Mainframe VSAM/ISAM to NoSQL/SQL Migration Automation

Manual analysis of VSAM/ISAM file layouts, copybooks, and access patterns consumes thousands of analyst hours and introduces mapping errors. A custom workflow uses agents to automatically parse physical file definitions, infer logical relationships from program calls, and generate normalized entity-relationship diagrams. This directly converts months of manual discovery into a validated, executable data model in days, slashing planning costs and project timelines.

Legacy data migrations fail due to misunderstood semantics, lost referential integrity, or performance cliffs in the new model. An automated workflow builds validation agents that generate and run equivalence tests—comparing record counts, key relationships, and business logic outputs between source and target—before cutover. This creates a safety net that catches semantic drift early, protecting revenue-critical operations and avoiding costly post-migration remediation.

VSAM/ISAM structures lock data into rigid, sequential access patterns, forcing expensive mainframe MIPS consumption for simple queries. The automated migration workflow analyzes historical access patterns (SMF data, program traces) to design optimal NoSQL (e.g., DynamoDB for transactions) or SQL (e.g., PostgreSQL for analytics) schemas. This shifts processing to scalable cloud infrastructure, reducing mainframe dependency costs by 30-50% and enabling sub-second analytics previously impossible.

Waiting for a clean, modern data layer is the single biggest blocker for refactoring mainframe applications. This workflow parallelizes data and application migration. While agents generate the new data layer and migration scripts, other agents use the inferred schema to stub out modern data access objects (DAOs) and REST APIs for development teams. This eliminates the traditional sequential waterfall, allowing application and data workstreams to proceed concurrently.

One-off migrations leave behind tribal knowledge and inconsistent patterns. A custom workflow codifies the entire process—from discovery and modeling to script generation and validation—into a reusable orchestration layer (e.g., using LangGraph). This turns migration from a project into a product, allowing enterprises to systematically modernize hundreds of files with consistent quality, audit trails, and predictable cost, building internal modernization IP.

Legacy data structures are incompatible with modern AI/ML pipelines and event-driven architectures. The automated workflow doesn't just lift-and-shift; it redesigns. Agents recommend and implement patterns like change data capture (CDC) streams from the new database and generate vector embedding pipelines for semantic search. This transforms a legacy data asset into a strategic platform supporting real-time analytics, AI agents, and composable business services.

This agent scans mainframe libraries (PDS/PDSE), JCL, and COBOL copybooks to inventory all VSAM KSDS/ESDS/RRDS and ISAM files. It parses FD entries, record descriptions, and access patterns (sequential, keyed, skip-sequential) to build a metadata catalog of source data assets, their physical characteristics, and embedded business keys—eliminating the manual, error-prone inventory phase.

Using the metadata catalog and analysis of application COBOL/CICS code, this component infers logical data relationships, transaction volumes, and read/write hotspots. It then recommends optimal target models—document (MongoDB) for hierarchical records, wide-column (Cassandra) for high-volume transactions, or relational (PostgreSQL) for complex joins—and generates the corresponding DDL or schema definitions.

This orchestration layer generates and executes optimized ETL scripts. It coordinates mainframe data extraction (via DFSORT, SyncSort, or direct VSAM reads), handles code page conversion (EBCDIC to UTF-8), transforms packed-decimal/zoned-decimal fields, and stages data for load. It manages batch windows, checkpoints, and restart logic to ensure complete, performant data movement without manual scripting.

A critical QA agent that runs parallel validation suites. It compares record counts, checksum values, and spot-checks business-critical fields between source and target after each load. It flags discrepancies, generates reconciliation reports, and can trigger re-extraction jobs. This automates the most risk-laden phase of migration, providing auditable proof of data integrity before cutover.

To prevent application rewrites, this component analyzes COBOL file I/O statements (READ, WRITE, REWRITE, DELETE) and generates equivalent, optimized data access modules in the target stack (e.g., Java Spring Data repositories, Python DAOs). It preserves the original logical operations while mapping them to modern database drivers and connection pools, decoupling business logic from the physical data migration.

This governance agent produces the executable migration playbook. It sequences the agents, defines approval gates (e.g., after schema generation, post-load validation), and integrates with ITSM tools like ServiceNow. Crucially, it pre-builds and can orchestrate rollback procedures—reverting data and schemas—if validation fails, providing the operational control needed for low-risk, phased production cutovers.