalog in commit order and applies each batch in a single Snowflake transaction. If a run fails, it rolls back and the next run resumes from the same position, which gives you exactly-once delivery. Apply uses delete-then-append rather than `MERGE`, so insert-only batches skip scanning the target table.\n\n### Set up a mirror with SQL\n\n**1. Prepare the source instance.** New Snowflake Postgres instances include the required extensions. Older instances need a refresh from **Manage** in Snowsight to pick up the latest software patch. The BURSTABLE tier isn't supported; use STANDARD or HIGH MEMORY.\n\n**2. Grant permissions.** The Snowsight UI handles this for you. With SQL, grant the mirror admin application role and give the `snowflake` application usage on your instance:\n\n```sql\nGRANT APPLICATION ROLE snowflake.postgres_mirror_admin TO ROLE \"my_role\";\nGRANT USAGE ON POSTGRES INSTANCE \"my_instance\" TO APPLICATION snowflake;\n```\n\n**3. Create the mirror.** List tables with `postgres_tables`, whole schemas with `postgres_schemas`, or both:\n\n```sql\nCALL SNOWFLAKE.POSTGRES.CREATE_MIRROR(\n    mirror_name         =\u003E 'orders_mirror',\n    postgres_instance   =\u003E 'my_instance',\n    postgres_database   =\u003E 'postgres',\n    target_database     =\u003E 'ORDERS_ANALYTICS',\n    postgres_tables     =\u003E ['public.customers', 'public.orders', 'public.order_items'],\n    postgres_schemas    =\u003E NULL,\n    refresh_interval    =\u003E '1 minute'\n);\n```\n\n`refresh_interval` defaults to `'10 minutes'` and can go up to `'1 day'`. The target database must not already exist; the mirror creates it. To mirror into Snowflake-managed Iceberg tables instead of standard tables, add `target_table_type =\u003E 'ICEBERG'`. Add `external_volume` as well to write those tables to your own storage.\n\n**4. Check status and query the data.** Tables show `SNAPSHOTTING` during the initial copy and `REPLICATING` once caught up:\n\n```sql\nCALL SNOWFLAKE.POSTGRES.LIST_MIRRORED_TABLES('orders_mirror');\n\n-- Applied data, updated every refresh_interval\nSELECT COUNT(*) FROM ORDERS_ANALYTICS.PUBLIC.ORDERS;\n\n-- Applied data plus pending changes, about 30 seconds behind the source\nSELECT COUNT(*) FROM ORDERS_ANALYTICS.PUBLIC.ORDERS$live;\n```\n\nTo add or remove tables later, use `ALTER_MIRROR` with `add_tables`/`remove_tables` or `add_schemas`/`remove_schemas`.\n\n### Data type mapping\n\nSchema, table, and column names are uppercased on the target, so `created_at` becomes `CREATED_AT`. Tables whose names differ only by case can't both be mirrored.\n\n| Postgres type | Lands in Snowflake as | Notes |\n|---|---|---|\n| `boolean`, `smallint`, `integer`, `bigint`, `real`, `double precision` | Native boolean, integer, and float types | Direct mapping |\n| `numeric(p,s)` with p and s ≤ 38 | `DECIMAL(p,s)` | Direct mapping |\n| Unbounded `numeric`, or p/s \u003E 38 | Double | Values beyond the `float8` range become ±Infinity |\n| `date`, `time`, `timestamp`, `timestamptz` | Native date and time types | `timetz` is normalized to UTC; `infinity` values are clamped to the nearest boundary |\n| `interval` | Struct (months, days, microseconds) | |\n| `text`, `varchar`, `char` | String | Values over 16 MiB are truncated |\n| `bytea` | Binary | Values over 8 MiB are truncated |\n| `uuid` | UUID | Top-level columns only; `uuid[]` or `uuid` inside a composite is rejected |\n| Arrays, composites of supported types | `ARRAY`, `OBJECT` | Multidimensional array values become NULL |\n| `json`, `jsonb`, `hstore`, `vector`, and other types | String or binary | Oversize `json`/`jsonb` values become NULL |\n| PostGIS `geometry` | Binary (WKB) | Needs `pg_lake_spatial`; use `TO_GEOMETRY` at query time |\n| Range and multirange types | String | Top-level only |\n\nSee the [type mapping reference](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-type-mapping) for the full list, including blocked types.\n\n### Things to know before you mirror\n\n* **Primary keys matter.** Tables without a primary key are mirrored insert-only: the extension sets `REPLICA IDENTITY NOTHING`, and Postgres then rejects `UPDATE` and `DELETE` on the source. Add a primary key first if the table needs updates or deletes.\n* **Not every table qualifies.** Unlogged tables, views, materialized views, foreign tables, tables with virtual generated columns, and tables with ANSI-reserved column names can't be mirrored.\n* **Table vs. schema scope.** Table-level mirrors only include what you name, so a new table stays unmirrored until you add it. Schema-level mirrors pick up every new table automatically, including ones you might not mean to expose.\n* **One replication slot per mirror.** The default `max_replication_slots` is 10, which is shared with any other logical replication subscribers.\n* **Watch WAL retention.** `max_slot_wal_keep_size` defaults to one-tenth of the disk. If lag pushes the WAL backlog past that, Postgres invalidates the slot and the mirror re-snapshots every table. For sustained high write volume, use STANDARD_L or larger with extra disk.\n* **Cost tradeoff.** Compute is the serverless apply task. A longer `refresh_interval` batches more changes per run and costs less, and `$live` still gives you fresh reads in between.\n\n### Postgres-to-Snowflake documentation\n\n* [Data mirroring overview](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring)\n* [Create a mirror](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-create)\n* [Query mirrored data](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-query)\n* [Manage and monitor mirrors](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-manage)\n* [Mirror procedures reference](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-reference)\n* [Type mapping reference](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-type-mapping)\n* [Postgres mirroring quickstart](https://www.snowflake.com/en/developers/guides/snowflake-postgres-mirror-to-snowflake/): an end-to-end walkthrough with sample data\n* [Data mirroring GA announcement](https://www.snowflake.com/en/blog/data-mirroring-ga-snowflake-postgres/)\n\n## Snowflake to Postgres\n\n### Bring Snowflake data closer to applications\n\nSnowflake-to-Postgres mirroring—also called reverse mirroring—continuously replicates Snowflake tables into a Snowflake Postgres instance. Changes made in Snowflake flow to Postgres within seconds, giving applications and Postgres-compatible tools access to data managed in Snowflake.\n\nUse Snowflake-to-Postgres mirroring when you want to:\n\n* Serve Snowflake data through a Postgres interface.\n* Create a low-latency read path for application workloads.\n* Use Postgres-compatible drivers, SQL, and tooling against selected Snowflake tables.\n* Keep an operational Postgres representation synchronized with Snowflake-managed data.\n\nSnowflake-to-Postgres mirroring is currently in **Private Preview**. Access requires enablement through your Snowflake account team. Preview setup uses CDC-based replication from Snowflake source tables into a Snowflake Postgres target instance.\n\n### How reverse mirroring works\n\nReverse mirroring runs the CDC flow in the other direction. Snowflake [change tracking](https://docs.snowflake.com/en/user-guide/streams-intro) on the source table records inserts, updates, and deletes. The `snowflake` application reads those changes on a schedule you set and applies them to a matching table in your Snowflake Postgres database. After an initial copy of the full table, only changed rows move.\n\nBecause the target is a regular Postgres table, your application reads it with any Postgres driver or ORM, joins it with your own tables, and adds indexes that suit its queries.\n\n### Set up a reverse mirror\n\nThe steps below show the Private Preview flow. Manual grants are expected to be automated at Public Preview.\n\n**1. Grant the `snowflake` application access to your Postgres instance:**\n\n```sql\nGRANT USAGE ON POSTGRES INSTANCE my_instance TO APPLICATION snowflake;\n```\n\n**2. Turn on change tracking for each source table.** Reverse mirroring reads changes from the table's change tracking data:\n\n```sql\nALTER TABLE analytics_db.public.customer_scores SET CHANGE_TRACKING = TRUE;\n```\n\n**3. Give the `snowflake` application read access to the source:**\n\n```sql\nGRANT USAGE ON DATABASE analytics_db TO APPLICATION snowflake;\nGRANT USAGE ON SCHEMA analytics_db.public TO APPLICATION snowflake;\nGRANT SELECT ON TABLE analytics_db.public.customer_scores TO APPLICATION snowflake;\n```\n\n**4. Create the reverse mirror.** The arguments are the mirror name, the target Postgres instance, the target Postgres database, an array of fully qualified source tables, and the refresh interval:\n\n```sql\nCALL SNOWFLAKE.POSTGRES.CREATE_REVERSE_MIRROR(\n    'scores_reverse_mirror',\n    'my_instance',\n    'postgres',\n    ARRAY_CONSTRUCT('ANALYTICS_DB.PUBLIC.CUSTOMER_SCORES'),\n    '10 seconds'\n);\n```\n\n**5. Query the table from Postgres.** Connect with `psql` using the host from `DESCRIBE POSTGRES INSTANCE`. The mirrored table appears in the target database once the initial copy completes:\n\n```sql\nSELECT count(*) FROM public.customer_scores;\n```\n\n### Things to know about reverse mirroring\n\n* **Change tracking is required** on every source table. Turning it on adds a small amount of storage on the Snowflake side.\n* **The target table is read-only for your application.** The mirror owns its contents, so writes from Postgres would be overwritten or conflict with incoming changes. Keep application-owned data in separate tables and join to the mirrored ones.\n* **Indexes aren't carried over.** Snowflake tables don't have Postgres-style secondary indexes, so you build them on the Postgres side yourself (see below).\n* **Short refresh intervals give low lag.** Intervals in seconds keep Postgres close to Snowflake for application reads.\n* **Preview behavior can change.** Syntax and setup steps may change before Public Preview.\n\n### Add indexes after the initial load\n\nReverse-mirrored tables arrive in Postgres without the secondary indexes your application queries need. Bulk-loading into an unindexed table is faster, so let the initial copy finish, then add indexes that match your read patterns. Use `CONCURRENTLY` so the build doesn't block the ongoing replication writes:\n\n```sql\nCREATE INDEX CONCURRENTLY idx_customer_scores_customer_id\n    ON public.customer_scores (customer_id);\n\nCREATE INDEX CONCURRENTLY idx_customer_scores_segment_score\n    ON public.customer_scores (segment, score DESC);\n\nANALYZE public.customer_scores;\n```\n\nSee the [indexing guide](https://www.snowflake.com/en/developers/postgres/indexing/) for choosing index types.\n\n### Snowflake-to-Postgres documentation\n\nPreview documentation is available from your Snowflake account team once your account is enabled.\n\n[Contact your Snowflake account team about Snowflake-to-Postgres mirroring](https://www.snowflake.com/en/contact-sales/)\n\n## One platform, two directions\n\nData mirroring gives teams a consistent way to move data between operational Postgres workloads and Snowflake analytics and AI workloads:\n\n* **Build applications in Snowflake Postgres, analyze in Snowflake.** Move application data into Snowflake without maintaining a separate CDC pipeline.\n* **Build analytical and AI workflows in Snowflake, serve data through Postgres.** Bring selected Snowflake tables into Snowflake Postgres for application access.\n* **Use each system for what it does best.** Keep transactional workloads close to applications while making data available wherever it is needed across the Snowflake platform.\n\nThe two directions are separate mirroring capabilities. Design each flow around the system that owns the source data and the freshness, access, and governance requirements of the target workload.\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":true,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-cf289ab987","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-fffe317250",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0516fb9a9d","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-27",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-a8438512db","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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