Do these migration tools actually work or are they just expensive bullshit?

Some actually work, most are expensive garbage. AWS Application Discovery Agent finds dependencies by watching network traffic - that's genuinely useful. mLogica's COBOL converter produces ugly Java but it compiles and runs - better than paying $250/hour for COBOL consultants who are all over 65. SonarQube finds real security issues but also flags 12,000 false positives. You'll spend 3 weeks arguing with your team about whether a SQL injection risk in a debug-only method that's called by the production error handler is worth fixing. **Reality check:** Tools handle about 65% of the boring work. The other 35% is business logic, edge cases, and weird integration patterns that some genius implemented in 2007 without documentation. Don't believe vendor demos - they always demo the one Spring Boot app that follows every best practice.

Which legacy apps can actually be migrated vs. which ones are fucked?

**Easy wins (tools work well):** - **Standard Java/Spring apps** - if your code follows normal patterns, Konveyor and similar tools handle it - **Simple COBOL business logic** - payroll, accounting, basic CRUD operations convert okay - **SQL Server/.NET apps** - Microsoft's migration tools work if you're not doing anything weird - **Oracle to PostgreSQL** - AWS DMS handles this well if you don't use Oracle-specific features **Prepare for pain:** - **Mainframes with assembler modules** - HLASM code usually needs complete rewrite - **Oracle Forms applications** - no good automated conversion tools exist, plan to rewrite - **Apps with COM/DCOM dependencies** - Windows-specific bullshit doesn't translate - **Custom protocols and file formats** - tools have no idea what your proprietary binary format does - **Stored procedures with cursors and temp tables** - database conversion tools choke on complex SQL **Nuclear waste (just rewrite):** - **PowerBuilder applications** - seriously, just start over - **VB6 with ActiveX controls** - the components don't exist anymore anyway - **Apps that call legacy DLLs** - if you can't find source code, you're screwed

How much money should I budget before my CFO fires me?

**Real world costs (based on actual projects):** **Small enterprise app (50K-100K lines):** - Tools: $75K-150K - Consultants because tools break in unexpected ways: $300K-600K - Internal team time: 8-15 months of senior devs who threaten to quit daily - **Total: $650K-1.2M** **Medium legacy monolith (100K-500K lines):** - Tools: $300K-750K (mLogica licensing will murder your budget) - Consulting: $750K-2.5M (specialists who actually know what they're doing) - Internal resources: 15-24 months including overtime and therapy - **Total: $1.8M-4.5M** **Mainframe migration (the nuclear option):** - mLogica LIBER*M licensing: $1M-3M+ (they have you by the balls) - Specialized consulting: $2M-8M (COBOL experts cost more than surgeons) - Internal project management: 24-48 months if nothing catastrophic happens - **Total: $5M-20M+ (yes, seriously, this is not a typo)** **Hidden costs that will fuck your budget:** - Parallel running costs (keeping old system alive) - Data migration scripts (always takes longer than expected) - Testing environments (need copies of production data) - Training your team on new tools - Rollback planning (because you'll need it)

Will this migration pass our security audit or get me fired?

**Tools find some security issues but miss the subtle ones:** SonarQube catches obvious SQL injection and XSS vulnerabilities but won't understand that your legacy authentication system uses MD5 hashes (which is probably a bigger problem). Migration tools don't understand your specific compliance requirements. If you're in healthcare, tools won't know that logging patient names violates HIPAA. If you're processing payments, tools won't catch that you're storing credit card data in session variables. **What actually happens:** - Automated scans find 80% of code-level security issues - You'll still need security experts to review architecture changes - Compliance auditors will want to review everything manually anyway - Plan an extra 2-3 months for security remediation after migration **Pro tip:** Run security scans on your legacy code BEFORE migration. Fix the obvious stuff first so you're not debugging security issues and migration bugs at the same time.

What happens when the migration inevitably goes to shit?

**It will go wrong. Plan for it:** Your converted COBOL will compile but fail on the first batch job because date formatting changed from `YYMMDD` to `YYYY-MM-DD` and nobody documented this anywhere. Your database migration will finish but all the currency calculations will be off by $0.01 due to decimal precision changes and you'll discover this when accounting reports $50K missing. Your web app will work fine until you hit that one edge case that processes files uploaded in 1987 with EBCDIC encoding that doesn't exist anymore. **Rollback planning (actually useful):** - Keep your legacy system running for at least 3 months after cutover - Have scripts ready to quickly switch traffic back - Document every configuration change you made - Test your rollback procedure before you need it **What actually saves your ass:** - Feature flags to turn off broken functionality quickly - Database snapshots taken right before cutover - Monitoring that alerts when shit breaks (not just when servers are down) - A senior engineer who knows both the old and new systems **Real failure rates (based on actual data, not vendor bullshit):** - 72% of migrations have major issues in the first month (usually at 3 AM) - 43% need significant code fixes after going live because testing missed edge cases - 15% actually roll back to the legacy system and pretend the migration never happened - Budget an extra 8-12 months for "stabilization" and fixing shit that should have worked

How long before my team stops complaining about these tools?

**Realistic learning curves:** **Senior engineers:** 2-3 weeks to understand what the tools actually do vs. what the vendor claims. Another month to figure out workarounds for when tools break. **Junior developers:** 1-2 weeks to learn the tools, 6 months to learn when the automated tools are lying to them about what actually works in production. **Project managers:** 2 days to learn the reporting dashboards, 6 months to learn that vendor timelines are bullshit. **DevOps engineers:** 1 week to integrate tools, 3 months to fix all the edge cases that break your pipelines. **What your team actually needs training on:** - How to debug when automated conversion fails - When to ignore tool recommendations (hint: often) - How to manually fix the weird edge cases - Why the generated code looks nothing like what humans would write

Will this completely fuck up our existing CI/CD pipeline?

**Good news:** Most tools integrate reasonably well with existing pipelines. **Bad news:** You'll spend weeks configuring and debugging integrations. **What actually changes:** - Your build times increase because tools add scanning steps - Code reviews take longer because you're reviewing generated code - Testing becomes more complex because you need to verify automated conversions - Deployment scripts need updates for new application architectures **Shit that will break:** - Custom build scripts that make assumptions about file locations - Security scanners that don't understand generated code patterns - Monitoring tools that look for specific log formats - Deployment automation that expects certain directory structures **Plan 2-4 weeks** just for pipeline integration and debugging, plus another month of fixing random issues that surface later.

What's the real success criteria (not vendor marketing bullshit)?

**Honest success metrics:** **Did we not get fired?** - Application works well enough that users stop complaining - No major data loss or corruption - Security audit doesn't find anything that makes the CISO cry - Project finished within 150% of original budget and timeline **Technical reality check:** - 90%+ of core functionality works correctly - Performance is "good enough" (may be slower than legacy) - Can deploy without manual intervention most of the time - Recovery time from outages is reasonable **Business success:** - Lower operational costs than legacy system (eventually) - Can actually find developers who know the new technology - Business stakeholders stop asking when we're reverting to the old system - Maintenance team doesn't threaten to quit

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AI-Optimized Cloud Native Migration Knowledge Base

Executive Summary

Legacy application migration to cloud-native platforms requires 8-24 months, costs $650K-20M+, and has a 72% major issue rate in the first month. Tools handle 65% of routine work; remaining 35% involves undocumented business logic and integration patterns that cause project failures.

Critical Failure Scenarios

High-Impact Failures

Payroll calculation errors: 3-week downtime, near-termination consequences
Invoice miscalculations: $50K revenue loss from $0.02 rounding errors
Authentication system breakdown: Complete user lockout scenarios
Database connection exhaustion: Application self-DDOS under normal load
Memory leaks masked by nightly reboots: Containerization exposes 10x usage increase

Hidden Dependencies That Break Everything

Monthly batch jobs running at 3 AM on last Tuesday
Registry keys set by German-commented batch scripts
Hardcoded Thread.sleep(30000) in payment processing
Business logic in stored procedures named SP_PROCESS_STUFF_V2_FINAL_REALLY
RabbitMQ configurations in undocumented registry locations

Tool Effectiveness Matrix

Tool	Automation Rate	Real Timeline	Cost Range	Critical Limitations
AWS Application Discovery Agent	90% network dependencies	1 week collection, 2 weeks analysis	$500/month for 50 servers	Misses batch jobs, scheduled tasks, manual processes
mLogica LIBER*M	70% COBOL conversion	8-24 months	$750K-3M+	Generated Java is functional but ugly, performance degradation
AWS DMS	80% database migration	3-12 weeks + 4-8 weeks debugging	$300-5K/month	Complex stored procedures, cursors, custom data types fail
SonarQube	80% security issue detection	1-2 days scan, weeks to fix	Free-$150K+	90% false positives, architectural problems undetected
Konveyor	65% common patterns	1-3 weeks assessment	Free	Limited to standard patterns, custom code needs manual work

Real Resource Requirements

Small Enterprise App (50K-100K lines)

Timeline: 8-15 months
Cost: $650K-1.2M
Team: 3-5 senior engineers
Success Rate: 60% without major issues

Medium Monolith (100K-500K lines)

Timeline: 15-24 months
Cost: $1.8M-4.5M
Team: 5-8 engineers + specialized consultants
Success Rate: 45% without major rollbacks

Mainframe Migration

Timeline: 24-48 months
Cost: $5M-20M+
Team: 8-15 specialists (COBOL experts cost $250/hour)
Success Rate: 30% on initial timeline/budget

Hidden Cost Multipliers

Add 50% buffer for unexpected failures
Parallel system running costs during migration
Security remediation: 2-3 additional months
Performance optimization: 6-12 months post-migration
Staff training and knowledge transfer: 3-6 months

Migration Readiness Assessment

Easy Wins (Tools Handle Well)

Standard Java/Spring applications following best practices
Simple COBOL business logic (payroll, accounting, CRUD)
SQL Server to PostgreSQL with minimal stored procedures
Standard .NET applications without COM dependencies

Moderate Difficulty (Partial Tool Support)

Oracle Forms with complex business logic
Java applications with custom frameworks
Multi-database applications with cross-references
Legacy apps with embedded middleware dependencies

High Risk/Rewrite Recommended

PowerBuilder applications (no viable conversion path)
VB6 with ActiveX controls (components no longer exist)
Applications calling legacy DLLs without source code
Mainframes with extensive assembler modules
Custom protocols and proprietary binary formats

Performance Impact Patterns

Containerization Performance Penalties

Memory usage: 2-4x increase due to container overhead
Startup time: 8-45 minutes vs. legacy 45 seconds
CPU utilization: 150% spikes during container initialization
Network latency: Additional 50-200ms between microservices

Database Migration Performance Issues

Query performance: 100ms becomes 2+ seconds
Connection pool exhaustion: 20 concurrent connections insufficient, need 25+
Data type conversion: Oracle NUMBER to PostgreSQL DECIMAL rounding differences
Stored procedure: Manual conversion required, performance typically degrades

Post-Migration Reality (6-18 months)

Phase 1: Initial Stability (Months 1-3)

Memory usage 4x higher than legacy
Database connection exhaustion under normal load
8-minute startup times
SSL certificate renewal failures
Uncontrolled log storage costs ($500+/month)

Phase 2: Performance Problems (Months 3-9)

Containerized apps slower than 10-year-old hardware
Database queries 20x slower
Kubernetes pod killing due to memory limits
Auto-scaling triggers during business hours
Network latency accumulation between services

Phase 3: Cost Reality (Months 6-18)

Cloud costs 2-3x higher than legacy infrastructure
Data transfer: $0.01/GB adds up to $2K+/month
NAT Gateway: $180/month for HA setup
Load balancer: $200+/month minimum
Applications scale up but never down

Security Audit Considerations

Tool Detection Capabilities

SonarQube: Catches 80% of code-level issues, misses architectural problems
Automated scans: Find obvious SQL injection/XSS, miss subtle compliance issues
Legacy authentication: MD5 hashes, hardcoded passwords often undetected
Compliance gaps: HIPAA, PCI DSS violations require manual review

Security Remediation Timeline

Code-level fixes: 2-4 weeks
Architecture review: 4-8 weeks
Compliance validation: 3-6 months
Penetration testing: 2-4 weeks

Rollback Planning Requirements

Critical Rollback Triggers

Data corruption or calculation errors
Performance degradation >50%
Security vulnerabilities exposed
Integration failures with critical systems

Rollback Preparation

Legacy system maintained 3+ months post-cutover
Traffic switching scripts tested and documented
Database snapshots taken before cutover
Configuration change documentation complete
Feature flags for quick functionality disable

Success Criteria (Realistic)

Technical Minimums

90%+ core functionality operational
Performance within 150% of legacy baseline
Security audit passes without critical findings
Deployment automation functional 85% of time

Business Success Indicators

Operational costs equal or lower within 12 months
Development team productivity maintained or improved
User complaint volume returns to baseline
No rollback required after 6-month stabilization

Team Success Metrics

Senior engineers not threatening to quit daily
On-call alerts <5 per week after stabilization
Knowledge transfer complete (junior devs can modify code)
Vacation possible without emergency calls

Tool Implementation Sequence

Phase 1: Discovery (Weeks 1-4)

Install AWS Application Discovery Agent on all servers
Run SonarQube scans on entire codebase
Document all database dependencies and schemas
Inventory integration points and external services

Phase 2: Assessment (Weeks 5-8)

Analyze discovery data for hidden dependencies
Prioritize security vulnerabilities from code scans
Create migration complexity scoring for each component
Develop rollback and testing strategies

Phase 3: Migration Execution (Months 3-18)

Start with non-critical applications
Database migration using AWS DMS
Application conversion with appropriate tools
Containerization and Kubernetes deployment
Performance tuning and optimization

Common Failure Patterns to Avoid

Database Migration Failures

Oracle PL/SQL cursor-based procedures don't convert
Hardcoded timezone assumptions (EST vs UTC)
Connection pool sizing underestimated
Data type precision changes break financial calculations

Application Migration Failures

WebSphere-specific thread pool dependencies
File path assumptions (/opt/legacy/data doesn't exist in containers)
Authentication integration with legacy LDAP attributes
Memory leak detection masked by nightly reboots

Infrastructure Migration Failures

Kubernetes resource limits too restrictive
Storage class selection inappropriate for workload
Network policies blocking legitimate traffic
Monitoring gaps during critical failure scenarios

Decision Framework

Use Migration Tools When:

Standard technology stack (Java/Spring, .NET, standard COBOL)
Well-documented architecture and dependencies
Sufficient budget for 18+ month project
Risk tolerance for 72% chance of initial major issues

Consider Rewrite When:

Proprietary/obsolete technology stack
Undocumented systems with departed original developers
Critical systems requiring 99.9%+ uptime
Budget constraints preventing proper tool licensing

Abort Migration When:

Source code unavailable or incomplete
Business stakeholders cannot define current functionality
Timeline pressure prevents proper testing
Security requirements cannot be met with current tools

Performance Optimization Checklist

Kubernetes Resource Configuration

resources:
  requests:
    memory: "1.5Gi"    # Start 3x higher than tool recommendations
    cpu: "750m"        # Account for container startup spikes
  limits:
    memory: "3Gi"      # Allow headroom for Java heap expansion
    cpu: "2000m"       # Handle 150% CPU spikes during initialization

Database Connection Tuning

Maximum pool size: 25 (not default 20)
Minimum idle: 8 (not default 5)
Connection timeout: 20 seconds (not 30)
Idle timeout: 10 minutes
Max lifetime: 20 minutes before rotation

JVM Container Optimization

Use G1GC for containerized applications
Set MaxRAMPercentage=75 (not default 25)
Enable heap dumps on OOM for debugging
Configure timezone explicitly (don't rely on container defaults)

Cost Control Strategies

Right-Sizing Approach

Monitor actual resource usage for 30 days
Reduce CPU requests by 50% from initial estimates
Use gp3 storage (20% cost reduction vs gp2)
Implement Vertical Pod Autoscaler for ongoing optimization

Hidden Cost Mitigation

Data transfer between AZs: $0.01/GB (budget $2K+/month)
NAT Gateway: $45/month per AZ (required for private subnets)
Load balancer: $16/month + $0.008/hour per LCU
Storage IOPS: Provision exact requirements, not maximum

This knowledge base provides operational intelligence for successful legacy application migration, emphasizing real-world challenges and proven solution approaches over vendor marketing claims.

Useful Links for Further Investigation

Tools That Actually Help (Not Vendor Marketing Lists)

Link	Description
AWS Application Discovery Agent	Actually works for network dependency mapping. Install on legacy servers, let it run for a week, get useful data. Misses batch jobs and scheduled tasks but way better than asking Steve from ops who left six months ago.
SonarQube	Free static code analysis that finds real security issues and code smells. Community edition works fine for most migrations. Expect 15,000+ issues, focus on the ones that won't make your security team cry first.
Konveyor	Open source migration toolkit for Java applications. Actually works for Spring framework migrations. Better than IBM's tools if you're not locked into IBM's ecosystem.
mLogica LIBER*M	Expensive as hell but actually converts COBOL to Java. Generated code is ugly but compiles and runs. Better than hiring COBOL contractors at $250/hour who are all over 65. Expect 8-24 month projects minimum.
IBM Transformation Advisor	Free tool that gives you a pretty report telling you exactly what you already knew: your code is old and needs fixing. Useful for management presentations, less useful for actual migration work. Registration required, lots of downloads.
AWS Database Migration Service	Actually works well for Oracle to PostgreSQL, SQL Server to MySQL, etc. Schema conversion tool handles 80% automatically, you fix the rest manually. Plan 2-4 weeks for data migration plus debugging.
Flyway	Simple database versioning that works. Free community edition handles most migration needs. Better than custom migration scripts that break everything.
pgloader	Fast, reliable tool for migrating data to PostgreSQL from MySQL, Oracle, SQLite. Command-line tool that handles data type conversions better than most GUI tools.
Terraform	Infrastructure as code that actually works. Learn this if you want reproducible deployments. Free and better than cloud-specific tools for multi-cloud scenarios.
Docker	Containerization platform you'll need for cloud deployment. Start with basic Dockerfiles, optimize later. Don't overthink it initially.
Kubernetes	Container orchestration platform that everyone uses. Complex but necessary for cloud-native apps. Start with basic deployments, add complexity gradually.
Prometheus	Open source monitoring that works. Set up basic alerting for CPU, memory, and application health. Free and integrates with everything.
Grafana	Visualization for Prometheus metrics. Makes pretty dashboards that executives like. Free community edition works fine.
ELK Stack (Elasticsearch, Logstash, Kibana)	Log aggregation and analysis. Useful for debugging when things break. Heavy on resources but worth it for large applications.
Stack Overflow	Where you'll find real solutions to migration problems. Search before posting, someone already asked your question.
Kubernetes Stack Overflow	Real engineers discussing real problems. Less marketing bullshit than vendor forums.
CNCF Slack	Active community for Kubernetes and cloud-native questions. Join specific channels for tools you're using.
Kubernetes Documentation	Actually well-written documentation. Start with concepts, then tutorials, then reference materials.
GitHub - Awesome Lists	Curated lists of actually useful tools and resources. Better than vendor "ecosystem" pages.
Linux Academy / A Cloud Guru	Practical training with hands-on labs. More useful than vendor certification training for actual skill building.
DevOps Stack Exchange	Real world experiences and honest tool reviews. Good for "what actually works" discussions.
Platform Engineering Slack	Community focused on platform engineering and cloud migrations. Active discussions about real implementations.
Local Meetups	Find local Kubernetes/Docker/DevOps meetups. Networking with people who've solved similar problems is invaluable.