Modernizing ETL for the Cloud Era
Legacy ETL tools like SSIS are becoming a strategic barrier as enterprises move to cloud-native, AI-driven data platforms. Built for on-premise workloads, SSIS cannot deliver the scalability, automation, or speed that businesses require in a fast-paced digital world.
However, SSIS migration to cloud is not straightforward. Critical logic is buried deep within XML-based packages, dependencies are spread across interconnected workflows, and manual rewrites lead to cost, risk, and delays. As a result, many transformation programs stall before they scale.
Our SSIS to PySpark Migration Solution addresses these challenges by automating logic extraction, converting workflows into PySpark pipelines on Databricks, and embedding governance. This helps enterprises modernize ETL for speed, accuracy, and future-ready analytics.
Key Benefits
By automating large parts of the SSIS to cloud conversion journey, organizations reduce their modernization effort, accelerate cloud value realization, and improve engineering productivity. Clients typically achieve:
60% Faster Migration:
Automated SSIS logic extraction and PySpark conversion reduce manual effort significantly.
30% Lower Token Cost:
Pre-processing and automation minimize engineering hours and token usage.
Business Logic Preservation:
Human-in-the-loop validation ensures workflows retain original intent.
Our Framework
Analyze
Ensure automated analysis of your existing SSIS landscape during the initial assessment phase.
- Smart Analyzer: Performs bulk SSIS package analysis, identifies complexity and technical debt, and generates intuitive reports.
- Dependency Mapper: Detects inter-package dependencies, external scripts, and database linkages for accurate migration planning.
- Risk Assessment: Highlights potential migration risks and provides mitigation strategies before conversion begins.
Convert
Automated code conversion and orchestration logic transformation reduce manual effort and improve efficiency.
- Object Converter: Translates SSIS Data Flow Tasks (DFTs) and Control Flow Tasks (CFTs) into equivalent PySpark transformations.
- SQL Logic Translator: Converts embedded SQL queries, stored procedures, and views into PySpark-native functions.
- Code Segmentation: Breaks SSIS logic into modular units for parallel processing and easier validation.
- Lineage Summary: Captures input/output sources and transformation lineage for governance and auditability.
Business Benefits
- Scintilla brings downs the total execution cycle from 60 hours to ~15 hours every month, which gives enough time for businesses to validate and proceed with the next steps on time.
- Reduction in TCO – around USD 1.5 million annually
