materialize-snowflake-create

name	materialize-snowflake-create
description	Create a Snowflake materialization to stream Estuary collections into Snowflake tables. Use when setting up Snowflake as a destination for captured data. Use when user says "send to Snowflake", "materialize to Snowflake", "Snowflake destination", or "load into Snowflake".

Create Snowflake Materialization

Create a Snowflake materialization using flowctl to stream data from Estuary collections into Snowflake tables.

Applies to: materialize-snowflake

Step 0: Load Connector Documentation

Before proceeding, fetch the official connector docs for prerequisites, config reference, and setup instructions.

Load the docs page: https://docs.estuary.dev/reference/Connectors/materialization-connectors/Snowflake/

Use WebFetch to load this page. It covers:

• Prerequisites (role/user creation, key-pair setup, warehouse)
• Full config property reference
• Authentication setup (JWT key-pair)
• Advanced options (delta updates, Snowpipe Streaming, hard deletes, sync schedule)

Search Kapa for tribal knowledge (if the Estuary MCP is configured):

Search kapa ai knowledge sources for "materialize snowflake common issues"

If Kapa MCP is not configured, the user can set it up: https://docs.estuary.dev/features/mcp-integration/

This skill provides the flowctl workflow and troubleshooting that docs don't cover.

Step 1: Gather Requirements

Before writing any YAML, ask the user:

1. Account URL? — Snowflake account identifier (e.g., xy12345.us-east-1.snowflakecomputing.com)
2. Database, schema, warehouse? — Target database, schema, and warehouse (optional — uses user's default if not specified)
3. Authentication method? — Must be JWT/key-pair (Snowflake deprecated password auth April 2025)
4. Timestamp type? — This is required. Help the user choose (see guidance below).
5. Non-default data plane? — Most users use the default. Ask if they need a non-default data plane.
6. Source collections? — Which Estuary collections to materialize
7. Hard deletes? — Off by default. Without it, deleted rows stay in Snowflake marked with _meta/op: 'd'. Enable to physically remove them.
8. Delta updates? — Off by default. Switches from one-row-per-key (standard merge) to append-only via Snowpipe Streaming. Use for event logs or history tables.
9. Sync schedule? — Controls how often batches are written to Snowflake (default: 30 minutes, 0s for real-time). Affects latency and warehouse cost.

CRITICAL: JWT/key-pair authentication is required. Snowflake deprecated username/password authentication in April 2025. If the user asks about password auth, explain they must use key-pair instead.

Choosing a timestamp type

This setting controls how Estuary creates timestamp columns in Snowflake. It's required and frequently confusing — especially for users migrating from Fivetran (which defaults to NTZ).

Type	Stores as	Best for	Spec value
TIMESTAMP_LTZ	UTC internally, displays in session timezone	Most users — preserves timezone, prevents ambiguity	TIMESTAMP_LTZ
TIMESTAMP_TZ	UTC + offset	When you need to preserve the original timezone offset (note: DST info is lost — only offset is stored)	TIMESTAMP_TZ
TIMESTAMP_NTZ (normalize)	Wall-clock time, no timezone (converted to UTC first)	New tasks that need NTZ — e.g., migrating from Fivetran where downstream queries assume NTZ	TIMESTAMP_NTZ (normalize to UTC)
TIMESTAMP_NTZ (discard)	Wall-clock time, no timezone (raw, timezone stripped)	Existing tasks already using NTZ that can't change	TIMESTAMP_NTZ (discard TZ)

Default recommendation: Use TIMESTAMP_LTZ unless you have a specific reason not to. It stores UTC and lets Snowflake handle display conversion per session — this avoids the "my timestamps shifted by N hours" problems that NTZ causes.

Migrating from Fivetran? Fivetran defaults to NTZ, so existing dbt models and queries may assume NTZ columns. Use TIMESTAMP_NTZ (normalize to UTC) to match that expectation while still normalizing to UTC before storage.

Important: If the user has explicitly set TIMESTAMP_TYPE_MAPPING in Snowflake (at account/role/user level), the Estuary setting must match. If it's left at Snowflake's default (unset), Estuary will use LTZ regardless of Snowflake's default being NTZ — this is intentional.

Step 2: Find the Correct Connector Version

Always use the latest numbered version tag. Query the connector registry:

flowctl raw get --table connector_tags \
  --query 'documentation_url=eq.https://go.estuary.dev/materialize-snowflake' \
  --query 'select=image_tag,documentation_url' \
  --output yaml

Use the returned image_tag — never hardcode a version.

Step 3: Help User Complete Prerequisites

Walk the user through prerequisites from the docs loaded in Step 0:

1. Role and user — Create a dedicated role and service user
2. Key-pair generation — Generate PKCS#8 private key and extract public key
3. Assign public key — Set RSA_PUBLIC_KEY on the Snowflake user
4. Database/schema/warehouse — Create and grant permissions

Refer to the docs page for exact SQL and shell commands.

Step 4: Create the Spec File

Build flow.yaml using the config reference from the docs. Minimal required config:

materializations:
  <TENANT>/<PATH>/materialize-snowflake:
    endpoint:
      connector:
        image: ghcr.io/estuary/materialize-snowflake:<VERSION>
        config:
          host: "<ACCOUNT>.snowflakecomputing.com"
          database: "<DATABASE>"
          schema: "<SCHEMA>"
          warehouse: "<WAREHOUSE>"
          timestamp_type: "TIMESTAMP_LTZ"
          role: "<ROLE>"
          credentials:
            auth_type: "jwt"
            user: "<USERNAME>"
            private_key: "<PKCS8-PRIVATE-KEY>"
    bindings:
      - source: <TENANT>/<collection-path>
        resource:
          table: "<TABLE_NAME>"

Important: timestamp_type is required. Use the value from the table in Step 1 (e.g., TIMESTAMP_LTZ, TIMESTAMP_TZ, TIMESTAMP_NTZ (normalize to UTC), or TIMESTAMP_NTZ (discard TZ)).

Step 5: Publish

flowctl catalog publish --source flow.yaml --auto-approve

Step 6: Verify

# Check status
flowctl catalog status <TENANT>/<PATH>/materialize-snowflake

# View logs
flowctl logs --task <TENANT>/<PATH>/materialize-snowflake --since 5m | jq -c '{ts, message}'

Status progression:

1. PENDING — Normal for ~30 seconds during shard assignment
2. BACKFILLING — Initial data sync from collection
3. OK — Running normally with real-time updates

Troubleshooting

"timestamp_type is required"

Cause: Missing timestamp_type in config

Fix: Add timestamp_type — see the choosing guide in Step 1. Default: TIMESTAMP_LTZ.

Timestamps shifted by N hours after backfill

Cause: Table was re-created with a different timestamp type (usually NTZ → LTZ)

Fix:

1. Set TIMESTAMP_TYPE_MAPPING explicitly on the Snowflake user to match the Estuary config:

   ALTER USER <user> SET TIMESTAMP_TYPE_MAPPING = 'TIMESTAMP_NTZ';
   

   Note: the level column in SHOW PARAMETERS LIKE 'TIMESTAMP_TYPE_MAPPING' must show USER (not blank) for the connector to recognize the setting.
2. Backfill the affected bindings — the fix only takes effect on new or re-backfilled tables.

Changed timestamp_type but columns still use the old type

Cause: Changing timestamp_type only affects new or re-backfilled tables — existing columns keep their original type

Fix: Backfill the affected bindings after changing the setting. Increment the backfill counter on each binding, or use the UI backfill button.

"JWT token is invalid" or authentication failures

Cause: Key-pair mismatch or incorrect format

Fix:

1. Verify public key fingerprints match (local vs Snowflake)
2. Ensure private key includes BEGIN/END markers
3. Ensure private key is PKCS#8 format (not PKCS#1)
4. Check user has correct RSA_PUBLIC_KEY set

"Warehouse not found" or warehouse errors

Cause: Warehouse doesn't exist or user lacks USAGE permission

Fix: Verify the warehouse exists and the role has USAGE — see docs for SQL.

"Schema does not exist"

Cause: Schema not created or user lacks permission

Fix: Create the schema and grant permissions — see docs for SQL.

Materialization stuck in PENDING

Wait 30-60 seconds — this is normal during shard assignment. If still stuck:

flowctl logs --task <TENANT>/<PATH>/materialize-snowflake --since 5m | jq 'select(.level == "error")'

"User cannot be used for login" or "Password required"

Cause: Using TYPE = SERVICE user with password auth, or deprecated auth method

Fix: Service users require JWT authentication. Ensure auth_type: "jwt" with valid private key.

Related Skills

• connector-disable-enable — Pause/restart existing materializations
• connector-delete-recreate — Nuclear option for stuck materializations
• estuary-logs — Deep log analysis
• estuary-catalog-status — Status checking