Fujitsu Mainframe

Fujitsu Mainframe Analyzer

Analyze and migrate Fujitsu mainframe systems (FACOM, BS2000/OSD, OSIV, NetCOBOL, PowerCOBOL, Fujitsu JCL, SYMFOWARE) to modern Java/cloud platforms.

Core Capabilities

1. Fujitsu COBOL Analysis

Extract NetCOBOL/PowerCOBOL programs, Fujitsu-specific verbs, proprietary file organizations (SAM/PAM/ISAM), SYMFOWARE embedded SQL, screen handling (ACCEPT/DISPLAY with CRT STATUS).

2. Fujitsu JCL Analysis

Parse JOB statements, STEP definitions, ASSIGN/FILEDEF statements, conditional execution, cataloged procedures, resource allocation.

3. BS2000/OSD System Analysis

Analyze ENTER statements, system commands, file handling (PAM, SAM, ISAM), job variables, SDF processing.

4. SYMFOWARE Database Migration

Extract embedded SQL, schemas, stored procedures, transactions. Migrate to PostgreSQL, Oracle, or SQL Server.

5. Migration to Modern Platforms

Generate Spring Boot microservices, REST APIs, cloud-native apps (AWS, Azure, GCP), containerized deployments (Docker, Kubernetes), CI/CD pipelines.

Workflow

Step 1: Discover Assets

find . -name "*.cbl" -o -name "*.CBL" -o -name "*.cob"  # COBOL
find . -name "*.ncb" -o -name "*.NCB"  # NetCOBOL
find . -name "*.fjcl" -o -name "*.jcl"  # JCL
find . -name "*.cpy" -o -name "*.CPY"  # Copybooks
find . -name "*.sdf" -o -name "*.SDF"  # SDF files

Step 2: Analyze Structure

Extract divisions, data structures, file definitions, screen definitions, embedded SQL, Fujitsu-specific extensions. Key features:

• File organization: SEQUENTIAL, RELATIVE, INDEXED
• Screen handling: CRT STATUS, screen control
• Database: SYMFOWARE SQL
• Fujitsu verbs: ACCEPT OMITTED, INSPECT extensions
• Error handling: FILE STATUS, DECLARATIVES

Step 3: Map Dependencies

Build graphs: CALL hierarchies, copybook usage, file dependencies (FACOM), database access (SYMFOWARE), JCL sequences, screen definitions.

Step 4: Create Migration Strategy

Document architecture, Fujitsu-specific features, Java/cloud design, data migration, roadmap. Load `references/migration-strategy.md` for detailed framework.

Fujitsu-Specific Features

NetCOBOL Extensions

• Windowing, GUI support (PowerCOBOL)
• Enhanced ACCEPT/DISPLAY with positioning
• Object-oriented: CLASS definitions
• Extended exception handling

SYMFOWARE Database

• Embedded SQL: SELECT, INSERT, UPDATE, DELETE
• Cursors: DECLARE, OPEN, FETCH, CLOSE
• Transactions: COMMIT, ROLLBACK
• Migration: SYMFOWARE → PostgreSQL (cost), Oracle (enterprise), SQL Server

File Systems

Quick Patterns

COBOL → Java Spring Boot

Load `references/migration-patterns.md` for detailed examples. Quick overview:

Fujitsu COBOL:

SELECT EMPFILE ASSIGN TO "EMPDATA"
    ORGANIZATION IS INDEXED
    ACCESS MODE IS RANDOM
    RECORD KEY IS EMP-ID

Java JPA:

@Entity
@Table(name = "employees")
public class Employee {
    @Id
    private Integer empId;
    private String empName;
    private BigDecimal empSalary;
}

JCL → Shell + Kubernetes

Fujitsu JCL:

//STEP010  EXEC PGM=VALIDATE
//STEP020  EXEC PGM=PROCESS,COND=(0,EQ,STEP010)

Shell:

./validate && ./process

Load `references/migration-patterns.md` for Kubernetes CronJob examples.

Data Type Mappings

Load `references/data-mappings.md` for comprehensive tables. Critical mappings:

Migration Strategies

Strangler Fig Pattern (Recommended)

Gradually replace functionality. Lower risk, learn and adjust. Load `references/migration-strategy.md` for detailed steps.

Big Bang

Complete rewrite, single cutover. Higher risk, clean architecture. For smaller systems.

Hybrid

Core services modernized first, periphery later. Balanced risk/reward.

Output Requirements

Analysis Report Structure

1. Executive Summary - Overview, business impact, recommendation 2. Current State - Inventory, architecture, technology, dependencies 3. Fujitsu Features - NetCOBOL/PowerCOBOL, SYMFOWARE, file systems, screens, JCL 4. Target Design - Architecture, tech stack, microservices, data model, APIs 5. Migration Plan - Approach, timeline, resources, risks, costs 6. Technical Appendix - Code samples, mappings, utilities, testing

Load `references/migration-strategy.md` for complete frameworks and templates.

Critical Best Practices

1. ALWAYS use BigDecimal for COMP-3 and decimals (NEVER float/double) 2. Preserve business logic - understand before changing 3. Test with production data - validate conversions 4. Document Fujitsu extensions - proprietary features need special handling 5. Plan parallel run - compare outputs before cutover 6. Automate testing - regression suite for validation 7. Monitor everything - logging, metrics, alerts 8. Security first - authentication, authorization, encryption

Common Challenges & Solutions

Fujitsu-Specific Features → Custom adapters, equivalent libraries, re-implementation Screen Handling → User requirements gathering, modern UX design File Processing → ETL tools, Spring Batch Performance → Caching (Redis), async processing, DB optimization Transactions → Spring @Transactional, Saga pattern

Reference Files

When detailed information needed:

• `references/migration-patterns.md` - Complete code examples for all migration patterns
• `references/data-mappings.md` - Comprehensive type mappings, REDEFINES, dates, best practices
• `references/migration-strategy.md` - Full framework: assessment, design, testing, cutover, costs

Load these files for in-depth guidance on specific topics.

Fujitsu Mainframe Data Type Mappings

Comprehensive mapping guide for converting Fujitsu mainframe data types to modern platforms.

Fujitsu COBOL to Java Type Mapping

Critical Notes

1. Never use `float` or `double` for monetary values - always use BigDecimal 2. COMP-3 must be BigDecimal - financial calculations require precision 3. Preserve precision - don't lose decimal places during conversion 4. Unicode handling - PIC N requires proper encoding

SYMFOWARE to PostgreSQL Type Mapping

PostgreSQL Advantages

1. TEXT vs VARCHAR: PostgreSQL's TEXT has no performance penalty 2. SERIAL types: Use SERIAL, BIGSERIAL for auto-increment 3. Arrays: PostgreSQL native array support: INTEGER[], TEXT[] 4. JSON: JSON and JSONB for semi-structured data 5. UUID: Native UUID type for globally unique identifiers

File System to Database Mapping

Fujitsu File Organizations

Migration Strategy by File Type

Sequential Files (SAM):

// Option 1: Keep as file for batch processing
Files.lines(Paths.get("input.dat"))
    .map(this::parseRecord)
    .forEach(this::processRecord);

// Option 2: Load into database
@BatchSize(1000)
List<Record> records = readRecordsFromFile();
repository.saveAll(records);

Indexed Files (ISAM):

// Always migrate to database table
@Entity
@Table(name = "indexed_data", indexes = {
    @Index(name = "idx_key", columnList = "recordKey")
})
public class IndexedData {
    @Id
    private String recordKey;
    private String data;
}

Partitioned Files (PAM):

// Option 1: Keep as files
Path directory = Paths.get("members");
Files.list(directory).forEach(this::processMember);

// Option 2: Store in database with member name
@Entity
public class Member {
    @Id
    private String memberName;
    @Lob
    private String content;
}

COBOL Data Structures to Java Classes

Simple Record

COBOL:

01  CUSTOMER-RECORD.
    05  CUST-ID          PIC 9(8).
    05  CUST-NAME        PIC X(40).
    05  CUST-BALANCE     PIC S9(9)V99 COMP-3.
    05  CUST-STATUS      PIC X.

Java:

@Entity
@Table(name = "customers")
public class CustomerRecord {
    @Id
    @Column(name = "cust_id")
    private Integer custId;
    
    @Column(name = "cust_name", length = 40)
    private String custName;
    
    @Column(name = "cust_balance", precision = 11, scale = 2)
    private BigDecimal custBalance;
    
    @Column(name = "cust_status", length = 1)
    private String custStatus;
}

Nested Structure

COBOL:

01  ORDER-RECORD.
    05  ORDER-ID         PIC 9(10).
    05  CUSTOMER-INFO.
        10  CUST-ID      PIC 9(8).
        10  CUST-NAME    PIC X(30).
    05  ORDER-DATE       PIC X(8).
    05  ORDER-TOTAL      PIC 9(9)V99.

Java:

@Embeddable
public class CustomerInfo {
    @Column(name = "cust_id")
    private Integer custId;
    
    @Column(name = "cust_name", length = 30)
    private String custName;
}

@Entity
@Table(name = "orders")
public class OrderRecord {
    @Id
    @Column(name = "order_id")
    private Long orderId;
    
    @Embedded
    private CustomerInfo customerInfo;
    
    @Column(name = "order_date", length = 8)
    private String orderDate;  // Or use LocalDate
    
    @Column(name = "order_total", precision = 11, scale = 2)
    private BigDecimal orderTotal;
}

Array/Table (OCCURS)

COBOL:

01  MONTHLY-SALES-RECORD.
    05  YEAR             PIC 9(4).
    05  MONTHLY-DATA OCCURS 12 TIMES.
        10  MONTH        PIC 99.
        10  SALES-AMOUNT PIC 9(9)V99.

Java (Separate Entity):

@Entity
@Table(name = "monthly_sales")
public class MonthlySalesRecord {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;
    
    @Column(name = "year")
    private Integer year;
    
    @OneToMany(mappedBy = "salesRecord", cascade = CascadeType.ALL)
    private List<MonthlyData> monthlyData;
}

@Entity
@Table(name = "monthly_data")
public class MonthlyData {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;
    
    @Column(name = "month")
    private Integer month;
    
    @Column(name = "sales_amount", precision = 11, scale = 2)
    private BigDecimal salesAmount;
    
    @ManyToOne
    @JoinColumn(name = "sales_record_id")
    private MonthlySalesRecord salesRecord;
}

REDEFINES (Union Types)

COBOL:

01  TRANSACTION-RECORD.
    05  TRANS-TYPE       PIC X.
    05  TRANS-DATA       PIC X(50).
    05  PAYMENT-DATA REDEFINES TRANS-DATA.
        10  PAYMENT-AMT  PIC 9(7)V99.
        10  PAYMENT-DATE PIC X(8).
        10  FILLER       PIC X(33).
    05  REFUND-DATA REDEFINES TRANS-DATA.
        10  REFUND-AMT   PIC 9(7)V99.
        10  REFUND-DATE  PIC X(8).
        10  REASON       PIC X(33).

Java (Separate View Classes):

@Entity
@Table(name = "transactions")
public class TransactionRecord {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;
    
    @Column(name = "trans_type", length = 1)
    private String transType;
    
    @Column(name = "trans_data", length = 50)
    private String transData;
    
    // View as payment
    public PaymentView asPayment() {
        return new PaymentView(this.transData);
    }
    
    // View as refund
    public RefundView asRefund() {
        return new RefundView(this.transData);
    }
}

public class PaymentView {
    private BigDecimal paymentAmt;
    private String paymentDate;
    
    public PaymentView(String transData) {
        // Parse transData into payment fields
    }
}

public class RefundView {
    private BigDecimal refundAmt;
    private String refundDate;
    private String reason;
    
    public RefundView(String transData) {
        // Parse transData into refund fields
    }
}

Date and Time Conversions

COBOL Date Formats to Java

Date Arithmetic

COBOL:

COMPUTE WS-DAYS = FUNCTION INTEGER-OF-DATE(WS-END-DATE)
                - FUNCTION INTEGER-OF-DATE(WS-START-DATE)

Java:

long days = ChronoUnit.DAYS.between(startDate, endDate);

Best Practices

1. Always use BigDecimal for money - never float/double 2. Use appropriate precision - match COBOL precision in database 3. Handle nulls properly - COBOL spaces vs Java null 4. Validate conversions - test with production data samples 5. Document mappings - create mapping tables for team reference 6. Consider performance - index columns used in WHERE clauses 7. Use constraints - NOT NULL, CHECK, FOREIGN KEY 8. Plan for Unicode - UTF-8 encoding throughout 9. Version control schemas - use Liquibase/Flyway 10. Test boundaries - max values, min values, edge cases

Fujitsu Mainframe Migration Patterns

Detailed migration examples and patterns for converting Fujitsu mainframe applications to modern platforms.

Pattern 1: Fujitsu COBOL to Java Spring Boot

Fujitsu COBOL Program Example

IDENTIFICATION DIVISION.
PROGRAM-ID. EMPMAINT.

ENVIRONMENT DIVISION.
INPUT-OUTPUT SECTION.
FILE-CONTROL.
    SELECT EMPFILE ASSIGN TO "EMPDATA"
        ORGANIZATION IS INDEXED
        ACCESS MODE IS RANDOM
        RECORD KEY IS EMP-ID
        FILE STATUS IS WS-FILE-STATUS.

DATA DIVISION.
FILE SECTION.
FD  EMPFILE.
01  EMP-RECORD.
    05  EMP-ID           PIC 9(6).
    05  EMP-NAME         PIC X(30).
    05  EMP-SALARY       PIC 9(7)V99.

WORKING-STORAGE SECTION.
01  WS-FILE-STATUS       PIC XX.

PROCEDURE DIVISION.
    OPEN INPUT EMPFILE
    MOVE 123456 TO EMP-ID
    READ EMPFILE KEY IS EMP-ID
        INVALID KEY
            DISPLAY "EMPLOYEE NOT FOUND"
        NOT INVALID KEY
            DISPLAY EMP-NAME
    END-READ
    CLOSE EMPFILE
    STOP RUN.

Modern Java Spring Boot Implementation

Entity:

@Entity
@Table(name = "employees")
public class Employee {
    @Id
    private Integer empId;
    
    @Column(length = 30)
    private String empName;
    
    @Column(precision = 9, scale = 2)
    private BigDecimal empSalary;
    
    // Constructors, getters, setters
}

Repository:

@Repository
public interface EmployeeRepository extends JpaRepository<Employee, Integer> {
}

Service:

@Service
public class EmployeeService {
    @Autowired
    private EmployeeRepository repository;
    
    public Employee findEmployee(Integer empId) {
        return repository.findById(empId)
            .orElseThrow(() -> new EmployeeNotFoundException("Employee not found: " + empId));
    }
}

Controller:

@RestController
@RequestMapping("/api/employees")
public class EmployeeController {
    @Autowired
    private EmployeeService service;
    
    @GetMapping("/{empId}")
    public ResponseEntity<Employee> getEmployee(@PathVariable Integer empId) {
        Employee employee = service.findEmployee(empId);
        return ResponseEntity.ok(employee);
    }
}

Pattern 2: Fujitsu JCL to Shell Script + Kubernetes

Fujitsu JCL Example

//DAILYJOB JOB (ACCT123),'DAILY PROCESS',CLASS=A,MSGCLASS=X
//STEP010  EXEC PGM=VALIDATE
//INFILE   ASSIGN DSN=INPUT.DAILY.DATA,DISP=SHR
//OUTFILE  ASSIGN DSN=VALID.DATA,DISP=(NEW,CATLG)
//STEP020  EXEC PGM=PROCESS,COND=(0,EQ,STEP010)
//INFILE   ASSIGN DSN=VALID.DATA,DISP=SHR
//OUTFILE  ASSIGN DSN=PROCESSED.DATA,DISP=(NEW,CATLG)
//STEP030  EXEC PGM=REPORT
//INFILE   ASSIGN DSN=PROCESSED.DATA,DISP=SHR

Modern Shell Script

#!/bin/bash
set -e

LOG_FILE="/var/log/daily_job_$(date +%Y%m%d).log"

log() {
    echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a "$LOG_FILE"
}

# Step 1: Validate
log "Starting validation..."
./validate --input="input_daily_data.csv" --output="valid_data.csv"
validate_rc=$?

if [ $validate_rc -ne 0 ]; then
    log "Validation failed with RC=$validate_rc"
    exit $validate_rc
fi

# Step 2: Process (only if validation succeeded)
log "Starting processing..."
./process --input="valid_data.csv" --output="processed_data.csv"
process_rc=$?

if [ $process_rc -ne 0 ]; then
    log "Processing failed with RC=$process_rc"
    exit $process_rc
fi

# Step 3: Generate report
log "Generating report..."
./report --input="processed_data.csv" --output="daily_report.pdf"

log "Job completed successfully"

Kubernetes CronJob

apiVersion: batch/v1
kind: CronJob
metadata:
  name: daily-job
  namespace: production
spec:
  schedule: "0 2 * * *"  # Run at 2 AM daily
  successfulJobsHistoryLimit: 3
  failedJobsHistoryLimit: 3
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: daily-job
            image: company/daily-job:latest
            command: ["/scripts/daily_job.sh"]
            env:
            - name: ENV
              value: "production"
            volumeMounts:
            - name: data-volume
              mountPath: /data
            - name: logs-volume
              mountPath: /var/log
          volumes:
          - name: data-volume
            persistentVolumeClaim:
              claimName: data-pvc
          - name: logs-volume
            persistentVolumeClaim:
              claimName: logs-pvc
          restartPolicy: OnFailure

Pattern 3: SYMFOWARE SQL to PostgreSQL with JPA

SYMFOWARE Embedded SQL

EXEC SQL
    DECLARE EMP_CURSOR CURSOR FOR
    SELECT EMP_ID, EMP_NAME, SALARY
    FROM EMPLOYEES
    WHERE DEPT_ID = :WS-DEPT-ID
    ORDER BY EMP_NAME
END-EXEC.

EXEC SQL OPEN EMP_CURSOR END-EXEC.

PERFORM UNTIL SQLCODE NOT = 0
    EXEC SQL
        FETCH EMP_CURSOR INTO :EMP-ID, :EMP-NAME, :SALARY
    END-EXEC
    IF SQLCODE = 0
        PERFORM PROCESS-EMPLOYEE
    END-IF
END-PERFORM.

EXEC SQL CLOSE EMP_CURSOR END-EXEC.

Java JPA/Spring Data

@Repository
public interface EmployeeRepository extends JpaRepository<Employee, Integer> {
    
    @Query("SELECT e FROM Employee e WHERE e.deptId = :deptId ORDER BY e.empName")
    List<Employee> findByDeptIdOrderByEmpName(@Param("deptId") Integer deptId);
    
    // Using method naming convention
    List<Employee> findByDeptIdOrderByEmpNameAsc(Integer deptId);
}

@Service
public class EmployeeService {
    
    @Autowired
    private EmployeeRepository repository;
    
    @Transactional(readOnly = true)
    public void processEmployeesByDept(Integer deptId) {
        List<Employee> employees = repository.findByDeptIdOrderByEmpName(deptId);
        employees.forEach(this::processEmployee);
    }
    
    private void processEmployee(Employee employee) {
        // Business logic here
        System.out.println("Processing: " + employee.getEmpName());
    }
}

Pattern 4: Screen Handling - Terminal to Web

Fujitsu COBOL Screen Definition

01  EMPLOYEE-SCREEN.
    05  BLANK SCREEN.
    05  LINE 01 COLUMN 01 VALUE "*** EMPLOYEE MAINTENANCE ***".
    05  LINE 03 COLUMN 01 VALUE "Employee ID: ".
    05  LINE 03 COLUMN 15 PIC 9(6) TO EMP-ID.
    05  LINE 05 COLUMN 01 VALUE "Name: ".
    05  LINE 05 COLUMN 15 PIC X(30) TO EMP-NAME.
    05  LINE 07 COLUMN 01 VALUE "Salary: ".
    05  LINE 07 COLUMN 15 PIC Z(7)9.99 TO EMP-SALARY.
    05  LINE 10 COLUMN 01 VALUE "F1=Help F3=Exit F5=Refresh F8=Update".

PROCEDURE DIVISION.
    DISPLAY EMPLOYEE-SCREEN.
    ACCEPT EMPLOYEE-SCREEN.

Modern Web UI with React

import React, { useState } from 'react';
import axios from 'axios';

const EmployeeMaintenance = () => {
  const [employee, setEmployee] = useState({
    empId: '',
    empName: '',
    empSalary: ''
  });

  const handleChange = (e) => {
    setEmployee({
      ...employee,
      [e.target.name]: e.target.value
    });
  };

  const handleSubmit = async (e) => {
    e.preventDefault();
    try {
      const response = await axios.put(
        `/api/employees/${employee.empId}`,
        employee
      );
      alert('Employee updated successfully');
    } catch (error) {
      alert('Error updating employee: ' + error.message);
    }
  };

  return (
    <div className="container">
      <h1>*** EMPLOYEE MAINTENANCE ***</h1>
      <form onSubmit={handleSubmit}>
        <div className="form-group">
          <label>Employee ID:</label>
          <input
            type="number"
            name="empId"
            value={employee.empId}
            onChange={handleChange}
            maxLength={6}
            required
          />
        </div>
        <div className="form-group">
          <label>Name:</label>
          <input
            type="text"
            name="empName"
            value={employee.empName}
            onChange={handleChange}
            maxLength={30}
            required
          />
        </div>
        <div className="form-group">
          <label>Salary:</label>
          <input
            type="number"
            name="empSalary"
            value={employee.empSalary}
            onChange={handleChange}
            step="0.01"
            required
          />
        </div>
        <div className="button-group">
          <button type="button" onClick={() => window.showHelp()}>
            F1 - Help
          </button>
          <button type="button" onClick={() => window.close()}>
            F3 - Exit
          </button>
          <button type="button" onClick={() => window.location.reload()}>
            F5 - Refresh
          </button>
          <button type="submit">F8 - Update</button>
        </div>
      </form>
    </div>
  );
};

export default EmployeeMaintenance;

Pattern 5: Batch Processing - File to Database

Fujitsu COBOL Batch File Processing

IDENTIFICATION DIVISION.
PROGRAM-ID. BATCHLOAD.

ENVIRONMENT DIVISION.
INPUT-OUTPUT SECTION.
FILE-CONTROL.
    SELECT INPUT-FILE ASSIGN TO "DATAFILE"
        ORGANIZATION IS SEQUENTIAL
        FILE STATUS IS WS-INPUT-STATUS.

DATA DIVISION.
FILE SECTION.
FD  INPUT-FILE.
01  INPUT-RECORD.
    05  IN-CUST-ID       PIC 9(8).
    05  IN-CUST-NAME     PIC X(40).
    05  IN-AMOUNT        PIC 9(9)V99.

WORKING-STORAGE SECTION.
01  WS-INPUT-STATUS      PIC XX.
01  WS-RECORD-COUNT      PIC 9(7) VALUE 0.
01  WS-ERROR-COUNT       PIC 9(7) VALUE 0.

PROCEDURE DIVISION.
    OPEN INPUT INPUT-FILE
    PERFORM UNTIL WS-INPUT-STATUS NOT = '00'
        READ INPUT-FILE
            AT END
                CONTINUE
            NOT AT END
                PERFORM PROCESS-RECORD
        END-READ
    END-PERFORM
    CLOSE INPUT-FILE
    DISPLAY 'PROCESSED: ' WS-RECORD-COUNT
    DISPLAY 'ERRORS: ' WS-ERROR-COUNT
    STOP RUN.

PROCESS-RECORD.
    ADD 1 TO WS-RECORD-COUNT
    EXEC SQL
        INSERT INTO CUSTOMERS (CUST_ID, CUST_NAME, AMOUNT)
        VALUES (:IN-CUST-ID, :IN-CUST-NAME, :IN-AMOUNT)
    END-EXEC
    IF SQLCODE NOT = 0
        ADD 1 TO WS-ERROR-COUNT
        DISPLAY 'ERROR ON RECORD: ' IN-CUST-ID
    END-IF.

Modern Spring Batch

@Configuration
@EnableBatchProcessing
public class BatchLoadConfiguration {
    
    @Bean
    public Job batchLoadJob(JobBuilderFactory jobBuilderFactory,
                            Step batchLoadStep) {
        return jobBuilderFactory.get("batchLoadJob")
            .incrementer(new RunIdIncrementer())
            .flow(batchLoadStep)
            .end()
            .build();
    }
    
    @Bean
    public Step batchLoadStep(StepBuilderFactory stepBuilderFactory,
                              ItemReader<Customer> reader,
                              ItemProcessor<Customer, Customer> processor,
                              ItemWriter<Customer> writer) {
        return stepBuilderFactory.get("batchLoadStep")
            .<Customer, Customer>chunk(1000)
            .reader(reader)
            .processor(processor)
            .writer(writer)
            .faultTolerant()
            .skipLimit(100)
            .skip(DataIntegrityViolationException.class)
            .listener(new JobExecutionListener() {
                @Override
                public void beforeJob(JobExecution jobExecution) {
                    log.info("Starting batch job");
                }
                
                @Override
                public void afterJob(JobExecution jobExecution) {
                    log.info("Batch job completed");
                    log.info("Records processed: " + jobExecution.getStepExecutions()
                        .iterator().next().getWriteCount());
                    log.info("Errors: " + jobExecution.getStepExecutions()
                        .iterator().next().getSkipCount());
                }
            })
            .build();
    }
    
    @Bean
    public FlatFileItemReader<Customer> reader() {
        return new FlatFileItemReaderBuilder<Customer>()
            .name("customerReader")
            .resource(new ClassPathResource("datafile.txt"))
            .delimited()
            .names("custId", "custName", "amount")
            .fieldSetMapper(new BeanWrapperFieldSetMapper<>() {{
                setTargetType(Customer.class);
            }})
            .build();
    }
    
    @Bean
    public ItemProcessor<Customer, Customer> processor() {
        return customer -> {
            // Validation logic
            if (customer.getCustId() == null || customer.getCustName() == null) {
                throw new ValidationException("Invalid customer data");
            }
            return customer;
        };
    }
    
    @Bean
    public RepositoryItemWriter<Customer> writer(CustomerRepository repository) {
        RepositoryItemWriter<Customer> writer = new RepositoryItemWriter<>();
        writer.setRepository(repository);
        writer.setMethodName("save");
        return writer;
    }
}

Migration Strategy Best Practices

1. Start with Data Layer

• Migrate file structures to database tables first
• Validate data conversion with production samples
• Create ETL pipelines for ongoing sync during transition

2. Service Layer Next

• Extract business logic into services
• Maintain same inputs/outputs initially
• Add unit tests for each service

3. API Layer

• Expose services via REST/GraphQL APIs
• Document with OpenAPI/Swagger
• Version APIs for backward compatibility

4. UI Layer Last

• Modern web/mobile UI
• Progressive enhancement
• Parallel run with legacy screens

5. Testing Strategy

• Compare outputs: legacy vs new
• Performance benchmarking
• Load testing with production volumes
• User acceptance testing

6. Deployment Strategy

• Blue-green deployment
• Feature flags for gradual rollout
• Monitoring and alerting
• Rollback plan ready

Fujitsu Mainframe Migration Strategy Framework

Complete framework for planning and executing Fujitsu mainframe migrations to modern platforms.

1. Assessment Phase

1.1 Inventory Collection

Create comprehensive inventory of all mainframe assets:

COBOL Programs:

• Count by type: NetCOBOL, PowerCOBOL, standard COBOL
• Lines of code per program
• Complexity metrics (cyclomatic complexity)
• Dependencies (CALL hierarchies)
• Screen programs vs batch programs

Database Assets:

• SYMFOWARE databases and schemas
• Table counts and row counts
• Stored procedures and triggers
• Views and indexes
• Transaction volume

JCL Jobs:

• Job count and frequency
• Steps per job
• Dependencies between jobs
• Resource requirements
• Schedule information

File Systems:

• SAM, PAM, ISAM file usage
• File sizes and volumes
• GDG datasets
• Data retention requirements

Copybooks and Includes:

• Shared data structures
• Usage frequency
• Dependencies

1.2 Complexity Analysis

Program Complexity Scoring:

Score = (LOC / 1000) * 0.3 
      + (ExternalCalls * 2) * 0.2
      + (FileOperations * 1.5) * 0.2
      + (SQLStatements * 1) * 0.15
      + (ScreenHandling * 2) * 0.15

Classification:
- Low: Score < 10
- Medium: Score 10-30
- High: Score 30-100
- Very High: Score > 100

Fujitsu-Specific Complexity:

• NetCOBOL OO features usage
• PowerCOBOL GUI components
• SYMFOWARE-specific SQL
• BS2000/OSD system calls
• Custom Fujitsu extensions

1.3 Dependency Analysis

Build complete dependency graph:

• Program call hierarchies
• Shared copybooks
• Database table access patterns
• File dependencies
• Job scheduling dependencies

1.4 Business Impact Assessment

• Critical business functions
• Transaction volumes
• Performance requirements
• Availability requirements (24/7, business hours)
• Regulatory compliance needs

2. Target Architecture Design

2.1 Technology Stack Selection

Application Layer:

• Framework: Spring Boot (recommended), Quarkus, Micronaut
• Language: Java 17+ or Kotlin
• API: REST (Spring REST), GraphQL (Spring GraphQL)
• Async: Spring WebFlux for reactive

Data Layer:

• Primary DB: PostgreSQL (cost-effective), Oracle (enterprise), SQL Server
• Caching: Redis, Hazelcast
• Search: Elasticsearch (if needed)
• Message Queue: RabbitMQ, Kafka

Batch Processing:

• Spring Batch (enterprise)
• Apache Airflow (complex workflows)
• Kubernetes CronJobs (simple)

Frontend:

• Web: React, Angular, Vue.js
• Mobile: React Native, Flutter
• Desktop: Electron (if needed)

Infrastructure:

• Cloud: AWS, Azure, GCP
• Containers: Docker
• Orchestration: Kubernetes
• Service Mesh: Istio (optional)

2.2 Microservices Decomposition

Identify Bounded Contexts:

Example for banking:
- Customer Management Service
- Account Service
- Transaction Service
- Loan Service
- Reporting Service

Design Principles:

• Single Responsibility
• Loose Coupling
• High Cohesion
• Independent Deployability
• Database per Service

API Design:

• RESTful conventions
• Versioning strategy (URL, header)
• OpenAPI documentation
• Rate limiting
• Authentication/Authorization (OAuth2, JWT)

2.3 Data Architecture

Database Strategy:

• Separate databases per microservice
• Shared reference data strategy
• Event-driven data sync
• CQRS pattern (if needed)

Migration Patterns:

SYMFOWARE → PostgreSQL:
1. Schema conversion
2. Data type mapping
3. Stored procedure conversion
4. Trigger rewrite
5. Performance tuning

File to Database Migration:

ISAM Files → Database Tables:
- Create table with primary key
- Add appropriate indexes
- Migrate data with validation
- Update programs to use DB

Sequential Files → Keep or DB:
- Low frequency: Keep as files
- High frequency: Consider database
- Batch processing: File might be fine

3. Migration Strategy Selection

3.1 Strangler Fig Pattern (Recommended)

Approach: Gradually replace functionality while keeping system operational.

Steps:

1. Identify first bounded context to migrate 2. Build new service with same interface 3. Route traffic to new service 4. Validate results in parallel 5. Cutover when confidence high 6. Repeat for next context

Advantages:

• Lower risk
• Continuous value delivery
• Learn and adjust
• Rollback easier

Disadvantages:

• Longer timeline
• Integration complexity
• Maintaining two systems

Best for:

• Large, complex systems
• Risk-averse organizations
• Limited migration team

3.2 Big Bang Migration

Approach: Complete rewrite, cutover at once.

Steps:

1. Build entire new system 2. Migrate all data 3. Extensive testing 4. Single cutover event

Advantages:

• Clean architecture
• Shorter calendar time (if successful)
• No hybrid complexity

Disadvantages:

• Very high risk
• Long time to value
• Difficult rollback

Best for:

• Smaller systems
• Greenfield opportunity
• Strong team with mainframe knowledge

3.3 Hybrid Approach

Approach: Core services modernized, periphery later.

Steps:

1. Identify core 20% delivering 80% value 2. Modernize core services 3. Keep peripheral functions on mainframe 4. Integrate via APIs 5. Gradually modernize peripheral

Advantages:

• Balanced risk/reward
• Quick wins possible
• Focus on high value

Disadvantages:

• Integration overhead
• Two platforms to maintain

Best for:

• Medium complexity
• Budget constraints
• Quick ROI needed

4. Data Migration Planning

4.1 Data Migration Strategy

Full Migration:

• All data moved at once
• Requires downtime
• Simpler approach

Incremental Migration:

• Data migrated in phases
• Minimal downtime
• Sync mechanism needed

Parallel Run:

• Both systems active
• Data synced bidirectionally
• Gradual cutover

4.2 Data Migration Steps

Phase 1: Analysis

1. Data profiling 2. Quality assessment 3. Mapping design 4. Transformation rules

Phase 2: Preparation

1. Create target schemas 2. Build ETL pipelines 3. Data cleansing 4. Validation rules

Phase 3: Migration

1. Initial bulk load 2. Incremental updates 3. Data reconciliation 4. Performance tuning

Phase 4: Validation

1. Row count validation 2. Checksum verification 3. Business rule validation 4. Sample data review

4.3 Data Migration Tools

ETL Tools:

• Talend
• Apache NiFi
• AWS Glue
• Azure Data Factory

Custom Scripts:

@Service
public class DataMigrationService {
    
    @Transactional
    public void migrateCustomers(String sourceFile) {
        List<Customer> customers = readFromMainframe(sourceFile);
        
        customers.stream()
            .map(this::transform)
            .forEach(customer -> {
                validateAndSave(customer);
                logMigration(customer);
            });
    }
    
    private Customer transform(LegacyCustomer legacy) {
        return Customer.builder()
            .customerId(legacy.getCustId())
            .name(legacy.getCustName().trim())
            .balance(legacy.getBalance())
            .build();
    }
}

5. Testing Strategy

5.1 Testing Levels

Unit Testing:

• JUnit 5
• Mockito for mocking
• TestContainers for integration
• Code coverage: 80%+ target

Integration Testing:

• API testing with REST Assured
• Database testing
• Message queue testing
• External service mocking

System Testing:

• End-to-end scenarios
• Performance testing
• Security testing
• Disaster recovery testing

UAT (User Acceptance Testing):

• Business user validation
• Real scenarios
• Production-like data
• Sign-off process

5.2 Migration Validation

Output Comparison:

# Run same input through both systems
./legacy_system < input.dat > legacy_output.txt
./new_system < input.dat > new_output.txt

# Compare outputs
diff legacy_output.txt new_output.txt

Parallel Run:

• Route requests to both systems
• Compare responses
• Alert on differences
• Build confidence over time

Shadow Mode:

• New system processes in background
• Doesn't affect live traffic
• Results logged for comparison

5.3 Performance Testing

Load Testing:

• JMeter, Gatling, K6
• Match production volumes
• Identify bottlenecks
• Capacity planning

Benchmarks:

• Response time vs legacy
• Throughput comparison
• Resource utilization
• Cost per transaction

6. Cutover Planning

6.1 Pre-Cutover Activities

1-2 Months Before:

• Complete UAT
• Performance testing passed
• Security audit completed
• Training completed

1-2 Weeks Before:

• Cutover rehearsal
• Final data migration test
• Rollback plan tested
• Communication plan ready

1 Day Before:

• Freeze legacy system
• Final backup
• Team on standby

6.2 Cutover Day

Timeline Example:

Friday 6:00 PM - Freeze legacy system
Friday 7:00 PM - Start final data migration
Saturday 12:00 AM - Data migration complete
Saturday 1:00 AM - Validation checks
Saturday 2:00 AM - Switch traffic to new system
Saturday 3:00 AM - Smoke tests
Saturday 6:00 AM - Monitor closely
Monday 8:00 AM - Business opens with new system

Cutover Checklist:

• [ ] Legacy system frozen
• [ ] Final data migrated
• [ ] Data validation passed
• [ ] DNS/Load balancer updated
• [ ] Traffic flowing to new system
• [ ] Smoke tests passed
• [ ] Monitoring active
• [ ] Support team ready
• [ ] Rollback plan ready

6.3 Post-Cutover

First 24 Hours:

• War room active
• All hands on deck
• Monitor metrics closely
• Quick issue resolution

First Week:

• Daily team meetings
• Issue tracking
• Performance monitoring
• User feedback collection

First Month:

• Weekly reviews
• Optimization iterations
• Documentation updates
• Lessons learned

6.4 Rollback Plan

Triggers for Rollback:

• Critical business function failure
• Data corruption detected
• Performance below acceptable
• Security breach

Rollback Steps:

1. Announce rollback decision 2. Stop traffic to new system 3. Restore legacy system 4. Sync any changed data 5. Verify legacy system operational 6. Communicate to stakeholders

7. Risk Management

7.1 Key Risks

7.2 Risk Mitigation Strategies

Technical Risks:

• Proof of concepts early
• Prototype key components
• Load testing early and often
• Automated testing

People Risks:

• Training programs
• Knowledge transfer sessions
• External consultants
• Documentation

Process Risks:

• Clear governance
• Change management
• Regular checkpoints
• Escalation procedures

8. Success Metrics

8.1 Technical Metrics

Migration Progress:

• % programs migrated
• % data migrated
• % tests passing
• Code coverage

Quality Metrics:

• Defect density
• Mean time to repair (MTTR)
• Availability (99.9%+)
• Performance vs baseline

Operational Metrics:

• Deployment frequency
• Lead time for changes
• Change failure rate
• Time to restore service

8.2 Business Metrics

Value Delivery:

• Features delivered
• User satisfaction
• Business value realized
• ROI

Cost Metrics:

• Migration cost vs budget
• Ongoing operational cost
• Mainframe decommission savings
• TCO (Total Cost of Ownership)

8.3 KPIs

Phase-Based KPIs:

Assessment Phase:
- Inventory completeness: 100%
- Complexity scoring: Complete

Design Phase:
- Architecture approval: Yes
- Technology decisions: Final

Development Phase:
- Sprint velocity: Tracking
- Test coverage: 80%+

Testing Phase:
- UAT sign-off: Yes
- Performance targets: Met

Cutover Phase:
- Downtime: < target
- Data accuracy: 100%

9. Cost Estimation

9.1 Cost Components

One-Time Costs:

• Assessment and planning
• Architecture and design
• Development (biggest component)
• Testing
• Data migration
• Training
• Cutover activities

Ongoing Costs:

• Cloud infrastructure
• Licenses (if any)
• Support and maintenance
• Monitoring tools
• Continuous improvement

9.2 Estimation Model

Development Effort:

Effort (person-days) = 
    (Low complexity programs * 2 days) +
    (Medium complexity programs * 5 days) +
    (High complexity programs * 15 days) +
    (Very high complexity programs * 40 days)

Add 30% for:
- Testing
- Project management
- Documentation
- Contingency

Cost Calculation:

Total Cost = 
    (Effort in person-days * Daily rate) +
    Infrastructure costs +
    Tool licenses +
    Training costs +
    Contingency (20%)

10. Communication Plan

10.1 Stakeholder Management

Executive Sponsors:

• Monthly steering committee
• Progress reports
• Risk and issue escalation
• Budget tracking

Business Users:

• Regular demos
• UAT involvement
• Training sessions
• Change management

Technical Teams:

• Daily standups
• Sprint reviews
• Technical design sessions
• Knowledge sharing

10.2 Communication Frequency

Daily:

• Development team standup
• Issue tracking updates

Weekly:

• Project status report
• Risk and issue review
• Sprint planning

Monthly:

• Executive steering
• Budget review
• Milestone tracking

Ad-hoc:

• Critical issues
• Major decisions
• Scope changes

11. Knowledge Transfer

11.1 Documentation

Technical Documentation:

• Architecture diagrams
• API documentation
• Database schemas
• Deployment guides
• Runbooks

User Documentation:

• User guides
• Training materials
• FAQs
• Video tutorials

11.2 Training Program

Technical Training:

• New technology stack
• Architecture overview
• Development standards
• Deployment procedures

Business Training:

• New UI/UX
• Changed workflows
• Reporting changes
• Support procedures

12. Post-Migration Activities

12.1 Optimization

Performance Tuning:

• Database query optimization
• Caching improvements
• Code optimization
• Infrastructure scaling

Cost Optimization:

• Right-size resources
• Reserved instances
• Eliminate waste
• Monitor usage

12.2 Continuous Improvement

Technical Debt:

• Address shortcuts
• Refactor as needed
• Update dependencies
• Security patches

Feature Enhancements:

• User feedback
• New capabilities
• Modernization beyond parity

12.3 Mainframe Decommission

Steps:

1. Verify new system stable (3-6 months) 2. Archive mainframe data 3. Document retention requirements 4. Decommission hardware 5. Cancel licenses 6. Realize cost savings

Checklist:

• [ ] All functions verified in production
• [ ] No rollback scenarios remaining
• [ ] Data archived per policy
• [ ] Audit requirements met
• [ ] Stakeholder approval
• [ ] Contracts terminated
• [ ] Cost savings captured