Modernizing Mainframe and IBM i Data for AI with Azure: A Strategic Guide

As organizations embark on their next wave of digital transformation, the strategic need to harmonize mainframe and IBM i legacy data with modern cloud platforms has shifted from a technical aspiration to an operational imperative. Most large enterprises, especially those in financial services, government, and manufacturing, are underpinned by mainframe and IBM i systems that contain vital operational, transactional, and customer data. However, the competitive landscape—and the potential unlocked by next-generation analytics, machine learning, and AI—demands that this data is made accessible, actionable, and secure within the most advanced cloud environments.

The Case for Mainframe and IBM i Modernization​

For decades, mainframes and IBM i environments have been the trusted backbone of banks, insurers, logistics companies, and retailers worldwide. Their legendary reliability, transaction throughput, and security are unmatched. Yet the strengths of these systems have become a double-edged sword: the rigidity and siloed architecture of legacy environments now threaten to limit organizational agility.

Why Modernize?​

• Real-time analytics: Business users seek up-to-the-minute visibility, not day-old batch reports.
• AI integration: Training, deploying, and governing machine learning models require massive, varied, and real-time data access.
• Cost efficiency: Mainframe compute and storage costs remain some of the highest in enterprise IT.
• Talent shortages: Fewer technologists are versed in COBOL or RPG, making maintenance and enhancement a perennial pain point.

The challenge is clear: how does an enterprise unlock the value of its legacy data—securely, efficiently, and without risking business continuity—to power next-generation analytics and AI on platforms like Microsoft Azure?

Microsoft Azure and the New Era of Legacy Data Integration​

Microsoft Azure has rapidly matured as a first-choice platform for enterprise cloud transformation, thanks in part to its aggressive focus on security, regulatory compliance, hybrid cloud operations, and deep integration with enterprise Windows environments. More recently, Azure has doubled down on data and AI, investing heavily in products like Azure Synapse Analytics, Azure Databricks, and a suite of AI and ML offerings that are reshaping how organizations extract insight from their most valuable data assets.
Yet, Azure’s greatest promise for many organizations hinges on its ability to bridge the old with the new—to seamlessly integrate mainframe and IBM i data into the cloud, without the cost and risk of rewriting “crown jewel” applications.

The Importance of Mainframe Data in Modern Analytics​

“Mainframe data is the source of truth for some of the world’s most demanding industries,” observes Ashish Kumar, a senior executive at Kyndryl. “But translating that data into actionable, AI-ready form has historically been a difficult, risky, and expensive proposition.” The transition to cloud-based analytics for such workloads must be engineered to preserve data integrity, ensure security, and deliver low-latency, high-throughput connectivity between systems.

Standing at the Forefront: Azure Databricks and Precisely Connect​

Two solutions leading the charge for seamless legacy-cloud integration are Azure Databricks and Precisely Connect.

Azure Databricks: The Epicenter for Unified Analytics​

Azure Databricks, a joint offering from Microsoft and Databricks, is built on Apache Spark and optimized for Azure, delivering a unified analytics platform—capable of handling massive, heterogenous datasets at scale. Its elastic, cloud-native architecture is designed to run petabyte-scale ETL, advanced analytics, and machine learning workloads—all while benefiting from the security, compliance, and integration hooks of Azure.
Key capabilities include:

• Elastic scalability: Dynamically scales compute to match workload, minimizing costs and maximizing performance.
• Integrated machine learning: Simplifies end-to-end ML workflows, from data prep and model training to deployment.
• Multi-language support: Enables teams using Python, SQL, R, and Scala to collaborate within the same environment.
• Native Azure ecosystem integration: Provides connectivity with Azure Data Lake Storage, Synapse, Azure Active Directory, and more.

Precisely Connect: The Bridge to Legacy Data​

Precisely Connect specializes in unlocking mainframe and IBM i data for advanced analytics—serving as both a data movement and transformation engine. Designed for high-performance, secure data integration, Connect lets organizations run petabyte-scale ETL pipelines on Azure Databricks, opening new paths to analytics and AI.
Core features:

• Agentless, non-intrusive integration: Reduces risk by avoiding direct changes to sensitive mainframe environments.
• Real-time and batch processing: Supports both regular interval and on-demand data synchronization models.
• Data lineage and transformation tracking: Ensures transparency and compliance throughout the data pipeline.
• Support for legacy formats: Handles COBOL copybooks, VSAM, IMS, DB2, and more.

The power of Precisely Connect lies in its ability to move raw legacy data directly into the Azure ecosystem—landing it in formats ready for Databricks, Synapse, or any other downstream analytics and AI workloads.

The Azure Modernization Journey: A Real-World Blueprint​

To understand the impact, consider a typical modernization path for a global financial institution:

• Discovery and assessment: Map mainframe and IBM i data assets, prioritize datasets for migration, assess security/compliance requirements.
• Data pipeline design: Use Precisely Connect to define scalable ETL jobs, mapping legacy formats to cloud-optimized schemas.
• Secure landing in Azure: Transfer and transform data directly into Azure Data Lake Storage Gen2 or Synapse Analytics, leveraging native security features such as managed identities and encryption at rest/in transit.
• Activation in Databricks: Run advanced analytics, predictive modeling, or business intelligence on unified datasets using Databricks, with full tracking and lineage.
• Operationalization and AI: Feed insights and trained models back into business workflows, optionally on-premises or in hybrid cloud deployments.

Lift-and-Shift Versus Full Transformation​

Solutions such as Skytap on Azure offer a “lift-and-shift” approach, enabling organizations to move entire IBM Power workloads—including AIX and IBM i—directly onto Azure infrastructure. This method preserves mission-critical business logic and operating environments, allowing modernization activities (e.g., adopting cloud-native services, AI augmentation) to occur incrementally and securely—a crucial concern for highly regulated sectors. Skytap’s model avoids costly code rewrites, expedites migration timelines, and empowers IT teams to transition at their own pace.

Comparative Analysis: Azure Versus Other Modernization Solutions​

Azure is hardly alone in the mainframe integration space—rivals include AWS Mainframe Modernization, IBM PowerVS, and Google Cloud’s assortment of migration tools. Each offers benefits, but Azure, with Skytap and Databricks, maintains several distinctive advantages:
Azure’s ability to offer non-disruptive, secure, and scalable migration, combined with advanced AI and analytics integration, gives it a substantial edge—particularly for hybrid cloud estates.

Critical Strengths of the Azure + Databricks + Connect Stack​

1. Rapid Time to Insight​

By landing mainframe and IBM i data directly into Azure, organizations slash the time between extraction and actionable analysis. Databricks streamlines the process of onboarding, prepping, and exploring legacy data, supporting rapid hypothesis testing for data science teams.

2. Elastic Economics and Performance​

Compared to legacy mainframe operations, Azure’s pay-as-you-go, elastic infrastructure radically reduces computing costs for analytics workloads. Databricks’ adaptive scaling means organizations don’t need to over-provision resources for peak loads—they pay for what they use.

3. Compliance, Security, and Data Sovereignty​

Microsoft’s security pedigree—complemented by tooling from Connect and governance layers like Azure Purview—ensures customer data is always encrypted, access is strictly controlled, and auditability is built in. For organizations in highly regulated sectors or geographies, Azure’s extensive global regions also support stringent data residency requirements.

4. Incremental, Low-Risk Modernization​

Thanks to tools like Skytap and Connect, companies can migrate specific workloads or datasets incrementally, avoiding risky big-bang conversions that historically plagued mainframe modernization. IT can focus first on analytics and AI, then gradually decommission legacy components as needed.

5. Unified Platform for Data and AI​

By converging legacy data with Azure’s native capabilities, companies can power everything from self-serve BI in Power BI to operational AI in Azure Machine Learning—all on a single, governed platform. This unified foundation unlocks enterprise agility and accelerates data-driven innovation.

Notable Use Cases and Industry Impacts​

• Financial Services: Real-time fraud detection and risk analytics running on mainframe-sourced data in Databricks, with outputs aiding AML and compliance.
• Manufacturing: Predictive maintenance models based on years of equipment log data extracted from IBM i, deployed in Azure via machine learning pipelines.
• Retail: Personalized marketing and inventory optimization powered by historic transaction data migrated from AS/400/iSeries, unified in Azure Synapse.
• Government: Citizen-facing digital services that combine legacy demographic records with cloud-native digital experience platforms.

Case studies repeatedly show that organizations adopting the Azure + Databricks + Connect approach realize faster reporting, innovative customer experiences, and more agile business operations—provided migrations are carefully planned and governed.

Practical Challenges and Considerations​

Despite its strengths, integrating mainframe and IBM i data with Azure is not without risks or complexities.

A. Operational Complexity​

Hybrid environments where Power and x86 coexist with cloud-native services can introduce configuration, networking, and monitoring challenges. Robust governance policies and skilled personnel are essential to tame sprawl and ensure operational integrity.

B. Vendor Lock-in​

The deep integration required for performance, security, and compliance can create switching costs. Organizations reliant on Azure-centric solutions should weigh long-term flexibility and maintain a clear exit strategy should business needs change.

C. Security and Compliance Burden​

While Azure leads on regulatory certifications, the movement of sensitive legacy data still raises data privacy and compliance challenges. Zero Trust security models, data masking, and continuous monitoring should be deployed from day one.

D. Talent and Change Management​

Legacy modernization is as much about people as technology. The ongoing shortage of experienced mainframe and cloud engineers, alongside the need to retrain business and technical teams, can hinder transformation if not actively managed.

Best Practice Recommendations​

• Conduct thorough assessment: Map dependencies, compliance constraints, and business priorities before commencing migrations.
• Engage trusted partners: Leverage Azure migration consultants and managed service providers experienced in both IBM and Microsoft ecosystems.
• Emphasize incrementalism: Start with analytics workloads or non-mission-critical datasets before tackling core transactional systems.
• Invest in security and governance: Leverage Azure’s Purview, Key Vault, and role-based access control; ensure Connect pipelines conform to best-practice data management.
• Plan for the long term: Develop clear archiving, decommissioning, and modernization roadmaps to avoid costly technical debt accumulation.

Looking Ahead: The Future of Mainframe-AI Integration​

As enterprise AI, real-time analytics, and compliance demands continue to rise, bridging mainframe and IBM i data with Azure will only gain urgency. Advances in no-code/low-code data prep, conversational AI interfaces, and unified governance platforms are working to make mainframe analytics accessible to ever broader user groups—from analysts to business leaders.
Recent developments in Databricks, such as natural language interfaces (Genie Conversational Assistant) and Databricks Apps, further lower the technical barrier—allowing users to “ask” questions of their legacy data conversationally, and operationalize insights directly into business workflows. These capabilities, however, will require ongoing investment in data governance and explainability to mitigate risks around bias, trust, and compliance.

Conclusion: Seizing the Opportunity​

The integration of mainframe and IBM i data with cloud-native analytics platforms like Azure Databricks, enabled by tools such as Precisely Connect, represents an inflection point for enterprise IT. Organizations that embrace this convergence can not only extract unprecedented value from decades-of-accumulated institutional knowledge but also future-proof themselves for the rapidly evolving demands of AI-driven business.
For Windows-centric enterprises, Azure’s blend of elasticity, security, and enterprise integration—tied with proven migration accelerators—delivers a verifiable and compelling path forward. Yet successful transformation will belong not to those who simply move data, but to those who govern, secure, and activate it for real business outcomes. As the journey unfolds, vigilance around operational complexity, vendor lock-in, and talent strategy will determine which organizations maximize return on their modernization investments—and which risk carrying the burden of an aging, isolated past into the digital future.

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Source: Precisely Microsoft Azure: Integrate Mainframe and IBM i Data