# ========== BIG BANG MIGRATION STRATEGY ==========
# Voordelen: Simpel, compleet, minder complexiteit
# Nadelen: Hoge downtime, hoog risico, alles-of-niets

strategy: big_bang
timeline:
  phase: Pre-Migration
  duration: 2-3 maanden
  activities:
    - Data assessment en profiling
    - Schema design en mapping
    - ETL development
    - Test environment setup
  
  phase: Migration Execution
  duration: 48-72 uur (weekend)
  downtime: volledige downtime
  steps:
    - Application freeze (T-24 uur)
    - Final full data extract (T-12 uur)
    - Data transformation en loading (T-12 tot T-4)
    - Validation en reconciliation (T-4 tot T-2)
    - Cutover to new system (T-2 tot T-0)
    - Application restart (T-0)
  
  phase: Post-Migration
  duration: 1-2 weken
  activities:
    - Monitoring en performance tuning
    - User acceptance testing
    - Old system decommissioning

# ========== TRICKLE (PHASED) MIGRATION ==========
# Voordelen: Minimale downtime, incremental, lager risico
# Nadelen: Complexer, langere timeline, data sync nodig

strategy: trickle_phased
timeline:
  phase: Parallel Run Phase 1
  duration: 1 maand
  approach: Pilot migration
  scope: 10% non-critical data
  activities:
    - Initial data sync
    - Bi-directional synchronization
    - User pilot testing
    - Performance baselining
  
  phase: Parallel Run Phase 2
  duration: 2 maanden
  approach: Departmental migration
  scope: 40% business units
  activities:
    - Department-by-department cutover
    - Continuous data sync
    - Departmental validation
    - Training en support
  
  phase: Parallel Run Phase 3
  duration: 1 maand
  approach: Remaining migration
  scope: Final 50% data
  activities:
    - Final data synchronization
    - Cutover planning voor resterende data
    - Full system validation
    - Old system read-only mode
  
  phase: Decommissioning
  duration: 2 weken
  activities:
    - Final data verification
    - Archive old system data
    - Decommission old infrastructure
    - Lessons learned documentation

# ========== HYBRID STRATEGY ==========
# Combineer beide benaderingen voor optimaal resultaat

strategy: hybrid_approach
components:
  - static_data: big_bang  # Reference data, configuration
  - transactional_data: trickle  # Customer data, orders
  - historical_data: big_bang  # Archived data
  - real_time_data: trickle  # Live transactions

sync_mechanism:
  - CDC (Change Data Capture) voor transactional
  - Batch loads voor historical
  - API synchronization voor real-time

# ========== 7-STEP MIGRATION METHODOLOGY ==========
# Step 1: Discover en Assess
activities:
  - Inventory current data assets
  - Data profiling en quality assessment
  - Dependency mapping
  - Volume en growth analysis
deliverables:
  - Data inventory report
  - Risk assessment matrix
  - Migration feasibility study

# Step 2: Plan en Design
activities:
  - Migration strategy selection
  - Timeline en resource planning
  - Technical architecture design
  - Cost estimation en budgeting
deliverables:
  - Migration plan document
  - Technical design document
  - Project plan met milestones

# Step 3: Build en Configure
activities:
  - ETL/ELT pipeline development
  - Migration tools configuration
  - Test environment setup
  - Monitoring en logging setup
deliverables:
  - Working migration pipelines
  - Configuration management
  - Operational runbooks

# Step 4: Test en Validate
activities:
  - Unit testing van migration scripts
  - Integration testing
  - Performance testing
  - User acceptance testing
deliverables:
  - Test reports en results
  - Validation scripts
  - Sign-off documentation

# Step 5: Execute Dry Runs
activities:
  - Multiple rehearsal migrations
  - Performance benchmarking
  - Issue identification en resolution
  - Timeline validation
deliverables:
  - Dry run reports
  - Updated migration plan
  - Rollback procedure validation

# Step 6: Cutover en Go-Live
activities:
  - Final data synchronization
  - Production cutover execution
  - Real-time monitoring
  - Issue resolution
deliverables:
  - Migration completion report
  - Production system handover
  - Post-migration checklist

# Step 7: Post-Migration Activities
activities:
  - Performance monitoring
  - Data quality validation
  - User training en support
  - Old system decommissioning
deliverables:
  - Post-implementation review
  - Lessons learned document
  - Operational support documentation

# ========== MIGRATION PROJECT CHARTER ==========
project:
  name: "Enterprise Data Warehouse Migration to Snowflake"
  id: DM-2025-001
  sponsor: Chief Data Officer
  manager: Senior Data Architect
  start_date: 2025-01-15
  end_date: 2025-09-30
  budget: €1,850,000

business_case:
  current_state:
    - On-premises Teradata data warehouse
    - 50 TB data volume
    - 200+ ETL jobs
    - High maintenance costs (€650k/year)
    - Performance degradation
    - Limited scalability
  
  future_state:
    - Cloud-native Snowflake data platform
    - Elastic scalability
    - 60% cost reduction
    - Improved query performance
    - Better data governance
    - Enhanced analytics capabilities

scope:
  in_scope:
    - All production data warehouse tables
    - Historical data (5 years)
    - ETL pipelines migration
    - Reporting layer migration
    - User access and permissions
    - Data governance framework
  
  out_of_scope:
    - Legacy system data older than 5 years
    - Experimental datasets
    - User training (separate project)
    - Application changes (phase 2)

success_criteria:
  - data_completeness: 100% of production data migrated
  - data_accuracy: 99.99% data accuracy post-migration
  - downtime: Maximum 8 hours during cutover
  - performance: 50% improvement in query performance
  - cost: 40% reduction in operational costs
  - timeline: Project completion within 10% of schedule

stakeholders:
  - executive_sponsor: CDO (Decision authority, budget approval)
  - business_owners: Department heads (Requirements, acceptance)
  - technical_team: Data engineers, DBAs (Implementation)
  - end_users: Analysts, report consumers (Testing, feedback)
  - compliance: Legal, security teams (Regulatory compliance)

risks:
  - risk_1: 
    description: Data corruption during migration
    probability: Medium
    impact: High
    mitigation: Multiple validation checks, backups, rollback plan
  
  - risk_2:
    description: Extended downtime affecting business
    probability: Low
    impact: Critical
    mitigation: Phased migration, weekend cutover
  
  - risk_3:
    description: Budget overrun
    probability: Medium
    impact: Medium
    mitigation: Regular cost monitoring, contingency budget

communication_plan:
  - weekly_status: Project team, sponsors
  - biweekly_steering: Executive sponsors
  - monthly_business: Business stakeholders
  - adhoc_alerts: Critical issues only

approval:
  - prepared_by: Project Manager
  - date: 2025-01-10
  - approved_by: Executive Sponsor
  - date: 2025-01-12

# ========== DETAILED MIGRATION TIMELINE ==========
project: Enterprise Data Migration
total_duration: 9 months
phases:

phase: Discovery & Assessment
duration: 6 weeks
milestones:
  - Week 1-2: Current state analysis
    - Data inventory completion
    - Volume assessment
    - Dependency mapping
  
  - Week 3-4: Technical assessment
    - Source system analysis
    - Target platform evaluation
    - Compatibility assessment
  
  - Week 5-6: Planning
    - Migration strategy selection
    - High-level design
    - Resource planning

phase: Design & Architecture
duration: 8 weeks
milestones:
  - Week 7-8: Detailed design
    - Data model mapping
    - ETL design specification
    - Security design
  
  - Week 9-10: Tool selection
    - Migration tool evaluation
    - Proof of concept
    - Tool procurement
  
  - Week 11-12: Environment setup
    - Development environment
    - Test environment
    - Production preparation

phase: Development
duration: 10 weeks
milestones:
  - Week 13-16: ETL development
    - Core migration pipelines
    - Data validation scripts
    - Error handling framework
  
  - Week 17-18: Testing framework
    - Unit test development
    - Integration test setup
    - Performance test scripts
  
  - Week 19-20: Documentation
    - Technical documentation
    - Operational runbooks
    - User guides

phase: Testing
duration: 8 weeks
milestones:
  - Week 21-22: Unit testing
    - Component validation
    - Data accuracy testing
    - Performance baselining
  
  - Week 23-24: Integration testing
    - End-to-end testing
    - User acceptance testing
    - Performance testing
  
  - Week 25-26: Dry runs
    - Full rehearsal migrations
    - Issue resolution
    - Timeline validation

phase: Cutover & Go-Live
duration: 2 weeks
milestones:
  - Week 27: Final preparation
    - Data freeze communication
    - Final backups
    - Team briefing
  
  - Week 28: Migration execution
    - Day 1-2: Final data sync
    - Day 3: Cutover execution
    - Day 4-5: Post-migration validation
    - Day 6-7: Monitoring & support

phase: Post-Migration
duration: 4 weeks
milestones:
  - Week 29-30: Stabilization
    - Performance monitoring
    - Issue resolution
    - User support
  
  - Week 31-32: Optimization
    - Performance tuning
    - Cost optimization
    - Documentation finalization
  
  - Week 33-36: Decommissioning
    - Old system archiving
    - Infrastructure decommissioning
    - Project closure

# ========== DATA DISCOVERY CHECKLIST ==========
# 1. Data Inventory
inventory_items:
  - databases: List all source databases
  - schemas: Database schemas and owners
  - tables: Table names, row counts, sizes
  - views: Materialized and standard views
  - stored_procedures: Business logic in databases
  - data_flows: ETL processes and dependencies
  - users: Database users and permissions
  - backups: Backup schedules and retention

# 2. Data Profiling Metrics
profiling_metrics:
  volume_metrics:
    - Total data size (GB/TB/PB)
    - Table sizes and growth rates
    - Historical data volume trends
    - Archive data requirements
  
  quality_metrics:
    - Null value percentages
    - Data type consistency
    - Duplicate records
    - Referential integrity violations
    - Data format compliance
  
  sensitivity_metrics:
    - PII (Personally Identifiable Information)
    - PHI (Protected Health Information)
    - PCI (Payment Card Industry) data
    - GDPR compliance requirements
  
  dependency_metrics:
    - Foreign key relationships
    - View dependencies
    - Stored procedure dependencies
    - Application dependencies

# 3. Technical Assessment
technical_assessment:
  source_system:
    - Database version and edition
    - Character set and collation
    - Supported data types
    - Special features used
    - Performance characteristics
  
  target_system:
    - Compatibility analysis
    - Data type mapping requirements
    - Feature gap analysis
    - Performance expectations
  
  migration_complexity:
    - Simple (direct mapping)
    - Medium (transformation required)
    - Complex (business logic rewrite)
    - Very complex (re-architecture needed)

# ========== DATA PROFILING SCRIPT ==========
# Python data profiling script
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
from dataclasses import dataclass
from typing import Dict, List, Any

@dataclass
class DataProfile:
    table_name: str
    row_count: int
    total_size_mb: float
    columns: List[str]
    data_types: Dict[str, str]
    null_percentages: Dict[str, float]
    unique_counts: Dict[str, int]
    sample_data: Dict[str, Any]

class DataProfiler:
    def __init__(self, connection_string: str):
        self.engine = create_engine(connection_string)
        self.profiles = []
    
    def profile_table(self, table_name: str) -> DataProfile:
        # Get basic table info
        query = f"""
            SELECT COUNT(*) as row_count,
                   SUM(data_length) as total_size
            FROM information_schema.tables
            WHERE table_name = '{table_name}'
        """
        df_info = pd.read_sql(query, self.engine)
        
        # Get column information
        query = f"""
            SELECT column_name, data_type, is_nullable
            FROM information_schema.columns
            WHERE table_name = '{table_name}'
            ORDER BY ordinal_position
        """
        df_columns = pd.read_sql(query, self.engine)
        
        # Get sample data for analysis
        df_sample = pd.read_sql(f"SELECT * FROM {table_name} LIMIT 1000", 
                               self.engine)
        
        # Calculate profiling metrics
        profile = DataProfile(
            table_name=table_name,
            row_count=int(df_info.iloc[0]['row_count']),
            total_size_mb=float(df_info.iloc[0]['total_size']) / 1024 / 1024,
            columns=df_columns['column_name'].tolist(),
            data_types=dict(zip(df_columns['column_name'], 
                              df_columns['data_type'])),
            null_percentages={
                col: (self.df_sample[col].isnull().sum() / 
                      len(self.df_sample)) * 100
                for col in self.df_sample.columns
            },
            unique_counts={
                col: self.df_sample[col].nunique()
                for col in self.df_sample.columns
            },
            sample_data=df_sample.head(5).to_dict('records')
        )
        
        self.profiles.append(profile)
        return profile
    
    def generate_assessment_report(self) -> Dict:
        # Generate comprehensive assessment report
        report = {
            "summary": {
                "total_tables": len(self.profiles),
                "total_rows": sum(p.row_count for p in self.profiles),
                "total_size_gb": sum(p.total_size_mb for p in self.profiles) / 1024,
                "avg_null_percentage": np.mean([
                    np.mean(list(p.null_percentages.values()))
                    for p in self.profiles
                ])
            },
            "tables_by_size": sorted(
                self.profiles, 
                key=lambda x: x.total_size_mb, 
                reverse=True
            ),
            "data_quality_issues": self._identify_quality_issues(),
            "migration_complexity": self._assess_complexity(),
            "recommendations": self._generate_recommendations()
        }
        
        return report

# ========== ETL PIPELINE ARCHITECTURE ==========
# pipeline-config.yaml
pipeline:
  name: enterprise-data-migration
  version: 1.0
  type: ELT  # Extract-Load-Transform

source:
  database:
    type: oracle
    version: 19c
    connection:
      host: oracle-prod.company.com
      port: 1521
      service_name: PRODDB
    extraction_method: CDC  # Change Data Capture
    tables:
      - schema: HR
        tables: [employees, departments, jobs]
        extract_mode: full
      
      - schema: SALES
        tables: [customers, orders, order_items]
        extract_mode: incremental
        incremental_column: last_updated

target:
  database:
    type: snowflake
    account: company.west-europe.azure
    database: PROD_MIGRATION
    schema: RAW  # Landing zone for raw data
  storage:
    type: azure_blob
    container: raw-data-migration
    format: parquet

transformation:
  staging:
    schema: STG  # Staging area for transformations
    cleanup_rules:
      - remove_duplicates
      - standardize_formats
      - handle_nulls
  
  business_rules:
    - name: customer_data_enrichment
      description: Enrich customer data with geolocation
      sql: |
        UPDATE stg.customers c
        SET c.country_code = g.country_code,
            c.region = g.region
        FROM geography.dim_geography g
        WHERE c.postal_code = g.postal_code;
    
    - name: currency_conversion
      description: Convert all amounts to EUR
      sql: |
        UPDATE stg.orders o
        SET o.amount_eur = o.amount * cr.conversion_rate
        FROM finance.currency_rates cr
        WHERE o.currency = cr.currency_code
          AND o.order_date = cr.rate_date;

quality_checks:
  pre_load:
    - name: row_count_validation
      sql: SELECT COUNT(*) FROM source_table
      threshold: > 0
    
    - name: data_type_validation
      sql: |
        SELECT column_name, data_type
        FROM information_schema.columns
        WHERE table_name = 'source_table'
      expected: pre_defined_mapping
  
  post_load:
    - name: reconciliation
      sql: |
        SELECT 
          'source' as system,
          COUNT(*) as row_count,
          SUM(amount) as total_amount
        FROM source.orders
        UNION ALL
        SELECT 
          'target' as system,
          COUNT(*) as row_count,
          SUM(amount_eur) as total_amount
        FROM target.orders;
      tolerance: 0.01  # 1% tolerance

error_handling:
  retry_policy:
    max_retries: 3
    retry_delay: 5m
    backoff_factor: 2
  
  error_categories:
    - category: connectivity
      action: retry
      notification: team-alerts
    
    - category: data_quality
      action: quarantine
      notification: data-quality-team
    
    - category: transformation
      action: skip_and_log
      notification: development-team

monitoring:
  metrics:
    - rows_processed
    - processing_time
    - error_count
    - data_latency
  
  alerts:
    - condition: error_count > 10
      severity: critical
      notification: pagerduty
    
    - condition: processing_time > 2h
      severity: warning
      notification: slack-channel

# ========== MIGRATION TESTING STRATEGY ==========
testing_phases:

phase: Unit Testing
objective: Validate individual ETL components
scope:
  - Individual transformation rules
  - Data type conversions
  - Business logic implementation
test_cases:
  - test_case: date_format_conversion
    description: Convert Oracle date to ISO format
    input: '15-JAN-2025'
    expected_output: '2025-01-15'
    actual_output: [to be filled]
    status: [pass/fail]
  
  - test_case: null_handling
    description: Handle NULL values in required fields
    input: {"name": "John", "email": null}
    expected_output: Default value or error
    actual_output: [to be filled]
    status: [pass/fail]

phase: Integration Testing
objective: Validate end-to-end data flow
scope:
  - Full table migration
  - Data dependency chains
  - Referential integrity
test_cases:
  - test_case: customer_order_integration
    description: Validate customer-order relationship
    validation_query: |
      SELECT COUNT(*) as orphaned_orders
      FROM target.orders o
      LEFT JOIN target.customers c ON o.customer_id = c.id
      WHERE c.id IS NULL;
    expected_result: 0
    actual_result: [to be filled]
    status: [pass/fail]

phase: Volume Testing
objective: Validate performance at scale
scope:
  - Large table migration
  - Concurrent data loads
  - Memory and CPU utilization
test_cases:
  - test_case: 10m_rows_migration
    description: Migrate 10 million row table
    metrics:
      - Start time: [timestamp]
      - End time: [timestamp]
      - Duration: [hh:mm:ss]
      - Rows per second: [number]
      - Peak memory: [GB]
      - CPU utilization: [%]
    success_criteria:
      - Duration < 2 hours
      - No data loss
      - Memory < 16GB

phase: Reconciliation Testing
objective: Validate data completeness and accuracy
scope:
  - Row count comparison
  - Data value comparison
  - Aggregate validation
test_cases:
  - test_case: financial_reconciliation
    description: Validate financial data accuracy
    validation_queries:
      - Source sum: SELECT SUM(amount) FROM source.invoices
      - Target sum: SELECT SUM(amount) FROM target.invoices
    tolerance: ±0.01%
    actual_difference: [percentage]
    status: [pass/fail]

phase: User Acceptance Testing (UAT)
objective: Validate business requirements
scope:
  - Business process validation
  - Report accuracy
  - Application functionality
test_cases:
  - test_case: monthly_sales_report
    description: Compare sales reports pre/post migration
    pre_migration_report: [file/reference]
    post_migration_report: [file/reference]
    differences: [list of differences]
    business_sign_off: [name, date, approval]

phase: Performance Testing
objective: Validate system performance
scope:
  - Query performance
  - Concurrent user load
  - System response times
test_cases:
  - test_case: critical_query_performance
    description: Compare query execution times
    query: SELECT * FROM sales WHERE date >= '2025-01-01'
    source_execution_time: [seconds]
    target_execution_time: [seconds]
    performance_improvement: [percentage]
    acceptance_criteria: Target ≤ 150% of source time

phase: Disaster Recovery Testing
objective: Validate rollback procedures
scope:
  - Rollback script execution
  - Data restoration
  - System recovery
test_cases:
  - test_case: full_rollback_simulation
    description: Simulate failed migration rollback
    steps:
      1. Take pre-migration backup
      2. Execute migration
      3. Simulate failure
      4. Execute rollback
      5. Verify system state
    rollback_duration: [hh:mm:ss]
    data_integrity: [verified/not verified]
    success_criteria: Complete rollback within 4 hours

# ========== CUTOVER RUNBOOK TEMPLATE ==========
# Project: ERP System Migration
# Cutover Window: Weekend, 48 hours
# Date: 2025-03-15 18:00 to 2025-03-17 18:00

cutover_team:
  - cutover_manager: [Name, Phone, Role]
  - database_lead: [Name, Phone, Role]
  - application_lead: [Name, Phone, Role]
  - network_lead: [Name, Phone, Role]
  - business_coordinator: [Name, Phone, Role]

communication_plan:
  - pre_cutover: All stakeholders informed 1 week before
  - during_cutover: Hourly updates via Slack/Teams
  - post_cutover: Go/No-Go decision meeting every 4 hours
  - emergency: Direct phone calls for critical issues

cutover_timeline:

# T-48 hours: Final Preparations
time: 2025-03-13 18:00
activity: Final system backups
owner: Database Team
duration: 4 hours
verification: Backup completion confirmed
success_criteria: All backups completed successfully
rollback_point: Backup archives created

# T-24 hours: Application Freeze
time: 2025-03-14 18:00
activity: Application freeze announcement
owner: Business Coordinator
duration: 1 hour
verification: All users logged out
success_criteria: No active user sessions
rollback_point: Pre-freeze state

# T-12 hours: Final Data Extract
time: 2025-03-15 06:00
activity: Final incremental data extract
owner: ETL Team
duration: 6 hours
verification: Data extract completion
success_criteria: All incremental changes captured
rollback_point: Incremental backup available

# T-6 hours: Data Validation
time: 2025-03-15 12:00
activity: Pre-cutover data validation
owner: QA Team
duration: 4 hours
verification: Validation reports reviewed
success_criteria: 99.9% data accuracy
rollback_point: Decision point before cutover

# T-0: Cutover Start
time: 2025-03-15 18:00
activity: Begin cutover execution
owner: Cutover Manager
duration: Instant
verification: All teams ready
success_criteria: Go decision from steering committee

# T+2 hours: Database Migration
time: 2025-03-15 20:00
activity: Database migration execution
owner: Database Team
duration: 8 hours
verification: Migration completion status
success_criteria: All databases migrated
rollback_point: Database restore available

# T+10 hours: Application Deployment
time: 2025-03-16 04:00
activity: New application deployment
owner: Application Team
duration: 4 hours
verification: Application startup logs
success_criteria: All services running
rollback_point: Application rollback scripts ready

# T+14 hours: Integration Testing
time: 2025-03-16 08:00
activity: Integration test execution
owner: Testing Team
duration: 6 hours
verification: Test results reviewed
success_criteria: All critical tests pass
rollback_point: Decision point before business testing

# T+20 hours: Business Verification
time: 2025-03-16 14:00
activity: Business user verification
owner: Business Coordinator
duration: 4 hours
verification: Key business processes tested
success_criteria: Business sign-off obtained
rollback_point: Final decision point

# T+24 hours: Go-Live Decision
time: 2025-03-16 18:00
activity: Go/No-Go decision meeting
owner: Cutover Manager
duration: 1 hour
verification: All criteria met
success_criteria: Formal Go decision
rollback_point: Last chance to rollback

# T+25 hours: Production Release
time: 2025-03-16 19:00
activity: Production system release
owner: Application Team
duration: 1 hour
verification: User access restored
success_criteria: Users can access system
rollback_point: Post-release rollback possible but complex

# T+26 to T+48 hours: Monitoring
time: 2025-03-16 20:00 to 2025-03-17 18:00
activity: Intensive monitoring period
owner: Operations Team
duration: 22 hours
verification: System metrics monitored
success_criteria: Stable system operation
rollback_point: Emergency rollback procedures defined

rollback_procedures:
  - level: 1 (Simple rollback)
    trigger: Failure during initial migration steps
    procedure: Restore from pre-cutover backups
    estimated_time: 2 hours
  
  - level: 2 (Complex rollback)
    trigger: Failure after partial cutover
    procedure: Restore and data synchronization
    estimated_time: 6 hours
  
  - level: 3 (Emergency rollback)
    trigger: Critical failure post go-live
    procedure: Full system restoration
    estimated_time: 12 hours

success_criteria:
  - technical:
    - All databases migrated successfully
    - All applications running without errors
    - Performance within acceptable limits
    - Monitoring systems operational
  
  - business:
    - Key business processes functioning
    - Users can access and use the system
    - Reports generating correctly
    - Data integrity maintained
  
  - operational:
    - Support teams trained and ready
    - Documentation updated
    - Rollback procedures validated
    - Lessons learned documented

# ========== POST-MIGRATION CHECKLIST ==========

# Week 1: Immediate Post-Migration
day_1_activities:
  - [ ] Monitor system performance 24/7
  - [ ] Track error rates and system logs
  - [ ] Validate critical business processes
  - [ ] Address immediate user issues
  - [ ] Update stakeholders on system status

day_2_3_activities:
  - [ ] Conduct comprehensive data validation
  - [ ] Verify all integration points
  - [ ] Test backup and restore procedures
  - [ ] Review performance metrics
  - [ ] Address any system issues

day_4_7_activities:
  - [ ] Complete user acceptance testing
  - [ ] Gather user feedback
  - [ ] Optimize system performance
  - [ ] Update documentation
  - [ ] Conduct post-mortem review

# Week 2-4: Stabilization Phase
performance_monitoring:
  - [ ] Daily performance review meetings
  - [ ] Query performance optimization
  - [ ] Resource utilization analysis
  - [ ] Cost monitoring and optimization
  - [ ] SLA compliance tracking

user_support:
  - [ ] Dedicated support desk for migration issues
  - [ ] User training sessions
  - [ ] Knowledge base updates
  - [ ] FAQ documentation
  - [ ] User satisfaction surveys

data_validation:
  - [ ] Weekly data quality checks
  - [ ] Reconciliation with old system (read-only)
  - [ ] Business report validation
  - [ ] Audit trail verification
  - [ ] Compliance validation

# Month 2-3: Optimization Phase
system_optimization:
  - [ ] Performance tuning based on usage patterns
  - [ ] Index optimization
  - [ ] Query optimization
  - [ ] Storage optimization
  - [ ] Cost optimization

process_improvement:
  - [ ] Update operational procedures
  - [ ] Automate manual processes
  - [ ] Enhance monitoring and alerting
  - [ ] Implement additional security measures
  - [ ] Disaster recovery testing

# Month 4-6: Decommissioning Phase
decommissioning_preparation:
  - [ ] Verify no active dependencies on old system
  - [ ] Archive historical data from old system
  - [ ] Backup final state of old system
  - [ ] Update all documentation references
  - [ ] Obtain formal decommissioning approval

decommissioning_execution:
  - [ ] Disable user access to old system
  - [ ] Shutdown applications and services
  - [ ] Decommission servers and storage
  - [ ] Update network configurations
  - [ ] Remove system from monitoring

post_decommissioning:
  - [ ] Verify all data archived successfully
  - [ ] Update asset management systems
  - [ ] Complete financial closure
  - [ ] Document lessons learned
  - [ ] Celebrate project success

# ========== POST-MIGRATION METRICS DASHBOARD ==========
key_metrics:
  performance_metrics:
    - Average query response time: [current] vs [baseline]
    - System availability: [percentage]
    - Concurrent users supported: [number]
    - Data processing throughput: [GB/hour]
  
  business_metrics:
    - User satisfaction score: [rating]
    - Report generation time: [improvement %]
    - Business process efficiency: [improvement %]
    - Cost savings: [€ per month]
  
  data_quality_metrics:
    - Data accuracy: [percentage]
    - Data completeness: [percentage]
    - Data timeliness: [percentage]
    - Error rates: [per 1000 transactions]

# ========== LESSONS LEARNED TEMPLATE ==========
project: [Project Name]
date: [Review Date]
participants: [Team Members]

what_went_well:
  - [List successful aspects]
  - [Best practices identified]
  - [Tools/techniques that worked well]
  - [Team collaboration successes]

what_could_be_improved:
  - [Areas for improvement]
  - [Challenges faced]
  - [Process inefficiencies]
  - [Communication gaps]

recommendations_for_future_projects:
  - [Process improvements]
  - [Tool recommendations]
  - [Team structure suggestions]
  - [Risk management improvements]

quantitative_results:
  - Actual vs planned timeline: [difference]
  - Actual vs planned budget: [difference]
  - Data migration accuracy: [percentage]
  - System performance improvement: [percentage]

action_items:
  - [Follow-up actions with owners]
  - [Process updates required]
  - [Documentation updates]
  - [Training needs identified]