Rapid Rebuild Hackathon 2025: 72 Hours to Fix What Others Spent Years Breaking

Unlike traditional hackathons that start with blank repositories and endless possibilities, Rapid Rebuild began with constraints. Teams received legacy codebases spanning different eras of software development: a PHP e-commerce platform from 2015, a Java enterprise application with XML configuration files, a Ruby on Rails CRM system with deprecated gem dependencies, and a .NET Framework inventory management tool tied to SQL Server 2012.

The mission was to achieve surgical precision disguised as creative chaos: preserve core functionality while modernizing architecture, improve performance without disrupting existing workflows, and implement contemporary security standards while maintaining backward compatibility. It was software archaeology meets rapid prototyping, evaluated by professionals who understand that successful legacy transformation requires proving improvements without introducing regressions. This challenge demands deep expertise in quality assurance methodologies and enterprise testing practices.

The Quality Assurance Perspective on Legacy Transformation

When established financial institutions modernize core systems, the stakes extend beyond user experience into regulatory compliance, data integrity, and operational continuity. Yulia Drogunova, Senior QA Engineer at Raiffeisen Bank with over eight years of industry experience, brought this critical perspective to evaluating Rapid Rebuild projects. Her expertise in building effective testing processes for major financial institutions informed her understanding of what teams needed to prove beyond basic functionality.

“Legacy system modernization isn’t just about making things look newer,” Drogunova explained during the evaluation process. “In banking, you’re dealing with systems that handle millions of transactions daily. Any regression, any data integrity issue, any performance degradation can have immediate financial consequences.”

Her experience at VTB Bank, Luxoft, and Lineate, combined with her current role implementing automated testing processes that reduce time to market while maintaining quality standards, provided crucial insight into the complexities teams faced. The challenge wasn’t simply rewriting old code in new frameworks—it was maintaining functional parity while implementing modern practices that could withstand enterprise-grade usage patterns and regulatory scrutiny.

The winning projects demonstrated sophisticated understanding of quality assurance principles that extend beyond basic functionality testing. They implemented comprehensive validation strategies that addressed data migration integrity, API compatibility, performance regression prevention, and security vulnerability assessment—concerns that reflect real-world modernization challenges in enterprise environments.

Automated Testing as Migration Safety Net

The most successful teams approached legacy transformation with automated testing strategies that mirrored enterprise modernization practices. Drogunova’s expertise in writing automated REST API tests and implementing UI test coverage for mobile banking applications on both Android and iOS provided essential perspective on these approaches.

“What impressed me most was seeing teams implement comprehensive test automation from the start,” she noted. “In my experience integrating automated tests into CI/CD processes, I’ve seen how this approach increases development speed while reducing defects. The teams that succeeded here understood this principle.”

Rather than treating tests as post-development additions, winning teams implemented testing frameworks that validated both original functionality and new feature implementation throughout the rebuild process. This approach reflected Drogunova’s understanding that legacy system modernization requires continuous validation to prevent regression errors that could compromise business operations.

The complexity of testing legacy system transformations often surprises developers accustomed to greenfield projects. Legacy systems frequently contain undocumented business logic, edge cases that evolved over years of production usage, and integration patterns that reflect historical technical constraints rather than optimal design decisions.

First Place: CoreSync – Enterprise Resource Planning Revolution

CoreSync emerged as the standout solution by transforming a cumbersome Java ERP system into a modern microservices architecture while maintaining full backward compatibility with existing data structures and third-party integrations.

The original system suffered from typical enterprise legacy problems: monolithic architecture that made feature updates risky, database queries that degraded under load, and user interfaces that reflected early 2010s web development practices. CoreSync’s approach demonstrated understanding that enterprise modernization succeeds through incremental transformation rather than complete replacement.

Their technical strategy employed a strangler fig pattern, gradually replacing legacy components while ensuring continuous system operation:

// Legacy integration adapter maintaining backward compatibility
interface LegacySystemAdapter { validateDataMigration(legacyData: any, modernData: any): ValidationResult; maintainApiCompatibility(legacyEndpoint: string, modernEndpoint: string): boolean; monitorPerformanceRegression(): PerformanceMetrics;
}
class ERPMigrationService implements LegacySystemAdapter { private readonly performanceBaseline: PerformanceMetrics; private readonly regressionThreshold = 0.15; // 15% performance degradation threshold constructor(baseline: PerformanceMetrics) { this.performanceBaseline = baseline; } /** * Validates that migrated data maintains referential integrity * and business rule compliance from legacy system */ validateDataMigration(legacyData: any, modernData: any): ValidationResult { const validationResults: ValidationResult = { integrityChecks: [], businessRuleValidation: [], performanceMetrics: {} }; // Verify referential integrity across related entities const integrityCheck = this.validateReferentialIntegrity(legacyData, modernData); validationResults.integrityChecks.push(integrityCheck); // Validate business rules are preserved const businessRuleCheck = this.validateBusinessRules(legacyData, modernData); validationResults.businessRuleValidation.push(businessRuleCheck); // Monitor performance regression during migration const performanceCheck = this.monitorPerformanceRegression(); validationResults.performanceMetrics = performanceCheck; return validationResults; } private validateReferentialIntegrity(legacy: any, modern: any): IntegrityResult { // Implementation would verify that all foreign key relationships // are preserved during migration process return { status: 'passed', details: 'All referential integrity constraints validated' }; } private validateBusinessRules(legacy: any, modern: any): BusinessRuleResult { // Implementation would verify business logic consistency return { status: 'passed', rulesValidated: ['inventory_constraints', 'financial_calculations', 'user_permissions'] }; } monitorPerformanceRegression(): PerformanceMetrics { // Implementation would track response times, query performance, // and resource utilization compared to baseline return { responseTime: 150, // milliseconds queryPerformance: 0.95, // relative to baseline memoryUsage: 0.88 // relative to baseline }; }
}

This validation approach ensured that modernization efforts maintained operational integrity while introducing performance improvements and enhanced user experience. The team understood that enterprise system migration requires proof that new implementations preserve critical business functionality while delivering measurable improvements.

Comprehensive Testing Strategy

CoreSync’s testing approach reflected enterprise-grade quality assurance practices, implementing multiple validation layers that addressed different aspects of legacy system transformation:

// Multi-layer testing framework for ERP system modernization
class ERPTestingSuite { private readonly apiCompatibilityTests: ApiTestSuite; private readonly dataIntegrityValidator: DataValidator; private readonly performanceMonitor: PerformanceTestSuite; private readonly securityAuditor: SecurityTestSuite; constructor() { this.apiCompatibilityTests = new ApiTestSuite(); this.dataIntegrityValidator = new DataValidator(); this.performanceMonitor = new PerformanceTestSuite(); this.securityAuditor = new SecurityTestSuite(); } /** * Executes comprehensive test suite covering functional, * performance, and security validation requirements */ async executeFullValidation(): Promise<TestResults> { const results = { apiCompatibility: await this.validateApiCompatibility(), dataIntegrity: await this.validateDataMigration(), performanceMetrics: await this.validatePerformanceImprovement(), securityCompliance: await this.validateSecurityStandards() }; return this.generateTestReport(results); } private async validateApiCompatibility(): Promise<ApiTestResults> { // Test that all existing API endpoints maintain compatibility const legacyEndpoints = await this.discoverLegacyApiEndpoints(); const compatibilityResults = []; for (const endpoint of legacyEndpoints) { const testResult = await this.apiCompatibilityTests.validateEndpoint(endpoint); compatibilityResults.push(testResult); } return { totalEndpoints: legacyEndpoints.length, passedTests: compatibilityResults.filter(r => r.status === 'passed').length, failedTests: compatibilityResults.filter(r => r.status === 'failed'), compatibilityScore: this.calculateCompatibilityScore(compatibilityResults) }; } private async validateDataMigration(): Promise<DataValidationResults> { // Verify data consistency between legacy and modern systems return await this.dataIntegrityValidator.runComprehensiveValidation(); } private async validatePerformanceImprovement(): Promise<PerformanceResults> { // Compare performance metrics against legacy baseline return await this.performanceMonitor.measureAgainstBaseline(); } private async validateSecurityStandards(): Promise<SecurityResults> { // Audit security improvements and compliance requirements return await this.securityAuditor.auditModernizedSystem(); }
}

This multi-layered approach addressed the complexity of enterprise system validation, where testing must verify not only functional correctness but also performance improvements, security enhancements, and regulatory compliance. The framework demonstrated understanding that enterprise modernization projects require proof that new systems meet or exceed existing operational standards.

The testing strategy particularly impressed evaluators because it addressed real-world concerns that enterprise development teams face during system modernization: How do you prove that a modernized system won’t introduce regressions? How do you validate that performance improvements are genuine and sustainable? How do you ensure that security enhancements don’t compromise existing integrations?

Second Place: DataFlow – Financial Data Pipeline Modernization

DataFlow tackled the challenge of modernizing a legacy financial data processing system that handled transaction analysis and reporting for compliance requirements. The original system, built with outdated technologies and manual processes, suffered from performance bottlenecks and regulatory compliance risks.

Their solution demonstrated sophisticated understanding of financial data processing requirements, implementing modern stream processing architecture while maintaining audit trails and regulatory compliance features:

# Financial data processing with comprehensive audit trail
import asyncio
from typing import Dict, List, Optional
from dataclasses import dataclass
from datetime import datetime
import logging
@dataclass
class TransactionRecord: transaction_id: str amount: float currency: str timestamp: datetime account_id: str transaction_type: str compliance_flags: List[str]
class FinancialDataProcessor: def __init__(self, compliance_validator, audit_logger): self.compliance_validator = compliance_validator self.audit_logger = audit_logger self.processing_metrics = {} async def process_transaction_batch( self, transactions: List[TransactionRecord] ) -> Dict[str, any]: """ Processes financial transactions with comprehensive validation, audit logging, and performance monitoring """ processing_start = datetime.now() # Pre-processing validation validation_results = await self.validate_transaction_batch(transactions) if not validation_results['is_valid']: await self.audit_logger.log_validation_failure( transactions, validation_results['errors'] ) return {'status': 'failed', 'errors': validation_results['errors']} # Process transactions with monitoring processed_transactions = [] failed_transactions = [] for transaction in transactions: try: processed_transaction = await self.process_single_transaction(transaction) processed_transactions.append(processed_transaction) # Log successful processing for audit trail await self.audit_logger.log_successful_processing(transaction) except Exception as e: failed_transactions.append({ 'transaction': transaction, 'error': str(e) }) await self.audit_logger.log_processing_error(transaction, e) # Record performance metrics processing_duration = (datetime.now() - processing_start).total_seconds() self.update_performance_metrics(len(transactions), processing_duration) return { 'status': 'completed', 'processed_count': len(processed_transactions), 'failed_count': len(failed_transactions), 'processing_time': processing_duration, 'failed_transactions': failed_transactions } async def validate_transaction_batch( self, transactions: List[TransactionRecord] ) -> Dict[str, any]: """ Validates transaction batch against compliance requirements and business rules """ validation_errors = [] for transaction in transactions: # Validate compliance requirements compliance_result = await self.compliance_validator.validate_transaction( transaction ) if not compliance_result['is_compliant']: validation_errors.extend(compliance_result['violations']) # Validate business rules business_rule_result = self.validate_business_rules(transaction) if not business_rule_result['is_valid']: validation_errors.extend(business_rule_result['violations']) return { 'is_valid': len(validation_errors) == 0, 'errors': validation_errors } def validate_business_rules(self, transaction: TransactionRecord) -> Dict[str, any]: """ Validates transaction against business rules and limits """ violations = [] # Example business rule validations if transaction.amount <= 0: violations.append(f"Invalid amount: {transaction.amount}") if transaction.amount > 1000000: # Large transaction threshold violations.append(f"Transaction exceeds limit: {transaction.amount}") return { 'is_valid': len(violations) == 0, 'violations': violations } def update_performance_metrics(self, transaction_count: int, duration: float): """ Updates processing performance metrics for monitoring """ throughput = transaction_count / duration if duration > 0 else 0 self.processing_metrics.update({ 'last_batch_size': transaction_count, 'last_processing_duration': duration, 'last_throughput': throughput, 'timestamp': datetime.now().isoformat() })

DataFlow’s architecture demonstrated understanding that financial system modernization requires maintaining rigorous audit trails while improving processing performance. Their solution implemented modern stream processing capabilities while ensuring that every transaction could be traced and validated according to regulatory requirements.

Quality Assurance in Financial Data Processing

The financial data processing domain presents unique quality assurance challenges that extend beyond typical software testing. Drogunova’s experience building testing processes for Raiffeisen Bank provided crucial insight into these requirements. “Financial systems require validation strategies that address both functional correctness and regulatory adherence,” she explained. “You’re not just testing if the code works—you’re testing if it works correctly under regulatory scrutiny.”

Her background implementing automated tests that reduced defects while maintaining compliance standards informed her evaluation of how teams approached these challenges. Regulatory compliance, audit trail integrity, and real-time processing accuracy require validation strategies that address both functional correctness and regulatory adherence.

DataFlow’s testing approach reflected understanding of these requirements, implementing validation frameworks that verified not only processing accuracy but also compliance with financial regulations and audit requirements:

# Comprehensive testing framework for financial data processing
class FinancialSystemTestSuite: def __init__(self): self.compliance_validator = ComplianceValidator() self.performance_tester = PerformanceTester() self.audit_verifier = AuditTrailVerifier() async def execute_regulatory_compliance_tests(self) -> ComplianceTestResults: """ Executes comprehensive compliance testing covering regulatory requirements and audit trail validation """ test_results = { 'aml_compliance': await self.test_anti_money_laundering_detection(), 'kyc_validation': await self.test_know_your_customer_procedures(), 'audit_trail_integrity': await self.test_audit_trail_completeness(), 'data_retention_compliance': await self.test_data_retention_policies(), 'reporting_accuracy': await self.test_regulatory_reporting() } return self.generate_compliance_report(test_results) async def test_anti_money_laundering_detection(self) -> TestResult: """ Tests AML detection algorithms with known suspicious patterns """ suspicious_patterns = self.load_suspicious_transaction_patterns() detection_results = [] for pattern in suspicious_patterns: detection_result = await self.compliance_validator.detect_suspicious_activity( pattern ) detection_results.append({ 'pattern': pattern, 'detected': detection_result['is_suspicious'], 'confidence': detection_result['confidence_score'] }) accuracy = len([r for r in detection_results if r['detected']]) / len(detection_results) return { 'test_name': 'AML Detection', 'accuracy': accuracy, 'passed': accuracy >= 0.95, # 95% detection threshold 'details': detection_results } async def test_audit_trail_completeness(self) -> TestResult: """ Verifies that audit trails capture all required information for regulatory compliance """ test_transactions = self.generate_test_transaction_set() audit_results = [] for transaction in test_transactions: audit_record = await self.audit_verifier.verify_audit_completeness( transaction ) audit_results.append(audit_record) complete_audits = len([r for r in audit_results if r['is_complete']]) completeness_rate = complete_audits / len(audit_results) return { 'test_name': 'Audit Trail Completeness', 'completeness_rate': completeness_rate, 'passed': completeness_rate == 1.0, # 100% completeness required 'missing_elements': [r['missing_fields'] for r in audit_results if not r['is_complete']] }

This testing framework addressed the reality that financial system modernization must prove regulatory compliance alongside functional improvements. The comprehensive validation approach demonstrated understanding that quality assurance in financial systems extends beyond traditional software testing into regulatory adherence and audit trail integrity.

The framework particularly addressed concerns that arise when modernizing financial systems: How do you ensure that algorithmic improvements don’t compromise regulatory compliance? How do you validate that audit trails meet evolving regulatory requirements? How do you prove that performance optimizations don’t introduce data integrity risks?

Third Place: ConnectSync – Healthcare Integration Platform

ConnectSync addressed the challenge of modernizing a healthcare data integration system that connected multiple hospital systems with insurance providers and regulatory reporting requirements. The legacy system struggled with data format inconsistencies, performance bottlenecks during peak usage, and compliance validation delays.

Their solution demonstrated understanding of healthcare system integration complexities, implementing modern API architecture while maintaining HIPAA compliance and data integrity requirements:

// Healthcare data integration with comprehensive validation
interface HealthcareDataRecord { patientId: string; providerId: string; serviceDate: Date; diagnosisCodes: string[]; procedureCodes: string[]; complianceStatus: 'validated' | 'pending' | 'failed';
}
class HealthcareIntegrationPlatform { private readonly hipaaValidator: HIPAAComplianceValidator; private readonly dataIntegrityChecker: DataIntegrityChecker; private readonly performanceMonitor: PerformanceMonitor; constructor() { this.hipaaValidator = new HIPAAComplianceValidator(); this.dataIntegrityChecker = new DataIntegrityChecker(); this.performanceMonitor = new PerformanceMonitor(); } /** * Processes healthcare data with comprehensive validation * and compliance checking */ async processHealthcareData( records: HealthcareDataRecord[] ): Promise<ProcessingResult> { const processingStart = performance.now(); // Pre-processing validation const validationResult = await this.validateDataCompliance(records); if (!validationResult.isValid) { return { status: 'failed', errors: validationResult.errors, processedRecords: 0 }; } // Process records with monitoring const processedRecords: HealthcareDataRecord[] = []; const failedRecords: Array<{record: HealthcareDataRecord, error: string}> = []; for (const record of records) { try { const processedRecord = await this.processIndividualRecord(record); processedRecords.push(processedRecord); } catch (error) { failedRecords.push({ record, error: error.message }); } } // Monitor performance metrics const processingTime = performance.now() - processingStart; await this.performanceMonitor.recordMetrics({ recordCount: records.length, processingTime, successRate: processedRecords.length / records.length }); return { status: 'completed', processedRecords: processedRecords.length, failedRecords: failedRecords.length, processingTime, errors: failedRecords.map(f => f.error) }; } private async validateDataCompliance( records: HealthcareDataRecord[] ): Promise<ValidationResult> { const errors: string[] = []; for (const record of records) { // HIPAA compliance validation const hipaaResult = await this.hipaaValidator.validateRecord(record); if (!hipaaResult.isCompliant) { errors.push(...hipaaResult.violations); } // Data integrity validation const integrityResult = this.dataIntegrityChecker.validateRecord(record); if (!integrityResult.isValid) { errors.push(...integrityResult.errors); } } return { isValid: errors.length === 0, errors }; }
}

ConnectSync’s approach demonstrated sophisticated understanding of healthcare system requirements, where data processing must balance performance optimization with strict compliance and security requirements. Their architecture enabled real-time data integration while maintaining comprehensive audit trails and regulatory compliance.

Quality Assurance in Healthcare System Integration

Healthcare system modernization presents unique quality assurance challenges that combine technical complexity with regulatory compliance requirements. Drogunova’s leadership experience, which often positions her as a mentor for junior team members, provided valuable perspective on how teams approached these multifaceted challenges.

“Healthcare integration requires the same systematic approach we use in banking,” she observed. “You need comprehensive validation frameworks that prove your system improvements don’t compromise patient data security or regulatory compliance. The teams that succeeded understood this from the beginning.”

The integration of multiple healthcare systems requires validation strategies that ensure data accuracy, patient privacy protection, and regulatory adherence across diverse system architectures. ConnectSync’s testing approach reflected Drogunova’s understanding of these requirements, implementing validation frameworks that addressed both technical functionality and healthcare-specific compliance needs.

The complexity of healthcare system integration testing often involves validating data consistency across systems with different data formats, ensuring that patient privacy protections remain intact during data transfers, and verifying that integration performance meets the real-time requirements of healthcare operations.

Innovation Patterns in Legacy System Modernization

The diversity of approaches across the Rapid Rebuild submissions revealed consistent patterns in how teams successfully navigated legacy modernization challenges. These patterns reflect broader industry trends toward systematic approaches to enterprise system transformation.

Incremental Migration Strategies: The most successful projects implemented gradual transformation approaches rather than complete system replacement. Teams that succeeded understood that enterprise systems require operational continuity during modernization, implementing strategies that preserved existing functionality while introducing modern capabilities.

Comprehensive Validation Frameworks: Winning teams consistently implemented multi-layered testing strategies that addressed functional, performance, and compliance requirements. Rather than treating testing as post-development validation, they integrated continuous testing throughout the modernization process.

Performance Monitoring Integration: Successful projects included performance monitoring capabilities that validated improvements against legacy system baselines. This approach demonstrated understanding that modernization projects must prove measurable improvements while maintaining operational reliability.

Technical Excellence Under Migration Pressure

The 72-hour timeline compressed typical enterprise migration timelines while requiring teams to address the full complexity of legacy system transformation. This compression revealed which architectural approaches could handle rapid modernization while maintaining production-quality standards.

The winning projects demonstrated understanding that legacy system modernization requires different technical approaches than greenfield development. They successfully balanced the need for rapid prototyping with the quality standards required for enterprise deployment, implementing validation strategies that ensured modernization efforts enhanced rather than compromised existing system capabilities.

Their success reflected understanding that enterprise modernization projects must prove value through measurable improvements while maintaining operational integrity. The most effective teams implemented comprehensive validation strategies that addressed not only functional requirements but also performance improvements, security enhancements, and regulatory compliance.

Quality Assurance Methodology for Rapid Modernization

The evaluation of Rapid Rebuild projects required assessment frameworks that could measure both technical innovation and practical viability for enterprise deployment. Drogunova’s contributions to the tech industry through expertise in both manual and automated testing, system optimization, and thought leadership shaped the judging criteria to reflect understanding that legacy modernization success depends on proving improvement across multiple dimensions rather than demonstrating individual technical capabilities.

“The most impressive projects weren’t just technically sophisticated,” Drogunova noted. “They demonstrated systematic thinking about quality assurance that would actually work in production environments. That’s what separates hackathon demos from real enterprise solutions.”

Her experience optimizing systems and reducing defects through comprehensive testing strategies informed the evaluation framework. Successful projects demonstrated comprehensive testing strategies that validated modernization benefits while ensuring operational continuity, reflecting Drogunova’s understanding that enterprise system transformation requires proof that new implementations deliver measurable improvements without introducing operational risks.

The testing methodologies implemented by winning teams addressed real-world concerns that enterprise development teams face during system modernization: maintaining data integrity during migration, preserving API compatibility for existing integrations, and ensuring that performance improvements are genuine and sustainable under production loads.

Lessons for Enterprise Modernization

The Rapid Rebuild results provided insights into effective strategies for enterprise system transformation that extend beyond hackathon contexts. The most successful approaches balanced rapid development with comprehensive validation, demonstrating that effective modernization requires understanding both technical innovation and enterprise operational requirements.

The winning teams implemented practices that mirror emerging industry trends toward systematic modernization methodologies. Their success reflected understanding that enterprise system transformation succeeds through careful validation of improvements rather than assumption that newer technologies automatically provide better solutions.

The emphasis on comprehensive testing and validation demonstrated understanding that enterprise modernization projects must prove value through measurable outcomes. Teams that succeeded implemented validation strategies that addressed both technical functionality and business impact, ensuring that modernization efforts delivered genuine operational improvements.

Future Implications for Legacy System Transformation

The Rapid Rebuild approach addresses growing industry need for systematic methodologies to modernize aging enterprise systems. As organizations confront technical debt accumulated over decades of development, the ability to implement effective modernization strategies becomes critical for competitive advantage.

The hackathon results demonstrated that successful legacy transformation requires understanding both modern development practices and enterprise operational requirements. The winning projects succeeded by implementing comprehensive validation strategies that proved modernization benefits while ensuring operational continuity.

Looking forward, the approaches demonstrated in Rapid Rebuild suggest methodologies for organizations seeking to modernize complex enterprise systems. Rather than pursuing complete replacement strategies, the most effective approaches implement incremental transformation with comprehensive validation at each step.

Conclusion: The Discipline of Systematic Modernization

The Rapid Rebuild Hackathon 2025 demonstrated that effective legacy system transformation requires more than technical innovation—it demands systematic approaches to validation, testing, and quality assurance that ensure modernization efforts deliver genuine operational improvements.

The winning projects succeeded not through dramatic technological shifts, but through careful implementation of validation strategies that proved modernization benefits while maintaining operational integrity. They demonstrated understanding that enterprise system transformation requires balancing innovation with reliability, ensuring that new implementations enhance rather than compromise existing capabilities.

Most importantly, the hackathon revealed that systematic approaches to legacy modernization aren’t just enterprise necessities—they’re methodologies for building reliable, scalable systems that can adapt to changing requirements while maintaining operational excellence. The success of comprehensive validation approaches suggests that quality assurance thinking provides essential frameworks for managing complex system transformations.

The Rapid Rebuild results point toward a future where systematic modernization methodologies enable organizations to transform aging systems without compromising operational reliability, proving that innovation and stability aren’t opposing forces but complementary aspects of effective enterprise system development.