home / skills / sfc-gh-dflippo / snowflake-dbt-demo / dbt-migration-ms-sql-server
This skill converts SQL Server and Azure Synapse DDL into Snowflake-ready dbt models, preserving logic while applying dbt best practices.
npx playbooks add skill sfc-gh-dflippo/snowflake-dbt-demo --skill dbt-migration-ms-sql-serverReview the files below or copy the command above to add this skill to your agents.
---
name: dbt-migration-ms-sql-server
description:
Convert Microsoft SQL Server/Azure Synapse T-SQL DDL to dbt models compatible with Snowflake. This
skill should be used when converting views, tables, or stored procedures from SQL Server to dbt
code, generating schema.yml files with tests and documentation, or migrating T-SQL to follow dbt
best practices.
---
# SQL Server / Azure Synapse to dbt Model Conversion
## Purpose
Transform SQL Server/Azure Synapse T-SQL DDL (views, tables, stored procedures) into
production-quality dbt models compatible with Snowflake, maintaining the same business logic and
data transformation steps while following dbt best practices.
## When to Use This Skill
Activate this skill when users ask about:
- Converting SQL Server views or tables to dbt models
- Migrating T-SQL stored procedures to dbt
- Translating T-SQL syntax to Snowflake
- Generating schema.yml files with tests and documentation
- Handling T-SQL-specific syntax (IDENTITY, TOP, #temp tables, TRY...CATCH)
---
## Task Description
You are a database engineer working for a hospital system. You need to convert SQL Server / Azure
Synapse DDL to equivalent dbt code compatible with Snowflake, maintaining the same business logic
and data transformation steps while following dbt best practices.
## Input Requirements
I will provide you the T-SQL DDL to convert.
## Audience
The code will be executed by data engineers who are learning Snowflake and dbt.
## Output Requirements
Generate the following:
1. One or more dbt models with complete SQL for every column
2. A corresponding schema.yml file with appropriate tests and documentation
3. A config block with materialization strategy
4. Explanation of key changes and architectural decisions
5. Inline comments highlighting any syntax that was converted
## Conversion Guidelines
### General Principles
- Replace procedural logic with declarative SQL where possible
- Break down complex procedures into multiple modular dbt models
- Implement appropriate incremental processing strategies
- Maintain data quality checks through dbt tests
- Use Snowflake SQL functions rather than macros whenever possible
### Sample Response Format
```sql
-- dbt model: models/[domain]/[target_schema_name]/model_name.sql
{{ config(materialized='view') }}
/* Original Object: [database].[schema].[object_name]
Source Platform: SQL Server / Azure Synapse
Purpose: [brief description]
Conversion Notes: [key changes]
Description: [SQL logic description] */
WITH source_data AS (
SELECT
customer_id::INTEGER AS customer_id,
-- NVARCHAR converted to VARCHAR (Unicode handled natively)
customer_name::VARCHAR(100) AS customer_name,
-- MONEY converted to NUMBER(18,2)
account_balance::NUMBER(18,2) AS account_balance,
-- DATETIME converted to TIMESTAMP_NTZ
created_date::TIMESTAMP_NTZ AS created_date
FROM {{ ref('upstream_model') }}
),
transformed_data AS (
SELECT
customer_id,
UPPER(customer_name)::VARCHAR(100) AS customer_name_upper,
account_balance,
created_date,
CURRENT_TIMESTAMP()::TIMESTAMP_NTZ AS loaded_at
FROM source_data
)
SELECT
customer_id,
customer_name_upper,
account_balance,
created_date,
loaded_at
FROM transformed_data
```
```yaml
## models/[domain]/[target_schema_name]/_models.yml
version: 2
models:
- name: model_name
description:
"Table description; converted from SQL Server / Azure Synapse [Original object name]"
columns:
- name: customer_id
description: "Primary key - unique customer identifier"
tests:
- unique
- not_null
- name: customer_name_upper
description: "Customer name in uppercase"
- name: account_balance
description: "Current account balance; Foreign key to OTHER_TABLE"
tests:
- relationships:
to: ref('OTHER_TABLE')
field: OTHER_TABLE_KEY
- name: created_date
description: "Date the customer record was created"
- name: loaded_at
description: "Timestamp when the record was loaded by dbt"
```
```yaml
## dbt_project.yml (excerpt)
models:
my_project:
+materialized: view
domain_name:
+schema: target_schema_name
```
### Specific Translation Rules
#### dbt Specific Requirements
- If the source is a view, use a view materialization in dbt
- Include appropriate dbt model configuration (materialization type)
- Add documentation blocks for a schema.yml
- Add descriptions for tables and columns
- Include relevant tests
- Define primary keys and relationships
- Assume that upstream objects are models
- Comprehensively provide all the columns in the output
- Break complex procedures into multiple models if needed
- Implement appropriate incremental strategies for large tables
- Use Snowflake SQL functions rather than macros whenever possible
- **Always cast columns with explicit precision/scale** using `::TYPE` syntax (e.g.,
`column_name::VARCHAR(100)`, `amount::NUMBER(18,2)`) to ensure output matches expected data types
- **Always provide explicit column aliases** for clarity and documentation
#### Performance Optimization
- Suggest clustering keys if needed
- Recommend materialization strategy (view vs table)
- Identify potential performance improvements
#### SQL Server/T-SQL to Snowflake Syntax Conversion
- Replace TOP n with LIMIT n
- Convert IDENTITY columns to Snowflake IDENTITY or sequences
- Replace #temp tables with session-scoped temporary tables
- Convert TRY...CATCH to Snowflake exception handling
- Handle ANSI_NULLS and QUOTED_IDENTIFIER settings
- Replace sys.\* system tables with Snowflake equivalents
- Convert MERGE syntax differences
- Replace @@ROWCOUNT with ROW_COUNT()
- Convert NOLOCK hints (remove them)
- Add inline SQL comments highlighting any syntax that was converted
#### Key Data Type Mappings
| T-SQL | Snowflake | Notes |
| --------------------------- | ---------------- | ------------------------- |
| INT/BIGINT/SMALLINT/TINYINT | Same | All alias to NUMBER(38,0) |
| BIT | BOOLEAN | |
| DECIMAL/NUMERIC | DECIMAL/NUMERIC | |
| FLOAT/REAL | FLOAT/REAL | |
| MONEY/SMALLMONEY | NUMBER(38,4) | |
| CHAR/VARCHAR/TEXT | Same | VARCHAR(MAX) → VARCHAR |
| NCHAR/NVARCHAR/NTEXT | VARCHAR | Unicode handled natively |
| DATE | DATE | |
| TIME | TIME | |
| DATETIME/DATETIME2 | TIMESTAMP_NTZ | |
| DATETIMEOFFSET | TIMESTAMP_TZ | |
| BINARY/VARBINARY/IMAGE | BINARY/VARBINARY | Max 8MB |
| UNIQUEIDENTIFIER | VARCHAR | |
| XML | VARIANT | |
| SQL_VARIANT | VARIANT | |
#### Key Syntax Conversions
```sql
-- TOP → LIMIT
SELECT TOP 10 * FROM table → SELECT * FROM table LIMIT 10
-- IDENTITY → IDENTITY or AUTOINCREMENT
id INT IDENTITY(1,1) → id INT AUTOINCREMENT START 1 INCREMENT 1
-- #temp tables → TEMPORARY tables
CREATE TABLE #temp → CREATE TEMPORARY TABLE temp
-- TRY...CATCH → Exception handling
BEGIN TRY ... END TRY BEGIN CATCH ... END CATCH → BEGIN ... EXCEPTION WHEN OTHER THEN ... END
-- ISNULL → COALESCE or IFNULL
ISNULL(col, 0) → COALESCE(col, 0)
-- GETDATE()/GETUTCDATE() → CURRENT_TIMESTAMP/SYSDATE
GETDATE() → CURRENT_TIMESTAMP()
-- DATEADD/DATEDIFF → Same (Snowflake supports)
DATEADD(day, 1, col) → DATEADD(day, 1, col)
-- @@ROWCOUNT → ROW_COUNT()
@@ROWCOUNT → ROW_COUNT()
-- NOLOCK hints → Remove
SELECT * FROM table WITH (NOLOCK) → SELECT * FROM table
```
#### Common Function Mappings
| T-SQL | Snowflake | Notes |
| ------------------------- | ---------------------------------------------- | ----- |
| `ISNULL(a, b)` | `COALESCE(a, b)` or `IFNULL(a, b)` | |
| `COALESCE(...)` | `COALESCE(...)` | Same |
| `NULLIF(a, b)` | `NULLIF(a, b)` | Same |
| `IIF(cond, a, b)` | `IFF(cond, a, b)` | |
| `GETDATE()` | `CURRENT_TIMESTAMP()` | |
| `GETUTCDATE()` | `CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())` | |
| `DATEADD(unit, n, d)` | `DATEADD(unit, n, d)` | Same |
| `DATEDIFF(unit, d1, d2)` | `DATEDIFF(unit, d1, d2)` | Same |
| `DATEPART(unit, d)` | `DATE_PART(unit, d)` | |
| `CONVERT(type, val)` | `val::type` or `TRY_CAST(val AS type)` | |
| `CAST(val AS type)` | `val::type` | |
| `CHARINDEX(s, str)` | `POSITION(s IN str)` | |
| `SUBSTRING(s, pos, len)` | `SUBSTR(s, pos, len)` | |
| `LEN(str)` | `LENGTH(str)` | |
| `REPLICATE(str, n)` | `REPEAT(str, n)` | |
| `STUFF(s, pos, len, new)` | `INSERT(s, pos, len, new)` | |
| `STRING_AGG(col, delim)` | `LISTAGG(col, delim)` | |
| `@@ROWCOUNT` | `ROW_COUNT()` | |
| `@@IDENTITY` | Use sequences or AUTOINCREMENT | |
#### Dependencies
- List any upstream dependencies
- Suggest model organization in dbt project
---
## Validation Checklist
- [] Every DDL statement has been accounted for in the dbt models
- [] SQL in models is compatible with Snowflake
- [] T-SQL-specific syntax converted (IDENTITY, TOP, #temp tables, TRY...CATCH)
- [] All business logic preserved
- [] All columns included in output
- [] Data types correctly mapped
- [] Functions translated to Snowflake equivalents
- [] Materialization strategy selected
- [] Tests added
- [] SQL logic description complete
- [] Table descriptions added
- [] Column descriptions added
- [] Dependencies correctly mapped
- [] Incremental logic (if applicable) verified
- [] Inline comments added for converted syntax
---
## Related Skills
- $dbt-migration - For the complete migration workflow (discovery, planning, placeholder models,
testing, deployment)
- $dbt-modeling - For CTE patterns and SQL structure guidance
- $dbt-testing - For implementing comprehensive dbt tests
- $dbt-architecture - For project organization and folder structure
- $dbt-materializations - For choosing materialization strategies (view, table, incremental,
snapshots)
- $dbt-performance - For clustering keys, warehouse sizing, and query optimization
- $dbt-commands - For running dbt commands and model selection syntax
- $dbt-core - For dbt installation, configuration, and package management
- $snowflake-cli - For executing SQL and managing Snowflake objects
---
## Supported Source Database
| Database | Key Considerations |
| ------------------------------ | ------------------------------------------------------------------------------------------------------- |
| **SQL Server / Azure Synapse** | T-SQL procedures, IDENTITY, TOP, #temp tables, TRY...CATCH, sys.\* tables, ANSI_NULLS/QUOTED_IDENTIFIER |
## Translation References
Detailed syntax translation guides are available in the `translation-references/` folder.
> **Copyright Notice:** The translation reference documentation in this repository is derived from
> [Snowflake SnowConvert Documentation](https://docs.snowflake.com/en/migrations/snowconvert-docs)
> and is © Copyright Snowflake Inc. All rights reserved. Used for reference purposes only.
### Reference Index
- [ETL BI Repointing Power BI Transact Repointing](translation-references/ms-sql-server-etl-bi-repointing-power-bi-transact-repointing.md)
- [Overview (README)](translation-references/ms-sql-server-readme.md)
- [SSIS Readme](translation-references/ms-sql-server-ssis-readme.md)
- [Subqueries](translation-references/ms-sql-server-subqueries.md)
- [Transact Continue Handler](translation-references/ms-sql-server-transact-continue-handler.md)
- [Transact Create Function](translation-references/ms-sql-server-transact-create-function.md)
- [Transact Create Procedure Snow Script](translation-references/ms-sql-server-transact-create-procedure-snow-script.md)
- [Transact Create Procedure](translation-references/ms-sql-server-transact-create-procedure.md)
- [Transact Exit Handler](translation-references/ms-sql-server-transact-exit-handler.md)
- [Alter Statement](translation-references/transact-alter-statement.md)
- [Ansi Nulls](translation-references/transact-ansi-nulls.md)
- [Built In Functions](translation-references/transact-built-in-functions.md)
- [Built In Procedures](translation-references/transact-built-in-procedures.md)
- [Create Function](translation-references/transact-create-function.md)
- [Create Index](translation-references/transact-create-index.md)
- [Create Materialized View](translation-references/transact-create-materialized-view.md)
- [Create Procedure Snow Script](translation-references/transact-create-procedure-snow-script.md)
- [Create Table](translation-references/transact-create-table.md)
- [Create View](translation-references/transact-create-view.md)
- [Data Types](translation-references/transact-data-types.md)
- [Dmls](translation-references/transact-dmls.md)
- [General Statements](translation-references/transact-general-statements.md)
- [Quoted Identifier](translation-references/transact-quoted-identifier.md)
- [Select](translation-references/transact-select.md)
- [System Tables](translation-references/transact-system-tables.md)
This skill converts Microsoft SQL Server and Azure Synapse T-SQL DDL (views, tables, stored procedures) into production-quality dbt models that run on Snowflake. It preserves business logic while applying dbt best practices, generating model SQL, schema.yml documentation and tests, and a recommended materialization strategy. The outputs are ready for data engineers to include in a dbt project and to validate with dbt test and run.
Provide the T-SQL DDL or stored-procedure SQL and the skill parses objects, maps T-SQL types/functions to Snowflake equivalents, and emits one or more dbt model files with explicit casts and aliases. It also generates a schema.yml with column descriptions, tests (unique, not_null, relationships), and a config block specifying materialization and incremental logic when appropriate. Inline comments identify converted T-SQL idioms (IDENTITY, TOP, #temp tables, TRY...CATCH) and explain architectural choices.
Will the conversion preserve business logic exactly?
Yes — the skill aims to preserve logic by translating functions and control flow into equivalent declarative SQL and splitting procedures into modular models; you should validate results with dbt test and sample record checks.
How are T-SQL types mapped to Snowflake types?
Types are mapped according to standard rules (e.g., DATETIME→TIMESTAMP_NTZ, MONEY→NUMBER(38,4), NVARCHAR→VARCHAR) and every column is cast explicitly using ::TYPE with precision/scale where applicable.