| name | data-infrastructure-setup |
| description | Shared infrastructure setup for data migration and data validation — prerequisites, compute pool, worker config, and source database/schema capture. |
| parent_skill | migration |
| license | Proprietary. See License-Skills for complete terms |
Data Infrastructure Setup
Overview — Tell the User
Before starting any configuration, tell the user verbatim:
Snowflake's Data Migration and Validation Framework is a fault-tolerant, scalable system for Snowflake Migrations. It uses a two-component architecture: an Orchestrator that runs inside your Snowflake account (on Snowpark Container Services) and breaks migration work into parallel tasks, and one or more Workers that run in an environment of your choice, connect and read data from your source system, and upload it to your Snowflake account.
If your Snowflake account cannot use Snowpark Container Services or you are not using a compute pool for the orchestrator, you can run the Orchestrator and Workers locally.
You are in full control of what gets migrated and how. You decide which tables to include, how they are partitioned for extraction, whether to use incremental synchronization or full loads, and how many workers run in parallel. You can filter rows, remap column names and types, and stop or resume the process at any point without losing progress. Nothing runs until you review and approve the configuration.
The same infrastructure is used for validation. You choose what level of validation to run: schema validation, metrics validation, and row-level validation.
Supported sources: SQL Server, Redshift, Oracle, Teradata, PostgreSQL
Supported target: Snowflake
Architecture
┌──────────────────┐ ┌──────────────────────────────────────┐
│ Customer Env │ │ Snowflake Account │
│ ┌─────────────┐ │ │ ┌────────────────────────────────┐ │
│ │ Worker(s) │─┼─────┤ │ Orchestrator (SPCS) │ │
│ └──────┬──────┘ │ │ │ → migration + validation tasks │ │
│ │ reads │ │ └────────────────────────────────┘ │
│ ┌─────────────┐ │ │ ┌────────────────────────────────┐ │
│ │Source System │ │ │ │ Target Tables │ │
│ └─────────────┘ │ │ └────────────────────────────────┘ │
└──────────────────┘ └──────────────────────────────────────┘
- Orchestrator runs on SPCS (requires a compute pool). Handles migration workflows (break into tasks,
COPY INTO) and validation workflows (schema/metrics/row comparison).
- Worker runs locally. Reads from the source and uploads to a Snowflake stage (migration) or streams rows for comparison (validation). Not needed for most Iceberg migration strategies.
- Both can be stopped and resumed safely. The next
migrate_data() / validate_data() call auto-resumes the SPCS service. To suspend everything between waves and minimise idle SPCS / warehouse cost, use ./teardown/SKILL.md.
- Whenever a worker or orchestrator starts, tell the user that idle infrastructure can accrue Snowflake credits until teardown —
migrate_data / validate_data run responses include a cost_reminder field to relay.
Idempotency
If this sub-skill has already been completed in the current project — i.e., .scai/settings/DataExchangeWorkerConfig.toml exists with no remaining <placeholder> values — tell the user verbatim: "Data infrastructure already configured — running scai data doctor to confirm nothing has drifted." This runs live checks against Snowflake and the source, so say it out loud rather than running silently. Then run Level 1 Data Doctor (iterate until result.hasFailures is false) and return to the caller without re-prompting.
Otherwise, proceed through the steps below.
Worker Deployment Decisions
After the overview, walk the user through the following questions in order. Each answer determines the next step.
Preflight: Orchestrator placement (before compute pool)
Ask the user:
Orchestrator: Can this Snowflake account run the migration orchestrator on Snowpark Container Services using a compute pool? (If no — account cannot use SPCS, or you will not use a compute pool / SPCS for the orchestrator — use the local orchestrator path and skip Question 0.)
| Situation | Next step |
|---|
| Yes — SPCS + compute pool for the orchestrator | Continue to Question 0 (compute pool). |
| No — local orchestrator | Skip Question 0 (no compute pool setup for orchestrator placement). The data orchestrator will run locally (Python must be available; the CLI prepares the orchestrator environment). Continue to Question 1. |
Question 0: Compute pool SPCS
If the user chose local orchestrator in the preflight, do not ask Question 0 — continue from Question 1.
Ask the user:
The Orchestrator needs a compute pool to run inside your Snowpark Container Services. Do you already have a compute pool, or will you need to set one up?
| Answer | Action |
|---|
| I have one | Verify it is active (see below), then save it and continue to Question 1. |
| I need to set one up | Route to → ./compute-pool-setup/SKILL.md — guide the user through creating and configuring a compute pool. After that skill completes, return here and run the steps below. |
Save compute pool to MCP config:
configure(compute_pool="<COMPUTE_POOL>")
The orchestrator SPCS service starts automatically when we start migrating or validating data later on.
Question 1: Snowflake role and privileges
Ask the user:
What Snowflake role will you use for the migration? The Orchestrator needs a role with USAGE on the SNOWCONVERT_AI database and its DATA_MIGRATION and DATA_VALIDATION schemas.
| Situation | Action |
|---|
| Role has the required privileges | Continue to Question 2. |
| Role needs grants | Run the grants below on behalf of the user (or show them to run with an admin role), then continue. |
| DATA_MIGRATION_SERVICE already exists (created by another role) | The executing role also needs OPERATE and MONITOR on the service. Run the extended grants below, then continue. |
Standard grants:
GRANT USAGE ON DATABASE SNOWCONVERT_AI TO ROLE <your_role>;
GRANT USAGE ON SCHEMA SNOWCONVERT_AI.DATA_MIGRATION TO ROLE <your_role>;
GRANT USAGE ON SCHEMA SNOWCONVERT_AI.DATA_VALIDATION TO ROLE <your_role>;
Extended grants (when the service was created by another role):
GRANT OPERATE, MONITOR ON SERVICE SNOWCONVERT_AI.DATA_MIGRATION.DATA_MIGRATION_SERVICE TO ROLE <your_role>;
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA SNOWCONVERT_AI.DATA_MIGRATION TO ROLE <your_role>;
GRANT ALL PRIVILEGES ON ALL PROCEDURES IN SCHEMA SNOWCONVERT_AI.DATA_MIGRATION TO ROLE <your_role>;
GRANT ALL PRIVILEGES ON ALL STAGES IN SCHEMA SNOWCONVERT_AI.DATA_MIGRATION TO ROLE <your_role>;
Question 2: Snowflake warehouse
Ask the user:
Which Snowflake warehouse should be used for data migration and validation? The Orchestrator uses this warehouse to execute COPY INTO statements and run validation queries. Your Snowflake connection must have a warehouse configured — without one, data migration will fail silently.
| Situation | Action |
|---|
| User provides a warehouse name | Verify the warehouse exists and the role from Question 1 has USAGE on it. Confirm that ~/.snowflake/config.toml has warehouse = "<WAREHOUSE>" under the active connection. If not, help the user add it. Continue to Question 3. |
| User is unsure | Run SHOW WAREHOUSES; to list available warehouses and help them pick one. For large migrations, recommend a dedicated warehouse to avoid contention with other workloads. |
Question 3: Worker deployment
Ask the user:
How will you run the Data Exchange Worker?
| Answer | Route |
|---|
| Single worker on one machine | → ./worker-local-setup/SKILL.md — handles install and start on one host |
| Multiple workers on separate VMs or servers | → ./worker-distributed-setup/SKILL.md — handles affinity config and per-host copy instructions |
| Snowpark Container Services (SPCS) | → ./worker-spcs/SKILL.md — handles image selection, Snowflake Secrets, and CREATE SERVICE |
| Kubernetes on an external cluster | → ./worker-k8s-external/SKILL.md — handles image selection, Kubernetes Secrets, and Deployment manifest |
Stop here for worker deployment — the routed sub-skill handles install/start, then returns control here. After it returns (or after you complete a path that does not route to a worker sub-skill), run the Data Doctor section below before returning to the caller.
Data Doctor (Level 1)
This is the single place Level 1 Data Doctor runs — after any worker deployment path (local, distributed, SPCS, Kubernetes) returns control here. The worker sub-skills do not run it themselves.
Run Level 1 Data Doctor. Do not start migration or validation setup until result.hasFailures is false. Iterate with the user on failures and warnings using each check's suggestion; you do not need to fix everything automatically.
Checklist
- [ ] Compute pool registered (configure(compute_pool=...)) — when using SPCS orchestrator
- [ ] Snowflake role has DATA_MIGRATION / DATA_VALIDATION usage (and service grants if needed)
- [ ] Warehouse configured on the Snowflake connection
- [ ] Worker config complete (no <placeholder> values) — unless pure Iceberg
- [ ] Level 1 scai data doctor — no Fail checks
Return control to the calling skill.
Reference
