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Some features may not be available.\u003C/strong\u003E<br>\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIf you're running an application on Snowflake Postgres, you probably have operational data that would be valuable for analytics in Snowflake. The \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-pg_lake\"\u003E\u003Cstrong\u003Epg_lake\u003C/strong\u003E\u003C/a\u003E extension makes this easy — it lets you create Iceberg tables directly from Postgres using standard SQL (\u003Ccode\u003ECREATE TABLE ... USING iceberg\u003C/code\u003E), and Snowflake can read those tables natively through a catalog integration. No ETL pipelines, no data movement tooling, no extra infrastructure.\u003C/p\u003E\n","\u003Cp\u003EIn a typical pattern, your application writes to regular Postgres tables for fast transactional operations. You then use an Iceberg table managed by pg_lake along with \u003Ca href=\"https://github.com/CrunchyData/pg_incremental\"\u003Epg_incremental\u003C/a\u003E to automatically sync data — both the initial backfill and ongoing new rows. Snowflake picks up changes through a catalog integration.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, you will set up an operational Postgres database, create an Iceberg table, automate a data sync to Iceberg and Snowflake, and query the results in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Postgres instance with an operational sensor data table\u003C/li\u003E\u003Cli\u003EAn Iceberg table managed by pg_lake\u003C/li\u003E\u003Cli\u003EAn automated pipeline using pg_incremental that backfills existing data and syncs new rows\u003C/li\u003E\u003Cli\u003EA Snowflake catalog integration connected to the Postgres Iceberg catalog\u003C/li\u003E\u003Cli\u003EA queryable Iceberg table in Snowflake sourced from Postgres\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up an operational Postgres table and an Iceberg table using pg_lake\u003C/li\u003E\u003Cli\u003EHow to use pg_incremental to backfill and continuously sync data with exactly-once processing\u003C/li\u003E\u003Cli\u003EHow to connect Snowflake to Postgres-managed Iceberg tables\u003C/li\u003E\u003Cli\u003EHow to query Iceberg data from Snowflake\u003C/li\u003E\u003Cli\u003EHow to manually refresh and enable auto-refresh for Iceberg tables\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 configuring the Snowflake catalog integration.\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 catalog integration in Snowflake. Keep your instance name handy — you will reference it in the Snowflake catalog integration step.\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<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up the Operational Database\u003C/h2\u003E\n","\u003Cp\u003EBefore setting up pg_lake or our analytics, start by creating a regular Postgres table to represent your application's operational data. In a real scenario, this is the table your application writes to — inserts, updates, and deletes happen here as part of normal operations. Later, you will copy this data into an Iceberg table for analytics in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Sensor Readings Table\u003C/h3\u003E\n","\u003Cp\u003ECreate a standard heap table for IoT sensor data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TABLE sensor_readings (\n    sensor_id INT,\n    device_type TEXT,\n    reading_time TIMESTAMPTZ,\n    temperature NUMERIC(5,2),\n    humidity NUMERIC(5,2),\n    battery_pct NUMERIC(4,1)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESeed Sample Data\u003C/h3\u003E\n","\u003Cp\u003EInsert 5,000 rows of simulated sensor data spanning the last 90 days:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO sensor_readings (sensor_id, device_type, reading_time, temperature, humidity, battery_pct)\nSELECT\n    (random() * 50)::int AS sensor_id,\n    (ARRAY['thermostat', 'weather_station', 'greenhouse', 'cold_storage', 'hvac'])[1 + (random() * 4)::int] AS device_type,\n    now() - (random() * interval '90 days') AS reading_time,\n    (random() * 40 + 5)::numeric(5,2) AS temperature,\n    (random() * 60 + 20)::numeric(5,2) AS humidity,\n    (random() * 80 + 20)::numeric(4,1) AS battery_pct\nFROM generate_series(1, 5000);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify the Data\u003C/h3\u003E\n","\u003Cp\u003EConfirm the data looks correct:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) AS total_rows FROM sensor_readings;\n\nSELECT device_type, count(*) AS readings, round(avg(temperature), 2) AS avg_temp\nFROM sensor_readings\nGROUP BY device_type\nORDER BY device_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see 5,000 total rows spread across the five device types.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up pg_lake and Create an Iceberg Table\u003C/h2\u003E\n","\u003Cp\u003ENow that you have an active operational table, enable pg_lake and create an Iceberg table to hold a copy of the data for Snowflake analytics. The Iceberg table lives in cloud object storage and is managed by Postgres as the Iceberg catalog — Snowflake reads from it directly. You will populate it using an automated pipeline in the next step.\u003C/p\u003E\n","\u003Ch3\u003EEnable Extensions\u003C/h3\u003E\n","\u003Cp\u003EInstall pg_lake, this a series of extensions to connect Postgres to object storage files:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE EXTENSION pg_lake CASCADE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Iceberg Table\u003C/h3\u003E\n","\u003Cp\u003ECreate an Iceberg table with the same schema as the operational table. The \u003Ccode\u003EUSING iceberg\u003C/code\u003E clause tells Postgres to store this table in Iceberg format on cloud object storage:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TABLE sensor_readings_iceberg (\n    sensor_id INT,\n    device_type TEXT,\n    reading_time TIMESTAMPTZ,\n    temperature NUMERIC(5,2),\n    humidity NUMERIC(5,2),\n    battery_pct NUMERIC(4,1)\n) USING iceberg;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAutomate Sync with pg_incremental\u003C/h2\u003E\n","\u003Cp\u003ERather than manually copying data, you can use pg_incremental to automatically sync rows on a schedule — both the initial backfill and ongoing new data. pg_incremental is built on pg_cron and provides exactly-once semantics — each time range is processed once and only once, so you will never get duplicates in the Iceberg table.\u003C/p\u003E\n","\u003Ch3\u003EInstall Extensions\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE EXTENSION pg_cron;\nCREATE EXTENSION pg_incremental;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Pipeline\u003C/h3\u003E\n","\u003Cp\u003EDefine a pipeline that copies rows based on \u003Ccode\u003Ereading_time\u003C/code\u003E. The \u003Ccode\u003E$1\u003C/code\u003E and \u003Ccode\u003E$2\u003C/code\u003E placeholders represent the start and end of each time interval that pg_incremental processes. Setting \u003Ccode\u003Estart_time\u003C/code\u003E to the minimum \u003Ccode\u003Ereading_time\u003C/code\u003E tells the pipeline to backfill all existing data first, then continue syncing new rows as they arrive.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT incremental.create_time_interval_pipeline(\n    pipeline_name      := 'sync_postgres_to_iceberg',\n    time_interval      := '1 minute',\n    source_table_name  := 'sensor_readings',\n    start_time         := (SELECT min(reading_time) FROM sensor_readings),\n    command            := $$\n        INSERT INTO sensor_readings_iceberg\n        SELECT sensor_id, device_type, reading_time, temperature, humidity, battery_pct\n        FROM sensor_readings\n        WHERE reading_time >= $1 AND reading_time < $2\n    $$\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen the pipeline is created, it immediately processes all completed intervals from \u003Ccode\u003Estart_time\u003C/code\u003E to now — this is the initial backfill. After that, it runs every minute and processes only new intervals as they complete. Setting \u003Ccode\u003Esource_table_name\u003C/code\u003E ensures the pipeline waits for any in-flight writes to \u003Ccode\u003Esensor_readings\u003C/code\u003E before processing, preventing data from being skipped.\u003C/p\u003E\n","\u003Cp\u003EYou can set the \u003Ccode\u003Etime_interval\u003C/code\u003E to your preference (minutes, hours, days, etc.) depending on how frequently you want new data to sync.\u003C/p\u003E\n","\u003Ch3\u003EVerify the data copy to Iceberg\u003C/h3\u003E\n","\u003Cp\u003EConfirm the pipeline added all existing data to the Iceberg tables. Both counts should show 5,000:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    (SELECT count(*) FROM sensor_readings) AS heap_rows,\n    (SELECT count(*) FROM sensor_readings_iceberg) AS iceberg_rows;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESimulate New Data\u003C/h3\u003E\n","\u003Cp\u003ENow insert 100 new rows with current timestamps to simulate real application writes arriving after the pipeline is running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO sensor_readings (sensor_id, device_type, reading_time, temperature, humidity, battery_pct)\nSELECT\n    (random() * 50)::int AS sensor_id,\n    (ARRAY['thermostat', 'weather_station', 'greenhouse', 'cold_storage', 'hvac'])[1 + (random() * 4)::int] AS device_type,\n    now() AS reading_time,\n    (random() * 40 + 5)::numeric(5,2) AS temperature,\n    (random() * 60 + 20)::numeric(5,2) AS humidity,\n    (random() * 80 + 20)::numeric(4,1) AS battery_pct\nFROM generate_series(1, 100);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify the Incremental Sync\u003C/h3\u003E\n","\u003Cp\u003EWait about two minutes for the pipeline to process the new interval, then check that the new rows have been picked up:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    (SELECT count(*) FROM sensor_readings) AS heap_rows,\n    (SELECT count(*) FROM sensor_readings_iceberg) AS iceberg_rows;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoth counts should show 5,100 (the original 5,000 plus the 100 new ones). From this point on, any new rows inserted into \u003Ccode\u003Esensor_readings\u003C/code\u003E will automatically flow into the Iceberg table on the next pipeline run.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnect Snowflake to Iceberg\u003C/h2\u003E\n","\u003Cp\u003ENow switch to Snowflake. You will create a catalog integration that connects Snowflake to the Iceberg metadata managed by Postgres, then create an Iceberg table in Snowflake that reads from it.\u003C/p\u003E\n","\u003Ch3\u003ECreate a Database\u003C/h3\u003E\n","\u003Cp\u003ECreate a database to hold your Iceberg tables in Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE pglake;\nUSE DATABASE pglake;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Catalog Integration\u003C/h3\u003E\n","\u003Cp\u003ECreate a catalog integration that points Snowflake at the Postgres Iceberg catalog. Replace \u003Ccode\u003E{instance_name}\u003C/code\u003E with the Snowflake Postgres instance name you saved earlier:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CATALOG INTEGRATION postgres_iceberg_integration\n  CATALOG_SOURCE    = SNOWFLAKE_POSTGRES\n  TABLE_FORMAT      = ICEBERG\n  CATALOG_NAMESPACE = 'public'\n  REST_CONFIG = (\n    POSTGRES_INSTANCE      = '{instance_name}'\n    CATALOG_NAME           = 'postgres'\n    ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS\n  )\n  ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Iceberg Table\u003C/h3\u003E\n","\u003Cp\u003ECreate an Iceberg table in Snowflake that references the \u003Ccode\u003Esensor_readings_iceberg\u003C/code\u003E table in Postgres:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE iot_sensors_from_postgres\n    CATALOG = 'postgres_iceberg_integration'\n    CATALOG_TABLE_NAME = 'sensor_readings_iceberg';\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery Iceberg Data\u003C/h2\u003E\n","\u003Cp\u003EWith the Iceberg table created in Snowflake, you can now query data that lives in Postgres-managed Iceberg storage.\u003C/p\u003E\n","\u003Ch3\u003EVerify Row Count\u003C/h3\u003E\n","\u003Cp\u003EConfirm data is visible in Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM iot_sensors_from_postgres;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnalyze the Data\u003C/h3\u003E\n","\u003Cp\u003ERun any typical Snowflake analytics. For example, with the sample data loaded above, you can see how many readings land on each day over the 90-day window:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    READING_TIME::DATE AS reading_date,\n    COUNT(*) AS record_count\nFROM iot_sensors_from_postgres\nGROUP BY reading_date\nORDER BY reading_date;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERefresh and Auto-Refresh\u003C/h2\u003E\n","\u003Cp\u003EWith pg_lake, Postgres is the Iceberg catalog and Snowflake reads the Iceberg metadata through the catalog integration. This makes the table "externally managed" from Snowflake's perspective — Snowflake can query and refresh the data, but Postgres owns the table lifecycle.\u003C/p\u003E\n","\u003Cp\u003EKey points:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYou can always run a \u003Cstrong\u003Emanual refresh\u003C/strong\u003E to pull the latest data using \u003Ccode\u003EALTER ICEBERG TABLE iot_sensors_from_postgres REFRESH;\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAuto-refresh\u003C/strong\u003E (continuous polling) is available but off by default — you must explicitly enable it per table\u003C/li\u003E\u003Cli\u003EThe polling frequency is controlled on the catalog integration via \u003Ccode\u003EREFRESH_INTERVAL_SECONDS\u003C/code\u003E (default 30s)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EEnable Auto-Refresh\u003C/h3\u003E\n","\u003Cp\u003EEnable automatic refresh so Snowflake continuously polls for changes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER ICEBERG TABLE iot_sensors_from_postgres SET AUTO_REFRESH = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure Refresh Interval\u003C/h3\u003E\n","\u003Cp\u003EThe catalog integration controls how often Snowflake polls for changes. Check the current setting:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDESCRIBE CATALOG INTEGRATION postgres_iceberg_integration;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo change the refresh interval (for example, refresh every 60 seconds):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER CATALOG INTEGRATION postgres_iceberg_integration SET REFRESH_INTERVAL_SECONDS = 60;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETest Auto-Refresh\u003C/h3\u003E\n","\u003Cp\u003ENow you can add another 100 rows to Postgres, see the Iceberg row count rise, along with your Snowflake table row count.\u003C/p\u003E\n","\u003Cp\u003EIn Postgres:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO sensor_readings (sensor_id, device_type, reading_time, temperature, humidity, battery_pct)\nSELECT\n    (random() * 50)::int AS sensor_id,\n    (ARRAY['thermostat', 'weather_station', 'greenhouse', 'cold_storage', 'hvac'])[1 + (random() * 4)::int] AS device_type,\n    now() AS reading_time,\n    (random() * 40 + 5)::numeric(5,2) AS temperature,\n    (random() * 60 + 20)::numeric(5,2) AS humidity,\n    (random() * 80 + 20)::numeric(4,1) AS battery_pct\nFROM generate_series(1, 100);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM iot_sensors_from_postgres;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003ECongratulations!\u003C/h3\u003E\n","\u003Cp\u003EYou have successfully synced data from an operational Postgres database to Snowflake using Iceberg tables, pg_lake, and pg_incremental — with no external ETL pipeline required.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up an operational Postgres table and an Iceberg table using pg_lake\u003C/li\u003E\u003Cli\u003EHow to use pg_incremental to backfill and continuously sync data with exactly-once processing\u003C/li\u003E\u003Cli\u003EHow to create a Snowflake catalog integration that connects to Postgres-managed Iceberg metadata\u003C/li\u003E\u003Cli\u003EHow to query Iceberg data from Snowflake\u003C/li\u003E\u003Cli\u003EHow to manually refresh Iceberg tables and enable auto-refresh for continuous sync\u003C/li\u003E\u003Cli\u003EHow to tune refresh intervals for different workload requirements\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EGoing Further\u003C/h3\u003E\n","\u003Cp\u003EThis guide covered a single-table sync with time-based incremental processing. In production, you can extend this pattern further:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ca href=\"https://github.com/citusdata/pg_cron\"\u003Epg_cron\u003C/a\u003E\u003C/strong\u003E: Schedule arbitrary SQL jobs directly inside Postgres — useful for aggregations, cleanup tasks, or custom sync logic beyond what pg_incremental handles.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/postgres-time-series-iceberg/\"\u003ETable partitioning\u003C/a\u003E\u003C/strong\u003E: Combine Postgres declarative partitioning with pg_lake to build automated archiving — keep recent data in local Postgres partitions for fast writes, then offload older partitions to Iceberg on S3 for analytics in Snowflake.\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/engineering-blog/pg-lake-postgres-lakehouse-integration/\"\u003EIntroducing pg_lake: Integrate Your Data Lakehouse with Postgres\u003C/a\u003E (blog)\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/pg_lake\"\u003Epg_lake GitHub Repository\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\u003C/ul\u003E"],"description":"","title":"Sync Data from Snowflake Postgres to Snowflake with Iceberg and pg_lake","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n\u003E**Note:\n\u003Epg_lake features are in preview and under rapid development. Some features may not be available.**\u003Cbr\u003E\n\nIf you're running an application on Snowflake Postgres, you probably have operational data that would be valuable for analytics in Snowflake. The [**pg_lake**](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-pg_lake) extension makes this easy — it lets you create Iceberg tables directly from Postgres using standard SQL (`CREATE TABLE ... USING iceberg`), and Snowflake can read those tables natively through a catalog integration. No ETL pipelines, no data movement tooling, no extra infrastructure.\n\nIn a typical pattern, your application writes to regular Postgres tables for fast transactional operations. You then use an Iceberg table managed by pg_lake along with [pg_incremental](https://github.com/CrunchyData/pg_incremental) to automatically sync data — both the initial backfill and ongoing new rows. Snowflake picks up changes through a catalog integration.\n\nIn this quickstart, you will set up an operational Postgres database, create an Iceberg table, automate a data sync to Iceberg and Snowflake, and query the results in Snowflake.\n\n### What You Will Build\n- A Snowflake Postgres instance with an operational sensor data table\n- An Iceberg table managed by pg_lake\n- An automated pipeline using pg_incremental that backfills existing data and syncs new rows\n- A Snowflake catalog integration connected to the Postgres Iceberg catalog\n- A queryable Iceberg table in Snowflake sourced from Postgres\n\n### What You Will Learn\n- How to set up an operational Postgres table and an Iceberg table using pg_lake\n- How to use pg_incremental to backfill and continuously sync data with exactly-once processing\n- How to connect Snowflake to Postgres-managed Iceberg tables\n- How to query Iceberg data from Snowflake\n- How to manually refresh and enable auto-refresh for Iceberg tables\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 configuring the Snowflake catalog integration.\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 catalog integration in Snowflake. Keep your instance name handy — you will reference it in the Snowflake catalog integration step.\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\u003C!-- ------------------------ --\u003E\n## Set Up the Operational Database\n\nBefore setting up pg_lake or our analytics, start by creating a regular Postgres table to represent your application's operational data. In a real scenario, this is the table your application writes to — inserts, updates, and deletes happen here as part of normal operations. Later, you will copy this data into an Iceberg table for analytics in Snowflake.\n\n### Create the Sensor Readings Table\nCreate a standard heap table for IoT sensor data:\n\n```sql\nCREATE TABLE sensor_readings (\n    sensor_id INT,\n    device_type TEXT,\n    reading_time TIMESTAMPTZ,\n    temperature NUMERIC(5,2),\n    humidity NUMERIC(5,2),\n    battery_pct NUMERIC(4,1)\n);\n```\n\n### Seed Sample Data\nInsert 5,000 rows of simulated sensor data spanning the last 90 days:\n\n```sql\nINSERT INTO sensor_readings (sensor_id, device_type, reading_time, temperature, humidity, battery_pct)\nSELECT\n    (random() * 50)::int AS sensor_id,\n    (ARRAY['thermostat', 'weather_station', 'greenhouse', 'cold_storage', 'hvac'])[1 + (random() * 4)::int] AS device_type,\n    now() - (random() * interval '90 days') AS reading_time,\n    (random() * 40 + 5)::numeric(5,2) AS temperature,\n    (random() * 60 + 20)::numeric(5,2) AS humidity,\n    (random() * 80 + 20)::numeric(4,1) AS battery_pct\nFROM generate_series(1, 5000);\n```\n\n### Verify the Data\nConfirm the data looks correct:\n\n```sql\nSELECT count(*) AS total_rows FROM sensor_readings;\n\nSELECT device_type, count(*) AS readings, round(avg(temperature), 2) AS avg_temp\nFROM sensor_readings\nGROUP BY device_type\nORDER BY device_type;\n```\n\nYou should see 5,000 total rows spread across the five device types.\n\n\u003C!-- ------------------------ --\u003E\n## Set Up pg_lake and Create an Iceberg Table\n\nNow that you have an active operational table, enable pg_lake and create an Iceberg table to hold a copy of the data for Snowflake analytics. The Iceberg table lives in cloud object storage and is managed by Postgres as the Iceberg catalog — Snowflake reads from it directly. You will populate it using an automated pipeline in the next step.\n\n### Enable Extensions\nInstall pg_lake, this a series of extensions to connect Postgres to object storage files:\n\n```sql\nCREATE EXTENSION pg_lake CASCADE;\n```\n\n### Create the Iceberg Table\nCreate an Iceberg table with the same schema as the operational table. The `USING iceberg` clause tells Postgres to store this table in Iceberg format on cloud object storage:\n\n```sql\nCREATE TABLE sensor_readings_iceberg (\n    sensor_id INT,\n    device_type TEXT,\n    reading_time TIMESTAMPTZ,\n    temperature NUMERIC(5,2),\n    humidity NUMERIC(5,2),\n    battery_pct NUMERIC(4,1)\n) USING iceberg;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Automate Sync with pg_incremental\n\nRather than manually copying data, you can use pg_incremental to automatically sync rows on a schedule — both the initial backfill and ongoing new data. pg_incremental is built on pg_cron and provides exactly-once semantics — each time range is processed once and only once, so you will never get duplicates in the Iceberg table.\n\n### Install Extensions\n\n```sql\nCREATE EXTENSION pg_cron;\nCREATE EXTENSION pg_incremental;\n```\n\n### Create the Pipeline\n\nDefine a pipeline that copies rows based on `reading_time`. The `$1` and `$2` placeholders represent the start and end of each time interval that pg_incremental processes. Setting `start_time` to the minimum `reading_time` tells the pipeline to backfill all existing data first, then continue syncing new rows as they arrive.\n\n```sql\nSELECT incremental.create_time_interval_pipeline(\n    pipeline_name      := 'sync_postgres_to_iceberg',\n    time_interval      := '1 minute',\n    source_table_name  := 'sensor_readings',\n    start_time         := (SELECT min(reading_time) FROM sensor_readings),\n    command            := $$\n        INSERT INTO sensor_readings_iceberg\n        SELECT sensor_id, device_type, reading_time, temperature, humidity, battery_pct\n        FROM sensor_readings\n        WHERE reading_time \u003E= $1 AND reading_time \u003C $2\n    $$\n);\n```\n\nWhen the pipeline is created, it immediately processes all completed intervals from `start_time` to now — this is the initial backfill. After that, it runs every minute and processes only new intervals as they complete. Setting `source_table_name` ensures the pipeline waits for any in-flight writes to `sensor_readings` before processing, preventing data from being skipped.\n\nYou can set the `time_interval` to your preference (minutes, hours, days, etc.) depending on how frequently you want new data to sync.\n\n### Verify the data copy to Iceberg\nConfirm the pipeline added all existing data to the Iceberg tables. Both counts should show 5,000:\n\n```sql\nSELECT\n    (SELECT count(*) FROM sensor_readings) AS heap_rows,\n    (SELECT count(*) FROM sensor_readings_iceberg) AS iceberg_rows;\n```\n\n### Simulate New Data\nNow insert 100 new rows with current timestamps to simulate real application writes arriving after the pipeline is running:\n\n```sql\nINSERT INTO sensor_readings (sensor_id, device_type, reading_time, temperature, humidity, battery_pct)\nSELECT\n    (random() * 50)::int AS sensor_id,\n    (ARRAY['thermostat', 'weather_station', 'greenhouse', 'cold_storage', 'hvac'])[1 + (random() * 4)::int] AS device_type,\n    now() AS reading_time,\n    (random() * 40 + 5)::numeric(5,2) AS temperature,\n    (random() * 60 + 20)::numeric(5,2) AS humidity,\n    (random() * 80 + 20)::numeric(4,1) AS battery_pct\nFROM generate_series(1, 100);\n```\n\n### Verify the Incremental Sync\nWait about two minutes for the pipeline to process the new interval, then check that the new rows have been picked up:\n\n```sql\nSELECT\n    (SELECT count(*) FROM sensor_readings) AS heap_rows,\n    (SELECT count(*) FROM sensor_readings_iceberg) AS iceberg_rows;\n```\n\nBoth counts should show 5,100 (the original 5,000 plus the 100 new ones). From this point on, any new rows inserted into `sensor_readings` will automatically flow into the Iceberg table on the next pipeline run.\n\n\n\u003C!-- ------------------------ --\u003E\n## Connect Snowflake to Iceberg\n\nNow switch to Snowflake. You will create a catalog integration that connects Snowflake to the Iceberg metadata managed by Postgres, then create an Iceberg table in Snowflake that reads from it.\n\n### Create a Database\nCreate a database to hold your Iceberg tables in Snowflake:\n\n```sql\nCREATE DATABASE pglake;\nUSE DATABASE pglake;\n```\n\n### Create the Catalog Integration\nCreate a catalog integration that points Snowflake at the Postgres Iceberg catalog. Replace `{instance_name}` with the Snowflake Postgres instance name you saved earlier:\n\n```sql\nCREATE OR REPLACE CATALOG INTEGRATION postgres_iceberg_integration\n  CATALOG_SOURCE    = SNOWFLAKE_POSTGRES\n  TABLE_FORMAT      = ICEBERG\n  CATALOG_NAMESPACE = 'public'\n  REST_CONFIG = (\n    POSTGRES_INSTANCE      = '{instance_name}'\n    CATALOG_NAME           = 'postgres'\n    ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS\n  )\n  ENABLED = TRUE;\n```\n\n### Create the Iceberg Table\nCreate an Iceberg table in Snowflake that references the `sensor_readings_iceberg` table in Postgres:\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE iot_sensors_from_postgres\n    CATALOG = 'postgres_iceberg_integration'\n    CATALOG_TABLE_NAME = 'sensor_readings_iceberg';\n```\n\n\u003C!-- ------------------------ --\u003E\n## Query Iceberg Data\n\nWith the Iceberg table created in Snowflake, you can now query data that lives in Postgres-managed Iceberg storage.\n\n### Verify Row Count\nConfirm data is visible in Snowflake:\n\n```sql\nSELECT COUNT(*) FROM iot_sensors_from_postgres;\n```\n\n### Analyze the Data\nRun any typical Snowflake analytics. For example, with the sample data loaded above, you can see how many readings land on each day over the 90-day window:\n\n```sql\nSELECT\n    READING_TIME::DATE AS reading_date,\n    COUNT(*) AS record_count\nFROM iot_sensors_from_postgres\nGROUP BY reading_date\nORDER BY reading_date;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Refresh and Auto-Refresh\n\nWith pg_lake, Postgres is the Iceberg catalog and Snowflake reads the Iceberg metadata through the catalog integration. This makes the table \"externally managed\" from Snowflake's perspective — Snowflake can query and refresh the data, but Postgres owns the table lifecycle.\n\nKey points:\n- You can always run a **manual refresh** to pull the latest data using `ALTER ICEBERG TABLE iot_sensors_from_postgres REFRESH;`.\n- **Auto-refresh** (continuous polling) is available but off by default — you must explicitly enable it per table\n- The polling frequency is controlled on the catalog integration via `REFRESH_INTERVAL_SECONDS` (default 30s)\n\n### Enable Auto-Refresh\nEnable automatic refresh so Snowflake continuously polls for changes:\n\n```sql\nALTER ICEBERG TABLE iot_sensors_from_postgres SET AUTO_REFRESH = TRUE;\n```\n\n### Configure Refresh Interval\nThe catalog integration controls how often Snowflake polls for changes. Check the current setting:\n\n```sql\nDESCRIBE CATALOG INTEGRATION postgres_iceberg_integration;\n```\n\nTo change the refresh interval (for example, refresh every 60 seconds):\n\n```sql\nALTER CATALOG INTEGRATION postgres_iceberg_integration SET REFRESH_INTERVAL_SECONDS = 60;\n```\n\n### Test Auto-Refresh\n\nNow you can add another 100 rows to Postgres, see the Iceberg row count rise, along with your Snowflake table row count.\n\nIn Postgres:\n```sql\nINSERT INTO sensor_readings (sensor_id, device_type, reading_time, temperature, humidity, battery_pct)\nSELECT\n    (random() * 50)::int AS sensor_id,\n    (ARRAY['thermostat', 'weather_station', 'greenhouse', 'cold_storage', 'hvac'])[1 + (random() * 4)::int] AS device_type,\n    now() AS reading_time,\n    (random() * 40 + 5)::numeric(5,2) AS temperature,\n    (random() * 60 + 20)::numeric(5,2) AS humidity,\n    (random() * 80 + 20)::numeric(4,1) AS battery_pct\nFROM generate_series(1, 100);\n```\n\nIn Snowflake:\n```sql\nSELECT COUNT(*) FROM iot_sensors_from_postgres;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\n### Congratulations!\nYou have successfully synced data from an operational Postgres database to Snowflake using Iceberg tables, pg_lake, and pg_incremental — with no external ETL pipeline required.\n\n### What You Learned\n- How to set up an operational Postgres table and an Iceberg table using pg_lake\n- How to use pg_incremental to backfill and continuously sync data with exactly-once processing\n- How to create a Snowflake catalog integration that connects to Postgres-managed Iceberg metadata\n- How to query Iceberg data from Snowflake\n- How to manually refresh Iceberg tables and enable auto-refresh for continuous sync\n- How to tune refresh intervals for different workload requirements\n\n### Going Further\nThis guide covered a single-table sync with time-based incremental processing. In production, you can extend this pattern further:\n\n- **[pg_cron](https://github.com/citusdata/pg_cron)**: Schedule arbitrary SQL jobs directly inside Postgres — useful for aggregations, cleanup tasks, or custom sync logic beyond what pg_incremental handles.\n- **[Table partitioning](https://www.snowflake.com/en/engineering-blog/postgres-time-series-iceberg/)**: Combine Postgres declarative partitioning with pg_lake to build automated archiving — keep recent data in local Postgres partitions for fast writes, then offload older partitions to Iceberg on S3 for analytics in Snowflake.\n\n\n### Related Resources\n- [Introducing pg_lake: Integrate Your Data Lakehouse with Postgres](https://www.snowflake.com/en/engineering-blog/pg-lake-postgres-lakehouse-integration/) (blog)\n- [pg_lake GitHub Repository](https://github.com/Snowflake-Labs/pg_lake)\n- [pg_lake for Snowflake Documentation](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-pg_lake)\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d8af151ec1","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-b5f3993791",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-66557c32c5","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-ad15632acb","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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