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LTM Data And AI SSIS Modernization to Databricks

LTIMindtree Limited

Prijzen Gratis

Outcreate data modernization with AI-driven SSIS to Azure Databricks migration that reduces effort, lowers risk, and speeds time to value.

Modernize SQL Server Integration Services (SSIS) packages into Azure Databricks workloads with AI-driven automation in a focused 6-week engagement.

Modernizing SSIS Workloads with Confidence

As organizations move toward Azure Databricks and Microsoft Fabric, many still rely on legacy SSIS workloads that are difficult to scale, maintain, and modernize. Moving those workloads to PySpark-based pipelines can unlock a more flexible and future-ready data foundation, but the shift often brings migration complexity, technical dependencies, and execution risk.

Our Solution: Accelerated SSIS-to-Databricks Migration

This 6-week engagement helps data leaders, extraction, transformation, and loading (ETL) architects, and modernization teams move from on-premises SSIS to Azure Databricks with more speed and control. LTM uses a generative AI-driven automation framework to reduce migration effort, simplify planning, and convert legacy ETL pipelines into scalable PySpark workflows aligned to the Microsoft data ecosystem.

With this approach, organizations can:

  • Reduce repetitive manual work and cut migration effort
  • Lower migration risk through guided assessment and automation
  • Move faster toward Azure Databricks adoption and broader Microsoft platform value

    • Why This Engagement Matters

  • Boost Azure Databricks adoption: Convert SSIS packages into cloud-ready pipelines that are easier to scale and maintain.
  • Improve Azure integration: Align modernized workloads with services such as Azure Data Factory and the wider Microsoft data stack.
  • Get more value from Microsoft investments: Modernize legacy workloads so existing platform capabilities, licensing, and data services deliver stronger returns.

    • Engagement Overview

    Week 1: Discovery and Assessment

  • Scan and inventory existing SSIS packages using proprietary tooling
  • Identify control flow, data flow, dependencies, and integration points
  • Classify workloads by complexity and modernization readiness

    • Week 2: Planning and Estimation

  • Generate effort estimates for migration
  • Identify blockers such as unsupported components or custom logic
  • Define a target-state architecture aligned to Azure-native services

    • Weeks 3 to 6: AI-Driven Modernization and Prototyping

  • Auto-generate PySpark-based pipelines compatible with Azure Databricks
  • Validate logic accuracy and performance alignment
  • Deliver a working prototype and a detailed migration roadmap

    • Deliverables and Outcomes

    By the end of the engagement, you will receive:

  • A modernization roadmap aligned to Microsoft best practices
  • A validated prototype that shows migration feasibility
  • A clearer path to scale SSIS modernization across your environment
  • Faster momentum toward a cloud-native data architecture built on Microsoft technologies

    • Een overzicht

    https://catalogartifact.azureedge.net/publicartifacts/ltim.ltimindtree_dataandai_ssis_modernization_databrics-46ed1cd0-4eda-4a2a-88ef-801880099e06/image0_SSIStoPySparkAzureDatabricks2.png