Microsoft SSIS to Databricks
Conversion Information
- Transpiler: BladeBridge
- Available target: Databricks notebooks (experimental)
Supported SSIS Versions
- SQL Server 2012, 2014, 2016, 2017, 2019, 2022
- Azure Data Factory SSIS Integration Runtime
Input Requirements
Export your SSIS packages as DTSX files:
- Solution Export: Export from Visual Studio / SQL Server Data Tools (SSDT)
- File System Packages: Direct DTSX file access
- SSISDB Export: Extract from SSIS catalog
-- Extract package from SSISDB catalog
DECLARE @packageData VARBINARY(MAX)
SELECT @packageData = [packagedata]
FROM [SSISDB].[catalog].[packages]
WHERE [name] = 'YourPackageName'
-- Save to file system for conversion
Running the Conversion
databricks labs lakebridge transpile \
--source-dialect ssis \
--input-source /path/to/ssis/packages \
--output-folder /output/sparksql \
--target-technology sparksql
The transpiler recursively scans the input directory for .dtsx files and generates Databricks notebook equivalents in the output folder.
Script Component Limitations
SSIS Script Task and Script Component contain C# or VB.NET code bodies that cannot be automatically converted. The converter preserves the logic structure, but the actual implementation must be rewritten in Python. See Supported Components for details.
What Gets Converted
| SSIS Concept | Databricks Equivalent |
|---|---|
| Control Flow Tasks | Notebook cells / dbutils.notebook.run() |
| Data Flow Task | Spark SQL temp views |
| Variables | Python variables |
| SSIS Expressions | Python f-strings / spark.sql() calls |
| Connection Managers | JDBC spark.read.format("jdbc") |
| ForEach Loop | Wildcard file reads with input_file_name() |
| Script Task | Python with dbutils |
For the full list of supported and unsupported components, see SSIS Supported Components.
For conversion examples with before/after code, see SSIS Conversion Examples.
Next Steps
- Export SSIS packages to DTSX files
- Run conversion (command above)
- Review generated notebooks for conversion warnings
- Configure Databricks secrets for connection strings
- Test with sample data in Databricks
- Deploy workflows to production
For more information, see: