SSIS to Databricks Migration, Automated by FLIP
Move legacy SQL Server Integration Services (SSIS) packages to Databricks without rebuilding every transformation by hand. FLIP accelerator converts supported .dtsx package logic into PySpark notebooks, validates outputs, and publishes them directly to your Databricks workspace.
The Tech Debt
The Cost of Running Legacy SSIS Infrastructure
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Developer productivity is lost due to outdated tools.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 SSIS to Databricks with Our Accelerator
Use Your Azure Committed Spend (MACC)
See FLIP Execute the SSIS to Databricks Migration
Watch FLIP migrate an SSIS multi-lookup .dtsx package. The demo creates an SSIS-to-Databricks pipeline, configures a Databricks workspace connection, processes the file, and shows the generated PySpark notebook inside the target workspace.
Migration ROI Calculator
Kanerika’s IP-led accelerator, FLIP, automates your migration with major resource savings, ensuring a faster, lower-risk move to your target platform.
The SSIS to Databricks Advantage
How Enterprises Benefit with SSIS to Databricks Migration
Scalable ETL Processing
- Run large ETL workloads on Spark
- Scale compute as data volumes grow
- Run batch transformations on Databricks clusters
Unified Data Engineering
- Manage pipelines within one shared workspace
- Connect engineering with analytics and AI
- Reduce reliance on separate ETL runtimes
PySpark Code Development
- Replace supported SSIS logic with PySpark
- Review and maintain transformations in code
- Use common tools for pipeline changes
Stronger Data Governance
- Control user access through workspace permissions
- Apply Unity Catalog to governed assets
- Track operational checks across migrated workloads
When Moving Off SSIS Makes Sense
Four common triggers bring SSIS modernization onto the roadmap. FLIP helps teams assess supported conversion paths before rebuilding packages manually.
SQL Server End-of-Support
Older SQL Server versions face support deadlines. Review SSIS package dependencies before extending infrastructure.
Complex Package Maintenance
Nested lookups and hidden transformations add maintenance work. Convert supported steps into reviewable notebooks.
Rising Processing Demand
Nightly batch jobs strain fixed SSIS infrastructure. Move suitable transformations to distributed Spark processing.
Databricks Adoption Underway
When Databricks is already your target platform, bring SSIS ETL into one engineering environment.
The Migration Process
How FLIP Migrates SSIS to Databricks
SSIS Package Discovery
Review .dtsx files, package logic, connections, transformations, and dependencies to define the full conversion scope.
Automated PySpark Conversion
Upload SSIS packages into FLIP and translate supported integration logic into Databricks-compatible PySpark notebooks automatically.
Validation and Review
Validate generated code against expected outputs. Flag unsupported components, connection changes, and exceptions for review.
Databricks Workspace Deployment
Configure the target workspace, run the migration pipeline, and deliver notebooks to the selected folder.
ROI YOU CAN EXPECT
Real Impact from Automated SSIS to Databricks Migration
Our accelerator cuts migration effort across every package complexity level. The numbers below show estimated time and effort savings based on your SSIS estate, so you know what to expect before the project starts.
ESTIMATED TIME SAVINGS
Average effort reduction
Basic - Single Package Migration
Less Effort
Standard - Multi-Package Control Flow
Less Effort
Enterprise - Large-Scale SSIS Migration
Less Effort
Illustrative estimates. Actual effort varies by package complexity, data volumes, and transformation logic.
Case studies
What Enterprises Get with Automated Migrations
Case studies showing measurable outcomes from Kanerika’s migration work.
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
74% Faster Retail Reporting with Microsoft Fabric
Impact:
- 74% Faster Reporting Cycles
- 65% Increase in Reporting Stability
- 72% Faster Access to Current Metrics
Frequently Asked Questions (FAQs)
What is SSIS to Databricks migration?
It is the process of moving SQL Server Integration Services package logic and dependent workflows to Databricks. Typical work includes reviewing .dtsx files, converting supported transformations into PySpark, configuring connections, validating outputs, and preparing production jobs.
Why move SSIS packages to Databricks?
Teams often move when SQL Server infrastructure needs replacement, ETL workloads are growing, or Databricks has become their main data engineering platform. The move can bring suitable batch workloads onto Spark while reducing dependence on legacy package execution.
How does FLIP migrate SSIS packages?
FLIP accepts an SSIS .dtsx file, creates a migration record, processes supported package logic, and generates PySpark notebooks. The demo shows the notebook being delivered to a configured Databricks workspace folder after processing completes.
Which SSIS files are needed?
The .dtsx package is the primary input shown in the demo. A full project may also require connection details, parameters, configuration values, referenced scripts, data source information, and package dependencies for accurate conversion.
Can SSIS lookup transformations be converted?
The demo uses a multi-lookup .dtsx package and generates a PySpark notebook in Databricks. Other lookup patterns and custom logic need compatibility review and output testing before teams rely on the converted notebooks.
Can every SSIS component be converted automatically?
No. Support depends on the specific SSIS task, transformation, script, driver, and external dependency. Unsupported or environment-specific components need engineering review or manual rebuilding, and FLIP flags them for that review.
How is data accuracy checked after conversion?
Compare migrated results with SSIS baselines using row counts, keys, null handling, totals, and business rules. Run representative test cases before production cutover, then investigate any differences between the two environments.
Can FLIP publish notebooks to Databricks?
Yes. The FLIP demo shows a Databricks workspace root configured as the target and a generated PySpark notebook appearing in a workspace folder after migration completes, ready for review and testing.
What happens to scheduling and orchestration?
An SSIS package schedule does not become a production Databricks job automatically just because a notebook is generated. Teams must map triggers, dependencies, retries, credentials, and monitoring into the target job design.
Can SSIS and Databricks run side by side?
Yes. Parallel execution can be used to compare results and processing times before switching downstream consumers to Databricks. Plan for duplicate writes and additional system load during the overlap period.
How long does SSIS to Databricks migration take?
It depends on package count, transformation complexity, custom scripts, connectivity, and test coverage. The short demo shows one sample conversion flow and is not a timeline for an enterprise-scale migration.
What determines migration cost?
Cost depends on the package inventory, unsupported components, source and target access, test scope, workload scheduling, and post-cutover support. An assessment can turn these details into a phased estimate.
