Snowflake destination

The Snowflake destination is in private alpha and available only to approved organizations. Request access before following this guide.

Snowflake is a managed data platform. Supabase Pipelines writes an append-only change history for each replicated Postgres table to Snowflake.

Prepare resources, configure the destination, then query replicated data.

Source table requirements#

Required REPLICA IDENTITY depends on the operations enabled in the Postgres publication:

Set full replica identity before publishing updates:

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alter table public.your_table replica identity full;

REPLICA IDENTITY FULL increases WAL volume, but lets Pipelines construct complete new rows when Postgres omits unchanged out-of-line TOAST values. The setting applies only to new WAL records. If retained WAL already contains an incompatible update, restart replication for the affected table after changing the setting.

Prepare Snowflake resources#

Create a dedicated Snowflake database, schema, role, and service user for Pipelines. Keep the schema otherwise empty to avoid ownership conflicts. Use unquoted identifiers for the service user and role. Pipelines converts the account and user names to uppercase during authentication.

Run the following as a Snowflake administrator. Change the example names as needed:

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create role if not exists PIPELINES_ROLE;
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create user if not exists PIPELINES_USER
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  type = service;
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grant role PIPELINES_ROLE to user PIPELINES_USER;
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alter user PIPELINES_USER set default_role = PIPELINES_ROLE;
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create database if not exists PIPELINES_DB;
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create schema if not exists PIPELINES_DB.REPLICATED;
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grant usage on database PIPELINES_DB to role PIPELINES_ROLE;
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grant usage on schema PIPELINES_DB.REPLICATED to role PIPELINES_ROLE;
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grant create table on schema PIPELINES_DB.REPLICATED to role PIPELINES_ROLE;

The pipeline role must own destination tables so it can alter, truncate, or drop them. Don't pre-create destination tables under another role.

Snowflake creates each table's managed default pipe, <TABLE>-STREAMING, automatically. No virtual warehouse, stage, or manually created pipe is required. See Snowpipe Streaming access privileges for the ingestion requirements.

Use a separate role and warehouse for downstream queries and transformations.

Keep the SQL and streaming roles aligned#

Pipelines uses two Snowflake interfaces:

• SQL requests use the optional Role configured in the Dashboard. When Role is empty, they use the user's default role.
• Snowpipe Streaming uses the user's DEFAULT_ROLE. It does not use the optional Role setting.

Set the pipeline role as the service user's DEFAULT_ROLE. Leave Role empty or set it to that same role. Otherwise, SQL validation can succeed while streaming fails.

Generate a key pair#

Pipelines authenticates with an RSA key pair. Snowflake requires a key of at least 2048 bits and recommends PKCS #8. Choose one of the following commands to generate rsa_key.p8.

For an unencrypted private key:

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openssl genrsa 2048 | openssl pkcs8 -topk8 \
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  -inform PEM -out rsa_key.p8 -nocrypt

Or, for a passphrase-protected private key:

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openssl genrsa 2048 | openssl pkcs8 -topk8 -v2 des3 \
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  -inform PEM -out rsa_key.p8

Derive the public key:

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openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub

Register only the public-key body with the service user. Omit the BEGIN PUBLIC KEY and END PUBLIC KEY lines:

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alter user PIPELINES_USER set rsa_public_key = '<public-key-body>';

Keep rsa_key.p8 and its passphrase secret. Don't commit them, paste them into logs, or send them to support. The Dashboard accepts unencrypted PKCS #1 or PKCS #8 keys, and encrypted PKCS #8 keys with a passphrase. It does not support encrypted PKCS #1 keys.

See Snowflake key-pair authentication to verify the public-key fingerprint and rotate keys with RSA_PUBLIC_KEY_2.

Find the account identifier#

Run this query in Snowflake:

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select current_organization_name() || '-' || current_account_name();

Enter the result as Account ID, for example MYORG-MYACCOUNT. Do not enter a full URL or dotted locator-and-region hostname. Account IDs can contain up to 63 characters. Legacy one-part account locators are also accepted. See Snowflake account identifiers for details.

Configure Snowflake as a destination#

Follow Set up Pipelines and select Snowflake. Enter these settings:

Click Create and start pipeline and complete the validation and cost confirmations.

Enter the database and schema identifiers exactly as stored in Snowflake. Unquoted identifiers are stored in uppercase. Choose an account near the managed pipeline region.

How it works#

Pipelines uses Snowflake's SQL REST API to validate the database and schema, create, evolve, and recreate destination tables, and apply source TRUNCATE operations. It sends initial and ongoing row data through Snowpipe Streaming.

Validation checks authentication, database and schema visibility, and that QUOTED_IDENTIFIERS_IGNORE_CASE is FALSE. It does not verify that the role can create or own tables or write through Snowpipe Streaming.

Destination table names#

Pipelines maps each Postgres schema and table pair to one Snowflake table name. It doubles existing underscores, joins the names with one underscore, and uppercases the result:

Postgres schema and table names cannot start or end with _ or contain " or ;. Names that differ only in case map to the same Snowflake name. Use lowercase Postgres names to avoid collisions. Source column names are preserved as quoted identifiers, except for the reserved metadata names.

Append-only change history#

Each destination table contains the replicated source columns plus two VARCHAR NOT NULL metadata columns:

The metadata names are reserved and can't be used by source columns. Initial-sync rows use insert and the shared sequence number 0000000000000000/0000000000000000.

Snowflake tables are an event history, not a current-state replica:

• An insert appends the new row.
• An update appends the complete new row. It does not append a before image.
• A delete appends the complete old row for REPLICA IDENTITY FULL. For a primary-key or USING INDEX identity, it sends only the identity columns. Other columns can contain destination defaults or NULL; do not treat them as the deleted row's original values.
• A source TRUNCATE truncates the Snowflake table, resets its streaming state, and does not append a truncate event.

The sequence number orders changes but is not a globally unique event ID. Snowpipe committed offsets suppress routine replay; consumers must still tolerate duplicate processing.

A table restart drops the Snowflake table and managed streaming state, erasing its history. It cannot recover past events. Removing a table from the publication leaves its destination history in place.

Query replicated data #

Use the replicated change history to build a current-state dataset for reports and analytics. Pipelines maintains the history table. You create and maintain the queries, views, or dynamic tables that read it.

Before you start#

The examples use public.orders, replicated to PIPELINES_DB.REPLICATED.PUBLIC_ORDERS, with source columns id and status. Replace these names with your own. Wait for the table's initial sync to finish before treating the result as a complete replica.

Choose a unique, non-null identity that stays the same when a row is updated. The examples use id. For a composite key, include every key column in partition by, such as partition by "tenant_id", "id". Include those columns in the publication and in delete events. REPLICA IDENTITY FULL alone does not make rows unique.

Use a separate analytics role and warehouse, with a schema outside the Pipelines-managed REPLICATED schema for derived objects. The examples use ANALYTICS_ROLE, ANALYTICS_WH, and PIPELINES_DB.ANALYTICS. Ask your Snowflake administrator to prepare these resources and grant the analytics role:

• USAGE on the warehouse, database, and both schemas.
• SELECT on the replicated table.
• CREATE VIEW on the analytics schema to create a view, or CREATE DYNAMIC TABLE to create a dynamic table.

The role must be available to the Snowflake user running the examples. Keep ownership of the replicated table with PIPELINES_ROLE. See Snowflake's dynamic table access control for the full privilege requirements.

Query current state#

Run these statements in a Snowflake SQL worksheet with your analytics role:

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use role ANALYTICS_ROLE;
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use warehouse ANALYTICS_WH;
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select "id", "status"
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from PIPELINES_DB.REPLICATED.PUBLIC_ORDERS
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qualify row_number() over (
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  partition by "id" order by "_cdc_sequence_number" desc
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) = 1
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and "_cdc_operation" != 'delete';

The result contains one row per identity whose latest operation is not delete. Ordering by the fixed-width sequence string selects the latest change. Repeated copies of the same event produce one result row. Keep the double quotes around source and metadata column names because Pipelines creates them as case-sensitive identifiers.

Keep the delete condition in qualify. A where "_cdc_operation" != 'delete' condition would remove delete events before ranking and could bring back an older row. Snowflake's QUALIFY reference explains this evaluation order.

To reuse the query from an analytics tool, save it as a view:

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create view PIPELINES_DB.ANALYTICS.ORDERS_CURRENT_VIEW as
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select "id", "status"
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from PIPELINES_DB.REPLICATED.PUBLIC_ORDERS
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qualify row_number() over (
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  partition by "id" order by "_cdc_sequence_number" desc
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) = 1
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and "_cdc_operation" != 'delete';

A regular view stores the query definition, not a separate copy of its results. Each read derives current state from the history available to that query. See Snowflake's comparison of views and dynamic tables.

Materialize with a dynamic table#

A dynamic table stores the query result and refreshes it as the replicated history changes. Use it when you want to query a maintained current-state dataset without defining a scheduled merge task.

1. Ask the owner of the replicated table to enable change tracking in Snowflake. This is a table setting, not a change to the replicated columns or data. Run as PIPELINES_ROLE, or another role that inherits ownership:

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   alter table PIPELINES_DB.REPLICATED.PUBLIC_ORDERS
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     set change_tracking = true;

   The analytics role does not own the replicated table, so it cannot enable change tracking automatically when creating the dynamic table. See Snowflake's change tracking requirements.

2. Switch to the analytics role and create the dynamic table:

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   use role ANALYTICS_ROLE;
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   use warehouse ANALYTICS_WH;
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   create dynamic table PIPELINES_DB.ANALYTICS.ORDERS_CURRENT
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     target_lag = '5 minutes'
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     warehouse = ANALYTICS_WH
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     refresh_mode = incremental
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     initialize = on_create
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   as
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   select "id", "status"
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   from PIPELINES_DB.REPLICATED.PUBLIC_ORDERS
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   qualify row_number() over (
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     partition by "id" order by "_cdc_sequence_number" desc
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   ) = 1
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   and "_cdc_operation" != 'delete';

   initialize = on_create populates the dynamic table before creation finishes. Explicit refresh_mode = incremental makes creation fail if your adapted query cannot refresh incrementally, instead of choosing a full refresh through AUTO. See Snowflake's refresh modes and CREATE DYNAMIC TABLE reference.

3. Check the refresh mode and read the materialized rows:

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   show dynamic tables like 'ORDERS_CURRENT'
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     in schema PIPELINES_DB.ANALYTICS;
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   select "id", "status"
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   from PIPELINES_DB.ANALYTICS.ORDERS_CURRENT;

   Confirm that refresh_mode is INCREMENTAL and scheduling is running. Use Snowflake's refresh monitoring to check the last successful refresh and any errors. After an insert, update, or delete reaches the replicated table, the next successful refresh reflects it in ORDERS_CURRENT.

The five-minute target_lag is an example freshness target relative to the history in Snowflake. It is not a fixed refresh schedule or an end-to-end latency guarantee from Postgres. Pipeline replication lag and dynamic-table refresh lag both affect freshness. See Snowflake's target lag guide.

Dynamic-table refreshes consume warehouse compute, and the materialized results consume storage. These costs are additional to ingestion and querying. Start with a freshness target that meets your reporting needs and measure a representative workload. A dedicated warehouse helps isolate refresh costs. See Snowflake's dynamic table cost guide.

Use streams and tasks#

Snowflake streams and tasks can maintain a separate table with scheduled MERGE statements. Use this option when you need control over the update procedure or schedule. Snowflake's SCD Type 1 examples compare this approach with dynamic tables.

Adapt the merge to Pipelines' "_cdc_operation" and "_cdc_sequence_number" columns. A stream on the history table sees appended rows, including rows representing source updates and deletes. Your job must interpret those operations, load existing history, tolerate replay, and rebuild current state after a source truncate or pipeline table reset.

Maintain derived objects#

Pipelines maintains the replicated history table, but does not update your view or dynamic-table definitions.

Recreating a dynamic table initializes its contents again and uses compute. See Snowflake's dynamic table modification guide for changes that require reinitialization.

Type mapping#

Pipelines creates Snowflake columns with these mappings:

Pipelines uses VARCHAR for character and text types, numeric, time with time zone, interval, uuid, bytea, bit strings, and custom or unknown types. bytea values are lowercase hexadecimal strings. Pipelines stores these values in serialized form, not as native Snowflake types.

Additional limits apply:

• Multi-dimensional arrays aren't supported. Non-default lower bounds on one-dimensional arrays aren't preserved.
• Non-finite real and double precision values are rejected. Non-finite numeric values are preserved as strings in VARCHAR columns.
• An uncompressed serialized row larger than 2 MiB is rejected.
• Source primary-key, unique, check, length, precision, and nullability constraints aren't copied. Only the two CDC metadata columns are NOT NULL.

Schema change support#

Pipelines supports:

• Adding, renaming, or dropping columns
• Adding or removing published columns on tracked tables

Replicated columns remain nullable in Snowflake, and changes to existing column defaults are not propagated. Initial table creation can copy compatible literal defaults. Columns added in Postgres can copy string, numeric, or boolean literal defaults; other defaults are omitted. Postgres still supplies the source values through replication.

Schema changes also affect stored history: renaming a column changes its name in old events, dropping it removes its historical values, and adding one with a default can populate older rows.

Previously excluded columns are added without defaults, leaving historical events NULL for those columns. Removing a published column drops its destination values; adding it again does not restore them.

For type changes, unsupported changes, and interrupted schema changes, see the shared schema-change behavior and recovery. Apart from enabling change tracking, do not alter managed destination objects manually.

Troubleshooting#

Use pipeline monitoring to inspect errors. For unresolved failures, contact support with the pipeline ID and error details.

Additional resources#

• Snowflake key-pair authentication
• Snowflake account identifiers
• Snowpipe Streaming default pipe
• Snowpipe Streaming access control