60% Faster Migration:
Automated SSIS logic extraction and PySpark conversion reduce manual effort significantly.
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.
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:
Automated SSIS logic extraction and PySpark conversion reduce manual effort significantly.
Pre-processing and automation minimize engineering hours and token usage.
Human-in-the-loop validation ensures workflows retain original intent.
Ensure automated analysis of your existing SSIS landscape during the initial assessment phase.
Automated code conversion and orchestration logic transformation reduce manual effort and improve efficiency.