SQL Server to Snowflake Translation with SnowConvert

This document summarizes the key transformations performed by SnowConvert when migrating SQL Server SQL to Snowflake. It covers data type mapping, function translations, and other SQL construct adjustments, providing examples to illustrate the process. This summary is intended as a high-level overview; always refer to the official SnowConvert documentation for the most comprehensive and up-to-date information.

Data Type Mapping:

SnowConvert handles the mapping of SQL Server data types to their Snowflake equivalents. While many types have direct counterparts, some require conversion or special handling.

  • Numeric Types: SQL Server’s INT, BIGINT, SMALLINT, TINYINT generally map directly to Snowflake’s INTEGER, BIGINT, SMALLINT. DECIMAL and NUMERIC types map to Snowflake’s NUMBER, but precision and scale should be carefully reviewed. FLOAT and REAL map to Snowflake’s FLOAT, but potential differences in floating-point representation should be considered.

    • Example: DECIMAL(10,2) in SQL Server becomes NUMBER(10,2) in Snowflake.

  • String Types: VARCHAR, NVARCHAR, CHAR, and NCHAR map to Snowflake’s VARCHAR and CHAR. TEXT and NTEXT (deprecated in SQL Server) map to Snowflake’s VARCHAR (with size limitations) or TEXT. Large text values might require alternative handling due to size restrictions in Snowflake’s VARCHAR. VARCHAR(MAX) maps to Snowflake’s VARCHAR with its maximum size.

    • Example: VARCHAR(255) becomes VARCHAR(255). TEXT might become VARCHAR(16777216) or require a different large object strategy.

  • Date/Time Types: DATETIME, SMALLDATETIME, DATETIME2, and DATETIMEOFFSET map to corresponding Snowflake timestamp types. DATE and TIME map directly. Time zone handling is a key consideration, especially for DATETIMEOFFSET. Snowflake offers TIMESTAMP_NTZ (no time zone) and TIMESTAMP_TZ (with time zone).

    • Example: DATETIME2 in SQL Server might become TIMESTAMP_NTZ or TIMESTAMP_TZ in Snowflake, depending on the desired time zone behavior.

  • Binary Types: BINARY, VARBINARY, and IMAGE (deprecated) map to Snowflake’s VARBINARY. IMAGE data might require a different storage strategy in Snowflake.

    • Example: VARBINARY(MAX) becomes VARBINARY.

  • Other Types: Other data types like UNIQUEIDENTIFIER, SQL_VARIANT, XML, and user-defined types require specific mapping strategies. Refer to the SnowConvert documentation for details.

SQL Function and Construct Translation:

SnowConvert handles the translation of numerous SQL functions and constructs. Many have direct equivalents, while others require conversion or emulation.

  • String Functions: Functions like SUBSTRING, UPPER, LOWER, TRIM, LEN, REPLACE, CONCAT are generally translated directly or have close equivalents. However, some functions might have slightly different names or argument orders.

    • Example: LEN(string) in SQL Server becomes LENGTH(string) in Snowflake. CONCAT(string1, string2) in SQL Server can be translated to string1 || string2 in Snowflake.

  • Numeric Functions: Functions like ABS, ROUND, MOD, CEILING, FLOOR, POWER, SQRT are usually translated directly.

  • Date/Time Functions: Functions like GETDATE(), GETUTCDATE(), DATEADD, DATEDIFF, DATEPART, YEAR, MONTH, DAY have Snowflake equivalents. However, time zone handling might differ.

    • Example: GETDATE() in SQL Server becomes CURRENT_TIMESTAMP() in Snowflake. DATEADD(month, 1, date) is the same in both.

  • Aggregate Functions: SUM, AVG, COUNT, MIN, MAX are typically translated directly.

  • Analytic Functions (Window Functions): SQL Server’s window functions (e.g., ROW_NUMBER, RANK, LAG, LEAD, OVER, PARTITION BY) are generally supported in Snowflake, but syntax or behavior might have subtle differences.

    • Example: ROW_NUMBER() OVER (PARTITION BY column ORDER BY column) is similar in both, but always verify for edge cases.

  • Conditional Logic: CASE expressions are generally translated directly. ISNULL and COALESCE are handled appropriately.

    • Example: CASE WHEN condition THEN result ELSE result END is the same in both. ISNULL(expression, replacement) in SQL Server becomes COALESCE(expression, replacement) in Snowflake.

  • Joins: Inner, outer, and cross joins are usually translated without issues.

  • DDL Statements: CREATE TABLE, ALTER TABLE, DROP TABLE are generally translated. However, constraints, indexes, and other table properties require careful review and mapping. SQL Server-specific features like filegroups might need to be adapted.

    • Example: A SQL Server CREATE TABLE statement with specific filegroup or storage parameters might require adjustments in Snowflake.

  • DML Statements: SELECT, INSERT, UPDATE, and DELETE statements are generally translated.

  • Stored Procedures and Functions (T-SQL): SQL Server’s T-SQL code needs to be converted to Snowflake’s stored procedure language (Snowflake Scripting). This is a complex process, and SnowConvert can assist, but manual intervention is often required.

  • Triggers: SQL Server triggers require re-implementation in Snowflake using Snowflake’s task and stream features.

  • Views: SQL Server views are usually translated directly.

  • Common Table Expressions (CTEs): CTEs are generally translated directly.

  • Temporary Tables: SQL Server temporary tables (#temp_table, ##global_temp_table) require careful consideration. Snowflake offers temporary tables, but their scope and behavior might differ.

  • Identity Columns: Identity columns are handled, but the specific implementation might require review.

Example of a More Complex Conversion:

Let’s say you have a SQL Server query like this:

SELECT employee_id,
       ename,
       hire_date,
       salary,
       ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) as rn
FROM employees
WHERE hire_date > DATEADD(year, -1, GETDATE());
Copy

SnowConvert might translate this to something like:

SELECT employee_id,
       ename,
       hire_date,
       salary,
       ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) as rn
FROM employees
WHERE hire_date > DATEADD(year, -1, CURRENT_TIMESTAMP());
Copy

This example demonstrates the translation of GETDATE() to CURRENT_TIMESTAMP(). More complex queries involving T-SQL, triggers, or specific SQL Server features would require more extensive transformations.

Key Considerations:

  • Data Volume and Distribution: Snowflake’s architecture is different from SQL Server’s. Consider how data volume and distribution will impact performance after migration.

  • Performance Tuning: Performance characteristics may vary. Optimize queries after conversion.

  • Security: Review security settings and access controls in Snowflake.

  • Testing: Thoroughly test all converted code to ensure accuracy and functionality.

This summary provides a general overview. Always consult the official SnowConvert documentation for the most detailed and accurate information on SQL Server to Snowflake translation. The tool itself will handle many of these conversions automatically, but understanding the underlying transformations is crucial for a successful migration.

Language: English