Materialize Generic Create

Create Materialization (Any Connector)

Create a materialization using flowctl for any supported Estuary destination connector. This generic skill handles connectors that don't have a dedicated skill.

Check for a Dedicated Skill First

Before using this generic workflow, check if a dedicated materialize--create skill exists for the user's target destination. Dedicated skills have connector-specific guidance and troubleshooting that this generic skill can't provide. Only use this skill if no dedicated one exists.

Step 0: Find the Connector and Load Documentation

Find the connector

Query the connector registry to find available materialization connectors:

# List all materialization connectors
flowctl raw get --table connector_tags \
  --query 'protocol=eq.materialization' \
  --query 'select=image_tag,documentation_url' \
  --output yaml

To search for a specific connector by name:

flowctl raw get --table connector_tags \
  --query 'documentation_url=ilike.**' \
  --query 'protocol=eq.materialization' \
  --query 'select=image_tag,documentation_url' \
  --output yaml

Load the documentation

Use WebFetch to load the documentation_url returned from the query. This is the canonical docs page with prerequisites, config reference, and setup instructions.

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

Search kapa ai knowledge sources for "materialize  common issues"

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

Step 1: Gather Requirements

Before writing any YAML, ask the user:

1. Target system details? — Connection info (host, port, credentials, etc.)
2. Network path? — Direct connection, SSH tunnel, or other networking requirements
3. Non-default data plane? — Most users use the default. Ask if they need a non-default data plane.
4. Source collections? — Which Estuary collections to materialize
5. Any connector-specific requirements? — Check the docs loaded in Step 0
6. Hard deletes? — Off by default. Without it, deleted rows stay in the destination marked with _meta/op: 'd'. Enable to physically remove them.
7. Delta updates? — Off by default. Switches from one-row-per-key (standard merge) to append-only. Use for event logs or history tables.
8. Sync schedule? — Controls how often batches are written to the destination (default: 30 minutes, 0s for real-time). Affects latency and destination compute cost.

Step 2: Find the Correct Connector Version

Use the image_tag from the Step 0 query. If you need to re-query for a specific connector:

flowctl raw get --table connector_tags \
  --query 'documentation_url=eq.' \
  --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. Common prerequisites across materializations:

1. Destination accessible — Network connectivity from Estuary cloud
2. Credentials configured — User/service account with write permissions
3. Target location exists — Database, schema, bucket, etc.

Refer to the docs page for connector-specific prerequisite details.

Step 4: Create the Spec File

Build flow.yaml using the config reference from the docs. General structure:

materializations:
  //materialize-:
    endpoint:
      connector:
        image: :
        config:
          # Properties from the docs config reference
          # Each connector has different required fields
    bindings:
      - source: /
        resource:
          table: ""
          # Some connectors use different resource fields
          # (e.g., "topic" for Kafka, "index" for Elasticsearch)

Fill in the config section using the property reference from the connector's docs page.

For SSH tunnel (if supported by the connector), add networkTunnel.sshForwarding block — see docs.

Step 5: Publish

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

Step 6: Verify

# Check status
flowctl catalog status //materialize-

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

Status progression:

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

Troubleshooting

Materialization stuck in PENDING

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

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

Config validation errors

Cause: Config structure doesn't match the connector's schema

Fix: Re-read the docs page from Step 0 and verify all required properties are set with correct types and formatting.

"collection not found"

Cause: Source collection doesn't exist or wrong prefix

Fix:

flowctl catalog list --prefix / | grep collection

Verify the collection name matches exactly.

"no connector tag found for image"

Cause: Wrong image name or tag

Fix: Re-query connector_tags (Step 0) for the correct image and version.

Network connectivity failures

Cause: Destination not reachable from Estuary cloud

Fix:

1. Check firewall rules / security groups
2. Verify Estuary IP addresses are allowlisted (see docs)
3. Consider SSH tunnel for private networks
4. For local testing: use ngrok or similar

Connector-specific errors

For errors specific to the connector, search Kapa:

Search kapa ai knowledge sources for "materialize  "

Related Skills

• schema-field-selection — Control which fields are materialized
• 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: estuary
• Source: estuary/agent-skills
• License: Apache-2.0
• Homepage: https://estuary.dev

Install and usage instructions live in the source repository linked above.