> ## Documentation Index > Fetch the complete documentation index at: https://docs.credibledata.com/llms.txt > Use this file to discover all available pages before exploring further. # Migrate from Snowflake Semantic Views > Convert your Snowflake semantic views into governed Malloy semantic models Snowflake semantic views and Malloy are close cousins — both are dimension, measure, and relationship graphs over SQL tables. That makes this one of the cleanest migrations: Credible reads your semantic view definition, maps it almost concept-for-concept to Malloy, and — where you connect it — validates against Snowflake's own verified queries. ## What Credible Reads Your native **`SEMANTIC VIEW`** objects — logical tables, relationships, facts, dimensions, and metrics — or any legacy **Cortex Analyst YAML** models. The agent works from the exported definition (the `CREATE SEMANTIC VIEW` DDL or the YAML). Connecting to Snowflake — via SQL (`DESCRIBE SEMANTIC VIEW`, `SHOW SEMANTIC VIEWS`) or the **Snowflake-managed MCP server** (Cortex) — is optional: it lets the agent read views in place and run parity queries under Snowflake's own RBAC. ## What Comes Across The everyday modeling carries over. Here's how the bigger pieces land — and where they get better: | In Snowflake | In Credible | | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | | Semantic view: logical tables & relationships | Malloy **sources** and joins | | Dimensions, facts, metrics | Dimensions, row-level facts, and measures — facts feed measures, so you can re-aggregate at any grain | | `WITH SYNONYMS` / `COMMENT` | `#(doc)` / `#(index)`, indexed by the [Context Engine](/how-to/analyzing/overview) | | Verified queries | Parity fixtures and named views | | `PUBLIC` / `PRIVATE`, RBAC | **Fine-grained access control in the model**, versioned and enforced on every surface | | Cortex Analyst / BI on top | Every surface — agents over MCP, [data apps](/how-to/analyzing/data-apps), [notebooks](/how-to/analyzing/workspaces), dashboards, APIs | ## The Migration Flow Credible [reads](/how-to/migrating/overview) the semantic view definition, translates logical tables and relationships to sources and joins (facts to row-level dimensions, metrics to measures), enriches with `#(doc)`/`#(index)` tags seeded from synonyms and comments, and — where you connect it — validates against Snowflake. ## What Credible Handles * **Two coexisting formats** — a deployment may use native `SEMANTIC VIEW` objects, legacy Cortex Analyst YAML on a stage, or both. Credible detects which and reads each accordingly. * **Facts vs. metrics** — a fact is a row-level expression; a metric is its aggregation. Credible keeps them distinct (fact → row-level dimension, metric → measure) so you can still re-aggregate at different grains. Collapsing them would lose that. * **Verified queries** are the best thing to validate against — Credible replays each question→SQL pair and diffs the result against the migrated Malloy. (Verified SQL references logical names, not physical tables.) ## Before & After A `CREATE SEMANTIC VIEW` statement: ```sql theme={"languages":{"custom":["/languages/motly.tmGrammar.json","/languages/malloy.tmGrammar.json"]}} CREATE OR REPLACE SEMANTIC VIEW sales.sales_analytics TABLES ( orders AS sales.orders PRIMARY KEY (order_id) WITH SYNONYMS = ('purchase_orders') COMMENT = 'Order transactions at the order grain', customers AS sales.customers PRIMARY KEY (customer_id) ) RELATIONSHIPS ( orders_to_customers AS orders (customer_id) REFERENCES customers (customer_id) ) FACTS ( orders.line_amount AS orders.amount COMMENT = 'Per-row order amount in USD', PRIVATE orders.is_shipped AS CASE WHEN orders.status = 'shipped' THEN 1 ELSE 0 END ) DIMENSIONS ( orders.order_date AS CAST(orders.created_at AS DATE) COMMENT = 'Date the order was placed', customers.region AS customers.geographic_region ) METRICS ( orders.total_revenue AS SUM(orders.line_amount) COMMENT = 'Total revenue in USD', orders.shipped_revenue AS SUM(orders.line_amount * orders.is_shipped), orders.shipped_revenue_pct AS SUM(orders.line_amount * orders.is_shipped) / SUM(orders.line_amount) ); ``` ```malloy theme={"languages":{"custom":["/languages/motly.tmGrammar.json","/languages/malloy.tmGrammar.json"]}} source: orders is conn.table('sales.orders') extend { primary_key: order_id join_one: customers is conn.table('sales.customers') on customer_id dimension: #(doc) Date the order was placed. Also known as: order_day order_date is created_at::date #(doc) Per-row order amount in USD (fact) line_amount is amount #(doc) Row-level shipped flag (fact) is_shipped is pick 1 when status = 'shipped' else 0 measure: #(doc) Total revenue in USD # currency total_revenue is sum(line_amount) #(doc) Revenue from shipped orders # currency shipped_revenue is sum(line_amount) { where: status = 'shipped' } #(doc) Share of revenue that shipped # percent shipped_revenue_pct is shipped_revenue / total_revenue } ``` `FACTS` become row-level dimensions that feed measures; `METRICS` become measures. `WITH SYNONYMS` and `COMMENT` fold into `#(doc)` (Malloy has no synonym primitive, so alternate names go in the description for retrieval), and `PRIVATE` facts map to access modifiers so they feed measures without being independently queryable. **More than a reformat.** Semantic views map almost 1:1, so the gain is what surrounds them — composable follow-up queries, [Context Engine](/how-to/analyzing/overview) discovery, and one model that reaches agents, apps, and BI beyond Cortex. [See what you gain →](/how-to/migrating/overview#more-than-a-reformat) ## Next Steps The method behind every migration Package and publish your migrated model