Semantic View Patterns

Overview

Interactive, end-to-end tutorials for 25 Snowflake Semantic View (SV) modeling patterns. Each pattern ships with annotated DDL and YAML, seed data, and live SEMANTIC_VIEW() queries. Two modes:

• Tutorial mode — deploy a working example into your account, walk through the DDL/YAML, run live queries, then clean up. Triggers: "walk me through", "teach me", "what patterns are available".
• Apply mode — read your existing SV (or table list), map the pattern's structural roles to your columns, and generate adapted DDL/YAML. Triggers: "apply X to my SV", "my tables are…", "help me implement".

If ambiguous, ask which mode the user wants.

Available Patterns

range_join, asof_join, multi_path_metrics, shared_degenerate_dimension, semi_additive_metric, window_metrics, derived_metrics, time_intelligence, entity_facts, variables, multi_fact_table, ai_metadata, tags, introspection, fact_as_relationship_key, system_explain_semantic_query, caller_rights ⚠️ ACCOUNTADMIN, standard_sql, inline_sv ⚠️ PrPr, materialization ⚠️ PrPr, scoped_dataset ⚠️ PrPr, row_access_policies ⚠️ ACCOUNTADMIN, role_playing_dimensions, accumulating_snapshot, sv_diagnostics.

Each pattern lives at <skill_dir>/snippets/<name>/ with README.md, schema.sql, seed_data.sql, semantic_view.sql, semantic_view.yaml, queries.sql.

Authoring Format

Ask whether to use DDL (CREATE SEMANTIC VIEW) or YAML (SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML). Skip if the user already specified.

-- Verify (dry-run):
CALL SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML('DB.SCHEMA', $$ <yaml> $$, TRUE);
-- Deploy:
CALL SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML('DB.SCHEMA', $$ <yaml> $$);
-- Export:
SELECT SYSTEM$READ_YAML_FROM_SEMANTIC_VIEW('DB.SCHEMA.SV');

DDL-only features (no YAML equivalent): AI_QUESTION_CATEGORIZATION, WITH TAG, MAX_STALENESS, ADD MATERIALIZATION, VARIABLES, ASOF joins, inline subqueries in TABLES. Apply these post-deploy via ALTER SEMANTIC VIEW.

Tutorial Workflow

1. Pick the pattern from the user's request, or list patterns and ask.
2. Pre-flight: probe SHOW DATABASES LIKE 'SNOWFLAKE_LEARNING_DB'. If present, offer it as default; otherwise ask for DATABASE.SCHEMA, role, and warehouse. Track every object you create for cleanup. Access-control snippets use hardcoded DBs (SV_CALLER_TEST, RAP_TEST) and need ACCOUNTADMIN.
3. Read the snippet files for the chosen pattern.
4. Act 1 — Problem: synthesize what the pattern solves in 2–3 sentences. Hint: "Ask 'tell me about other approaches' to see how Power BI / Tableau / dbt handle this."
5. Act 2 — Data Model: walk through schema.sql, deploy schema + seed via snowflake_sql_execute (substitute SNIPPETS.PUBLIC → TARGET_DB.TARGET_SCHEMA), then SELECT * LIMIT 5 from each table.
6. Act 3 — SV Pattern: excerpt and annotate TABLES/RELATIONSHIPS/FACTS/DIMENSIONS/METRICS sections, then deploy.
7. Act 4 — Live Queries: run each numbered query in queries.sql, narrate the actual output values.
8. Act 5 — Gotchas: read ## What Doesn't Work and present each trap plainly.
9. Cleanup: list every object created, offer to drop them via the -- CLEANUP block.

Apply Workflow

1. Match the request to the closest pattern; confirm with the user.
2. Read README.md and the chosen format file (DDL or YAML). Skip schema/seed/queries.
3. Get the user's existing SV: pasted text, file path, or GET_DDL('semantic_view', 'DB.SCHEMA.SV'). If from scratch, take table descriptions.
4. Show a mapping table (snippet role → user column) and have them fill it in. Ask only for what the pattern requires.
5. Generate adapted output: a diff for existing SVs, a complete definition for new ones. Use the user's exact table/column names. For YAML, include the dry-run + deploy snippet.
6. Flag schema-specific gotchas (composite keys, non-standard date grain).
7. Offer to deploy, run test queries, or layer another pattern.

Common Mistakes

• Not confirming mode or format first — a Tutorial-mode answer to an Apply-mode user wastes both your time. Ask once at the start.
• Reverting to snippet names in adapted output — use the user's FACT_ORDERS.ORDER_DATE, not the snippet's FACT_SALES.SALE_MONTH.
• Pasting the README verbatim — synthesize, don't dump.
• Skipping cleanup — always list created objects and offer to drop them.
• Generating YAML for DDL-only features silently — call out ASOF, VARIABLES, WITH TAG, MATERIALIZATION and emit the post-deploy DDL.
• Cardinality wrong on a relationship — silently inflates metrics; verify with SHOW METRICS and a row-count sanity query.
• Fan trap from two facts joined through a shared dim without USING — disambiguate with the USING (relationship) clause on each metric.
• Forgetting to switch roles in access-control snippets before running query blocks.