Synapse to Databricks Migration with Kanerika’s FLIP Accelerator
Move Azure Synapse SQL workloads to Databricks with Kanerika’s FLIP accelerator. Convert supported SQL artifacts into PySpark notebooks and deploy them to your selected workspace. Reduce manual rewriting so your engineering team can focus on validating results, testing performance, and preparing workloads for production.
The Cost of Legacy Data Infrastructure
How Legacy ETL Systems Increase Enterprise Costs
80%
IT budgets are spent on legacy system maintenance.
44%
CIOs see legacy systems as barriers to growth.
50%
Developer productivity is lost due to outdated tools.
70%
Digital transformation efforts fail due to legacy systems.
Migrate from Synapse to Databricks with Our Accelerator
Use Your Azure Committed Spend (MACC)
From Synapse SQL to a Databricks Notebook
See FLIP convert a Synapse SQL filter into a PySpark notebook and deploy it to the selected workspace.
Estimate the ROI of Synapse to Databricks Migration
Estimate how automated conversion could affect migration effort based on SQL artifact counts, query complexity, validation scope, and engineering capacity.
Business Outcomes from Synapse Modernization
Business Benefits of Moving Synapse SQL to Databricks
Control Data Platform Costs
- Consolidate migrated SQL workloads alongside existing Databricks data pipelines.
- Compare compute, storage, and support costs across both platforms.
- Retire duplicate workloads after confirming downstream teams can switch.
Prepare Data for AI
- Bring migrated datasets closer to analytics and AI workloads.
- Prepare governed datasets for reporting, experimentation, and model development.
- Reuse validated data across engineering, analytics, and AI teams.
Free Up Engineering Capacity
- Convert supported SQL logic into PySpark notebooks with FLIP.
- Give engineers a consistent workspace to review converted notebooks.
- Redirect engineering time toward testing, tuning, and production readiness.
Maintain Data Governance
- Map access permissions and data ownership before switching platforms.
- Review target workspace permissions before granting teams production access.
- Verify that migrated workloads meet existing data access requirements.
When Moving Off Synapse SQL Makes Sense
Four common situations lead teams to review a Synapse to Databricks migration.
Databricks Already in Use
Teams already run data engineering workloads in Databricks and want SQL logic built and reviewed in the same workspace.
Duplicate SQL Logic Across Platforms
Similar SQL logic is maintained in both environments. Convert supported queries once and retire the duplicate.
Cost of Supporting Two Environments
Running two platforms adds operating cost. Compare run costs before deciding which workloads should move.
Testable Workloads to Start With
Workloads with clear dependencies and testable outputs make the safest first candidates for conversion.
The Migration Process
Synapse to Databricks Migration Process
Assess Synapse SQL Workloads
Inventory Synapse SQL artifacts and dependencies. Identify supported SQL patterns, prioritize workloads, and define validation requirements upfront.
Convert Supported SQL Artifacts
Upload the selected SQL artifact to FLIP and run the migration pipeline to generate a corresponding PySpark notebook.
Deploy Notebooks to Databricks
FLIP deploys the generated notebook to the configured Databricks workspace. Engineers then review the converted logic and check dependencies.
Validate and Approve Workloads
Compare Synapse and Databricks outputs, test key business rules, and review performance. Resolve exceptions before approving workloads for production.
ROI YOU CAN EXPECT
Synapse to Databricks Migration ROI
Reduce manual SQL conversion and move workloads faster.
ESTIMATED MIGRATION SAVINGS
Average effort reduction
Basic - Single Query and Filters
Less Effort
Standard - Joins and Aggregations
Less Effort
Enterprise - Large Synapse SQL Estate
Less Effort
Illustrative estimates. Actual effort varies by SQL complexity, data volume, and validation scope. Bars are scaled within each tier.
Case studies
Synapse to Databricks Migration Case Studies
See how automated SQL conversion can reduce manual redevelopment and help data teams prepare Synapse workloads for Databricks.
Accelerating Reporting Modernization with Automated Crystal Reports to DevExpress Migration
Impact:
- 73 Reports, Successfully Migrated
- 70% Migration Automated
- 590 Hours, Total Migration Effort
60% Faster Migration: Crystal Reports to Power BI
Impact:
- 60% Faster Migration
- 10x Data Scale
- 99.5% Refresh Success Rate
60% Faster Analytics with Informatica to Alteryx
Impact:
- 45% Faster Reporting Cycles
- 38% Reduction in Maintenance Costs
- 60% Improved Analytics Turnaround
Frequently Asked Questions (FAQs)
How Do You Migrate Synapse SQL Workloads to Databricks?
Start with an inventory of SQL artifacts, data sources, schedules, and downstream dependencies. Select workloads for conversion and use FLIP to generate PySpark notebooks from supported SQL files. Deploy the notebooks to Databricks, compare source and target results, and plan cutover after checking permissions, dependencies, and performance in the target workspace.
Can FLIP Convert Synapse SQL to PySpark Automatically?
Yes. FLIP converts supported Synapse SQL artifacts into PySpark notebooks and deploys them to a configured Databricks workspace. The amount of automation depends on the SQL patterns in scope. Review each artifact for unsupported syntax or custom logic that may require manual engineering.
What Are the Main Challenges in Synapse to Databricks Migration?
The main challenges are translating SQL logic, preserving business rules, and adapting dependencies to the target environment. Teams also need to check data types, null handling, and performance. Start with representative workloads to identify conversion exceptions before setting the full migration scope.
How Long Does a Synapse to Databricks Migration Take?
Timing depends on the number of SQL artifacts, the complexity of queries, the data volumes involved, and the testing required. A small group of supported queries can move faster than an estate with shared dependencies. Kanerika can assess the inventory and define a rollout plan based on conversion and validation effort.
How Much Does Synapse to Databricks Migration Cost?
Cost depends on the number of SQL artifacts, manual remediation, data movement, and validation scope. Include engineering effort, deployment work, and any period when both platforms run in parallel. Compare these costs against the conversion effort FLIP can reduce for the SQL patterns in scope.
Can FLIP Preserve SQL Logic and Business Rules?
FLIP converts supported SQL logic into PySpark notebook code. Teams should verify that the generated notebooks return the expected results against the same input data. Compare row counts, key totals, filters, joins, null handling, and other outputs that matter to downstream reporting.
What Happens to Synapse Pipelines and Schedules?
Assess pipeline triggers, schedules, dependencies, and runtime parameters separately from SQL conversion. The FLIP walkthrough covers conversion and deployment of a SQL artifact. Before cutover, confirm how the converted notebooks will run, which upstream tasks they depend on, and how failures will be handled.
Can Databricks Replace Every Synapse Workload?
Assess each workload by how it runs and which teams depend on it. SQL conversion addresses one part of the move. Ingestion, orchestration, security, reporting connections, and performance may require separate design and testing. Group these dependencies by workload before deciding what to migrate.
When Does Moving Off Synapse SQL Make Sense?
Consider migration when teams already run data engineering workloads in Databricks, maintain duplicate SQL logic across platforms, or need to reduce the cost of supporting two environments. Start with workloads that have clear dependencies and testable outputs. Use the results to compare operating costs, assess how much work remains, and decide which workloads should move in later phases.
How Do You Validate a Synapse to Databricks Migration?
Run the original SQL and the converted notebook against the same test data. Compare counts, totals, filters, joins, and business rule outcomes. Check runtime, permissions, and downstream jobs. Resolve differences and record approval before switching production workloads to Databricks.
