DB Snowflake SQL

Snowflake SQL

Generate Snowflake-compatible SQL from metadata-provided object and column names. Preserve Snowflake object resolution, type, and semi-structured data semantics.

Namespaces and identifiers

• Qualify schema objects as database.schema.object when the current database and schema are not guaranteed; otherwise use the shortest unambiguous name.
• Treat warehouses, roles, and databases as account-level objects rather than schema objects.
• Remember that unquoted identifiers are stored and resolved in uppercase, while double-quoted identifiers preserve exact case and characters.
• Reuse metadata names exactly. Use double quotes for case-sensitive, reserved, spaced, or special-character identifiers; do not use backticks or square brackets.

Queries and expressions

• Use LIMIT or ANSI FETCH for row limiting and add a deterministic ORDER BY for pagination.
• Use QUALIFY to filter window-function results without an extra subquery when it makes the query clearer.
• Use Snowflake functions such as DATEADD, DATEDIFF, DATE_TRUNC, IFF, and TRY_TO_* with their Snowflake argument order and return semantics.
• Use ILIKE for case-insensitive pattern matching when intended. Do not import another dialect's date formatting or interval syntax without conversion.
• Treat session settings such as timezone, week policy, and timestamp mapping as part of date/time semantics when interpreting results.

Types and semi-structured data

• Use Snowflake types such as NUMBER(p,s), FLOAT, VARCHAR, BINARY, BOOLEAN, DATE, TIME, TIMESTAMP_NTZ, TIMESTAMP_LTZ, and TIMESTAMP_TZ.
• Distinguish TIMESTAMP_NTZ from session-timezone-aware TIMESTAMP_LTZ and offset-preserving TIMESTAMP_TZ; do not use generic TIMESTAMP when the distinction matters.
• Use VARIANT, OBJECT, and ARRAY for semi-structured data. Parse text with functions such as PARSE_JSON before storing it in VARIANT.
• Access semi-structured paths with Snowflake path syntax and cast scalar results to the required SQL type.
• Use LATERAL FLATTEN(INPUT => expression) to expand VARIANT, OBJECT, or ARRAY values into rows.

DDL and writes

• Use CREATE OR REPLACE only when replacing the object and its dependent behavior is acceptable; otherwise use IF NOT EXISTS or inspect metadata.
• Use CLUSTER BY for clustering where justified. Do not generate traditional indexes for ordinary Snowflake tables.
• Treat primary-key, unique, and foreign-key constraints on standard tables primarily as metadata; do not rely on them to enforce integrity. Account for enforced NOT NULL and supported CHECK constraints, and distinguish hybrid-table enforcement.
• Account for DDL transaction boundaries; do not assume DDL participates in a surrounding DML transaction.
• Use MERGE for matched update/delete and unmatched insert logic. Preserve the possibility of multiple source rows matching one target row when reasoning about correctness.

Data loading and unloading

• Use COPY INTO table to load from named, user, or table stages and COPY INTO location to unload query or table data.
• Use existing stages and file formats from metadata when available. Otherwise make the required stage, path, file format, pattern, column mapping, and copy options explicit.
• Use a SELECT transform inside COPY INTO table when files require column reordering, casting, or semi-structured extraction.
• Treat VALIDATION_MODE, ON_ERROR, MATCH_BY_COLUMN_NAME, PURGE, and FORCE as explicit capabilities with data-quality and idempotency consequences; do not enable them implicitly.
• Never place secrets directly in generated SQL when a storage integration, external stage, or configured credential is available.

Avoid common dialect leaks

Before returning SQL, reject backticks, PostgreSQL/MySQL upsert syntax, Oracle FROM DUAL, SQL Server TOP, traditional index DDL, and unqualified objects whose database and schema cannot be inferred.