Hi @Akshay_Petkar
Migrating Amazon Redshift SQL to Databricks (especially Delta Lake or Unity Catalog-backed systems) using BladeBridge is a practical yet less-documented use case.
Since BladeBridge is a commercial tool with limited public documentation, here's a consolidated response based on real-world usage patterns, typical migration steps,
and best practices gathered from enterprise implementations.
BladeBridge for Redshift to Databricks Migration
BladeBridge operates as a code translation framework, helping automate SQL/ETL conversions with configurable rule engines.
For Redshift to Databricks (SQL/Delta/Unity Catalog), this often means:
- Parsing Redshift SQL (DDL, DML, Views, Functions)
- Translating syntax, types, and warehouse-specific constructs to Spark SQL / Databricks SQL
- Packaging output as Databricks notebooks, dbt models, or SQL scripts.
Step-by-Step Migration Flow:
Step 1: Set Up BladeBridge
- Get access to the BladeBridge environment (either via your enterprise license or BladeBridge-managed services).
- Work with BladeBridge support to enable Redshift as source and Databricks (Delta Lake/Spark SQL) as the target.
Step 2: Extract Redshift Code
- Use BladeBridgeโs metadata extractor or CLI to scan your Redshift warehous.
- This typically includes:
1. Stored procedures
2. UDFs
3. Views
4. Complex SQL queries
5. ETL control logic (if embedded)
Step 3: Define Mapping Rules
BladeBridge uses a rule-based translation engine. You or the BladeBridge team will:
- Map Redshift-specific functions (e.g., DISTINCT ON, ENCODE, STL_* system tables) to Databricks-compatible alternatives.
- Handle data type conversions (SUPER, GEOMETRY, etc. โ struct/JSON or compatible formats)
- Replace Redshift-specific syntax with Spark/Databricks equivalents.
Step 4: Generate Target Code
- BladeBridge will generate:
1 Spark SQL / Databricks SQL scripts
2 Optional: PySpark or Scala code if procedural logic needs translation
3 Notebooks (.dbc or .ipynb)
4 dbt-compatible models (if configured)
Step 5: Validation & QA
- BladeBridge offers data diffing / validation capabilities to compare Redshift and Databricks output.
- Integrate with Great Expectations or Delta Live Tables expectations if needed.
- Unit tests and volume-based data checks are essential post-conversion.
Step 6: Deployment
- Load converted code into Databricks (via Workspace API, Git sync, or notebooks).
- Use Databricks Jobs or Workflows to orchestrate converted SQL pipelines.
- Set up access permissions if you're using Unity Catalog.
Ask BladeBridge for:
- Redshift โ Databricks Conversion Mapping Guide
- Rule Engine Customization Manual
- CLI/SDK Usage Docs
- Check with your Databricks TAM (if enterprise) โ they often co-pilot BladeBridge-based migrations.
My Suggestion:
If this is your first time, request BladeBridge to:
- Do a pilot migration of 10โ20 complex querie
- Provide documentation for custom rules
- Clarify the translation logic visibility so you can tune it in-house later
LR
