Version: 2.0.5
Enterprise Readiness: ❌ 15-20% (Not Production Ready)
Critical Gaps: 80-85% of enterprise features missing
- Basic pandas DataFrame lineage tracking
- Column-level dependency mapping
- Simple visualization capabilities
- Performance optimizations for small datasets
- Basic documentation and examples
- No security framework (empty security directories)
- No authentication/authorization
- No scalability architecture
- No monitoring/alerting system
- No compliance features
- No enterprise integrations
- No deployment automation
Priority: 🔴 Critical - Must Have
├── authentication/
│ ├── oauth2_provider.py
│ ├── saml_integration.py
│ ├── ldap_connector.py
│ └── jwt_manager.py
├── authorization/
│ ├── rbac_engine.py
│ ├── permission_manager.py
│ ├── policy_engine.py
│ └── access_control.py
├── encryption/
│ ├── data_encryption.py
│ ├── transit_security.py
│ └── key_management.py
└── audit/
├── audit_logger.py
├── compliance_tracker.py
└── security_events.py
Key Features:
- JWT-based authentication
- RBAC with fine-grained permissions
- End-to-end encryption (AES-256)
- Comprehensive audit logging
- SSO integration (SAML, OAuth2, LDAP)
├── infrastructure/
│ ├── database/
│ │ ├── connection_pool.py
│ │ ├── migrations/
│ │ └── models/
│ ├── caching/
│ │ ├── redis_cache.py
│ │ └── memory_cache.py
│ ├── messaging/
│ │ ├── kafka_producer.py
│ │ └── rabbitmq_client.py
│ └── monitoring/
│ ├── metrics_collector.py
│ ├── health_checker.py
│ └── alerting_engine.py
Key Features:
- PostgreSQL/MongoDB for metadata storage
- Redis for caching and session management
- Kafka for event streaming
- Prometheus/Grafana for monitoring
- Connection pooling and circuit breakers
├── api/
│ ├── v1/
│ │ ├── lineage_endpoints.py
│ │ ├── metadata_endpoints.py
│ │ └── admin_endpoints.py
│ ├── middleware/
│ │ ├── authentication.py
│ │ ├── rate_limiting.py
│ │ └── request_validation.py
│ └── schemas/
│ ├── lineage_schemas.py
│ └── response_models.py
Key Features:
- FastAPI-based REST API
- GraphQL support for complex queries
- Rate limiting and throttling
- API versioning and documentation
- Request/response validation
Priority: 🟡 Major - Core Enterprise
├── distributed/
│ ├── cluster_manager.py
│ ├── worker_nodes.py
│ ├── task_scheduler.py
│ └── load_balancer.py
├── streaming/
│ ├── kafka_lineage_consumer.py
│ ├── real_time_processor.py
│ └── stream_analytics.py
└── scalability/
├── horizontal_scaling.py
├── auto_scaling.py
└── resource_optimizer.py
Key Features:
- Apache Spark integration for large datasets
- Kubernetes-native deployment
- Auto-scaling based on load
- Distributed lineage computation
- Real-time streaming lineage
├── advanced_lineage/
│ ├── column_level_tracking.py
│ ├── schema_evolution.py
│ ├── impact_analysis.py
│ ├── root_cause_analyzer.py
│ └── dependency_resolver.py
├── transformations/
│ ├── sql_parser.py
│ ├── spark_lineage_extractor.py
│ └── custom_transformation_tracker.py
└── quality/
├── data_profiler.py
├── quality_scorer.py
└── anomaly_detector.py
Key Features:
- Granular column-level lineage
- SQL parsing and transformation logic capture
- Impact analysis for downstream changes
- Data quality integration
- Schema change tracking
├── connectors/
│ ├── cloud_platforms/
│ │ ├── snowflake_connector.py
│ │ ├── databricks_connector.py
│ │ ├── bigquery_connector.py
│ │ └── redshift_connector.py
│ ├── orchestration/
│ │ ├── airflow_plugin.py
│ │ ├── prefect_integration.py
│ │ └── dbt_lineage_extractor.py
│ └── bi_tools/
│ ├── tableau_connector.py
│ ├── powerbi_connector.py
│ └── looker_connector.py
Key Features:
- Native connectors for major data platforms
- Orchestration tool integrations
- BI tool lineage extraction
- Metadata synchronization
- Real-time connector health monitoring
Priority: 🟡 Major - Regulatory Requirements
├── governance/
│ ├── data_catalog.py
│ ├── business_glossary.py
│ ├── data_stewardship.py
│ ├── policy_engine.py
│ └── certification_manager.py
├── compliance/
│ ├── gdpr_compliance.py
│ ├── sox_compliance.py
│ ├── hipaa_compliance.py
│ └── retention_policies.py
└── quality/
├── validation_rules.py
├── quality_metrics.py
└── sla_monitoring.py
Key Features:
- Data catalog with business context
- Automated compliance checking
- Data retention and deletion policies
- Quality SLA monitoring
- Stewardship workflows
├── monitoring/
│ ├── real_time_dashboard.py
│ ├── anomaly_detection.py
│ ├── performance_monitor.py
│ └── cost_analyzer.py
├── alerting/
│ ├── intelligent_alerts.py
│ ├── escalation_manager.py
│ ├── notification_channels.py
│ └── alert_correlation.py
└── analytics/
├── usage_analytics.py
├── trend_analyzer.py
└── predictive_insights.py
Key Features:
- ML-based anomaly detection
- Intelligent alerting with noise reduction
- Multi-channel notifications (Slack, PagerDuty, email)
- Predictive analytics for pipeline issues
- Cost optimization recommendations
Priority: 🟢 Enhancement - Competitive Advantage
├── ml_lineage/
│ ├── model_tracker.py
│ ├── feature_store_integration.py
│ ├── experiment_lineage.py
│ └── model_performance_correlation.py
├── advanced_analytics/
│ ├── graph_analytics.py
│ ├── pattern_recognition.py
│ ├── optimization_engine.py
│ └── recommendation_system.py
└── ai_insights/
├── natural_language_queries.py
├── automated_documentation.py
└── intelligent_suggestions.py
Key Features:
- End-to-end ML pipeline lineage
- Feature store integration
- Model performance correlation with data changes
- Natural language query interface
- Automated documentation generation
├── ui/
│ ├── react_frontend/
│ ├── interactive_visualizations/
│ ├── mobile_responsive/
│ └── accessibility_features/
├── collaboration/
│ ├── team_workspaces.py
│ ├── commenting_system.py
│ ├── version_control.py
│ └── knowledge_sharing.py
└── self_service/
├── no_code_interface.py
├── drag_drop_builder.py
└── automated_reports.py
Key Features:
- Modern React-based UI
- Interactive 3D lineage visualization
- Mobile-responsive design
- Team collaboration features
- Self-service analytics for business users
- Microservices Architecture: Break into independent, scalable services
- API-First Design: All features accessible via REST/GraphQL APIs
- Cloud-Native: Kubernetes deployment with Helm charts
- Test-Driven Development: 90%+ test coverage for all enterprise features
- DevOps Integration: CI/CD pipelines with automated testing and deployment
Backend:
- Python 3.9+ (FastAPI, SQLAlchemy, Celery)
- PostgreSQL (metadata), Redis (cache), Kafka (streaming)
- Apache Spark (distributed processing)
- Kubernetes (orchestration)
Frontend:
- React 18+ with TypeScript
- D3.js for advanced visualizations
- Material-UI for consistent design
- WebSocket for real-time updates
Infrastructure:
- Docker containers
- Kubernetes with Helm
- Prometheus/Grafana monitoring
- ELK stack for logging
- Terraform for IaC- Security: OWASP compliance, penetration testing
- Performance: Load testing for 100K+ concurrent users
- Scalability: Horizontal scaling to 1M+ lineage nodes
- Reliability: 99.9% uptime SLA
- Compliance: SOC 2, GDPR, HIPAA certifications
- Authentication system with SSO integration
- RBAC with role-based permissions
- End-to-end encryption implementation
- Audit logging for all operations
- API rate limiting and security
- Distributed processing for >1TB datasets
- Real-time streaming lineage
- Column-level lineage tracking
- 5+ enterprise platform connectors
- Impact analysis capabilities
- GDPR/SOX/HIPAA compliance features
- Data governance workflows
- Advanced monitoring and alerting
- Quality SLA tracking
- Automated compliance reporting
- ML pipeline lineage tracking
- Natural language query interface
- Predictive analytics for data issues
- Self-service analytics platform
- Advanced collaboration features
- Senior Backend Engineers: 3-4 FTE
- Frontend Engineers: 2 FTE
- DevOps Engineers: 2 FTE
- Security Engineer: 1 FTE
- Product Manager: 1 FTE
- QA Engineers: 2 FTE
- Development Environment: $5K/month
- Testing Environment: $3K/month
- Security Tools: $2K/month
- Monitoring Tools: $1K/month
- Personnel: $2.5M - $3M (12 months)
- Infrastructure: $130K (12 months)
- Tools & Licenses: $50K (12 months)
- Total: $2.7M - $3.2M
DataLineagePy v2.0.5 requires a complete enterprise transformation to become production-ready. The current codebase provides a foundation but lacks 80-85% of enterprise requirements.
Recommendation: Treat this as a greenfield enterprise project rather than an enhancement of existing code. The investment required is substantial but necessary for true enterprise adoption.
Timeline: 12-18 months for full enterprise readiness Risk Level: High (significant architectural changes required) Success Probability: Medium (depends on dedicated enterprise-focused team)
Critical Gap: No authentication, authorization, or encryption
# security/authentication/mfa_manager.py
class MFAManager:
def setup_totp(self, user_id: str) -> str:
"""Setup Time-based One-Time Password"""
secret = pyotp.random_base32()
totp = pyotp.TOTP(secret)
return totp.provisioning_uri(user_id, issuer_name="DataLineagePy")
def verify_totp(self, user_id: str, token: str) -> bool:
"""Verify TOTP token"""
user_secret = self.get_user_secret(user_id)
totp = pyotp.TOTP(user_secret)
return totp.verify(token, valid_window=1)# security/authentication/jwt_manager.py
class JWTManager:
def __init__(self, secret_key: str, algorithm: str = "HS256"):
self.secret_key = secret_key
self.algorithm = algorithm
self.token_expiry = timedelta(hours=24)
self.refresh_expiry = timedelta(days=30)
def generate_access_token(self, user_data: Dict[str, Any]) -> str:
"""Generate JWT access token with user claims"""
payload = {
"user_id": user_data["id"],
"email": user_data["email"],
"roles": user_data["roles"],
"permissions": user_data["permissions"],
"exp": datetime.utcnow() + self.token_expiry,
"iat": datetime.utcnow(),
"jti": str(uuid.uuid4()) # JWT ID for token revocation
}
return jwt.encode(payload, self.secret_key, algorithm=self.algorithm)
def validate_token(self, token: str) -> Dict[str, Any]:
"""Validate and decode JWT token"""
try:
payload = jwt.decode(token, self.secret_key, algorithms=[self.algorithm])
# Check if token is blacklisted
if self.is_token_blacklisted(payload["jti"]):
raise InvalidTokenError("Token has been revoked")
return payload
except jwt.ExpiredSignatureError:
raise InvalidTokenError("Token has expired")
except jwt.InvalidTokenError:
raise InvalidTokenError("Invalid token")# security/authentication/sso_providers.py
class SAMLProvider:
def __init__(self, idp_metadata_url: str, sp_entity_id: str):
self.idp_metadata_url = idp_metadata_url
self.sp_entity_id = sp_entity_id
self.saml_settings = self._load_saml_settings()
def initiate_sso(self, return_url: str) -> str:
"""Initiate SAML SSO flow"""
auth = OneLogin_Saml2_Auth(self.saml_settings)
return auth.login(return_to=return_url)
def process_sso_response(self, saml_response: str) -> Dict[str, Any]:
"""Process SAML response and extract user data"""
auth = OneLogin_Saml2_Auth(self.saml_settings)
auth.process_response()
if auth.is_authenticated():
return {
"user_id": auth.get_nameid(),
"email": auth.get_attribute("email")[0],
"name": auth.get_attribute("name")[0],
"roles": auth.get_attribute("roles") or []
}
else:
raise AuthenticationError("SAML authentication failed")
class OAuth2Provider:
def __init__(self, client_id: str, client_secret: str, provider: str):
self.client_id = client_id
self.client_secret = client_secret
self.provider = provider
self.oauth_config = self._get_oauth_config(provider)
def get_authorization_url(self, state: str) -> str:
"""Get OAuth2 authorization URL"""
params = {
"client_id": self.client_id,
"response_type": "code",
"scope": "openid email profile",
"state": state,
"redirect_uri": self.oauth_config["redirect_uri"]
}
return f"{self.oauth_config['auth_url']}?{urlencode(params)}"
def exchange_code_for_token(self, code: str, state: str) -> Dict[str, Any]:
"""Exchange authorization code for access token"""
token_data = {
"client_id": self.client_id,
"client_secret": self.client_secret,
"code": code,
"grant_type": "authorization_code",
"redirect_uri": self.oauth_config["redirect_uri"]
}
response = requests.post(self.oauth_config["token_url"], data=token_data)
if response.status_code == 200:
return response.json()
else:
raise AuthenticationError("Failed to exchange code for token")# security/authorization/rbac_engine.py
class RBACEngine:
def __init__(self, db_session):
self.db = db_session
self.permission_cache = TTLCache(maxsize=10000, ttl=300) # 5-minute cache
def check_permission(self, user_id: str, resource: str, action: str) -> bool:
"""Check if user has permission for resource action"""
cache_key = f"{user_id}:{resource}:{action}"
if cache_key in self.permission_cache:
return self.permission_cache[cache_key]
# Get user roles
user_roles = self.get_user_roles(user_id)
# Check permissions for each role
has_permission = False
for role in user_roles:
role_permissions = self.get_role_permissions(role.id)
for permission in role_permissions:
if self._matches_permission(permission, resource, action):
has_permission = True
break
if has_permission:
break
# Cache result
self.permission_cache[cache_key] = has_permission
return has_permission
def _matches_permission(self, permission: Permission, resource: str, action: str) -> bool:
"""Check if permission matches resource and action with wildcard support"""
resource_match = (
permission.resource == resource or
permission.resource == "*" or
fnmatch.fnmatch(resource, permission.resource)
)
action_match = (
permission.action == action or
permission.action == "*" or
fnmatch.fnmatch(action, permission.action)
)
return resource_match and action_match
# security/authorization/policy_engine.py
class PolicyEngine:
def __init__(self):
self.policies = {}
self.policy_evaluator = PolicyEvaluator()
def create_policy(self, policy_name: str, policy_definition: Dict[str, Any]):
"""Create a new access policy"""
policy = {
"name": policy_name,
"version": "1.0",
"statement": policy_definition["statement"],
"created_at": datetime.utcnow(),
"created_by": policy_definition.get("created_by")
}
self.policies[policy_name] = policy
def evaluate_policy(self, user_context: Dict[str, Any], resource: str, action: str) -> bool:
"""Evaluate policies against user context"""
applicable_policies = self._get_applicable_policies(user_context, resource)
for policy in applicable_policies:
if self.policy_evaluator.evaluate(policy, user_context, resource, action):
return True
return False# security/authorization/permissions.py
class PermissionManager:
# Lineage Permissions
LINEAGE_READ = "lineage:read"
LINEAGE_WRITE = "lineage:write"
LINEAGE_DELETE = "lineage:delete"
LINEAGE_ADMIN = "lineage:admin"
# Data Permissions
DATA_VIEW = "data:view"
DATA_EXPORT = "data:export"
DATA_MASK = "data:mask"
# System Permissions
SYSTEM_ADMIN = "system:admin"
USER_MANAGE = "user:manage"
ROLE_MANAGE = "role:manage"
# Compliance Permissions
AUDIT_VIEW = "audit:view"
COMPLIANCE_MANAGE = "compliance:manage"
@staticmethod
def get_default_roles():
return {
"viewer": [
PermissionManager.LINEAGE_READ,
PermissionManager.DATA_VIEW
],
"analyst": [
PermissionManager.LINEAGE_READ,
PermissionManager.LINEAGE_WRITE,
PermissionManager.DATA_VIEW,
PermissionManager.DATA_EXPORT
],
"admin": [
PermissionManager.LINEAGE_ADMIN,
PermissionManager.DATA_VIEW,
PermissionManager.DATA_EXPORT,
PermissionManager.USER_MANAGE,
PermissionManager.ROLE_MANAGE,
PermissionManager.AUDIT_VIEW
],
"system_admin": ["*"] # All permissions
}# security/encryption/data_encryption.py
class DataEncryption:
def __init__(self, master_key: bytes):
self.master_key = master_key
self.cipher_suite = Fernet(master_key)
def encrypt_sensitive_data(self, data: str) -> str:
"""Encrypt sensitive data using AES-256"""
if not data:
return data
encrypted_data = self.cipher_suite.encrypt(data.encode('utf-8'))
return base64.b64encode(encrypted_data).decode('utf-8')
def decrypt_sensitive_data(self, encrypted_data: str) -> str:
"""Decrypt sensitive data"""
if not encrypted_data:
return encrypted_data
try:
decoded_data = base64.b64decode(encrypted_data.encode('utf-8'))
decrypted_data = self.cipher_suite.decrypt(decoded_data)
return decrypted_data.decode('utf-8')
except Exception as e:
raise DecryptionError(f"Failed to decrypt data: {str(e)}")
def encrypt_column_data(self, df: pd.DataFrame, sensitive_columns: List[str]) -> pd.DataFrame:
"""Encrypt sensitive columns in DataFrame"""
encrypted_df = df.copy()
for column in sensitive_columns:
if column in encrypted_df.columns:
encrypted_df[column] = encrypted_df[column].apply(
lambda x: self.encrypt_sensitive_data(str(x)) if pd.notna(x) else x
)
return encrypted_df
# security/encryption/key_management.py
class KeyManager:
def __init__(self, vault_client=None):
self.vault_client = vault_client # HashiCorp Vault integration
self.key_rotation_interval = timedelta(days=90)
def generate_master_key(self) -> bytes:
"""Generate new master encryption key"""
return Fernet.generate_key()
def rotate_keys(self):
"""Rotate encryption keys"""
new_key = self.generate_master_key()
# Store new key in vault
if self.vault_client:
self.vault_client.secrets.kv.v2.create_or_update_secret(
path='datalineage/encryption-key',
secret={'key': base64.b64encode(new_key).decode('utf-8')}
)
# Re-encrypt existing data with new key
self._re_encrypt_data_with_new_key(new_key)
def get_current_key(self) -> bytes:
"""Get current encryption key from vault"""
if self.vault_client:
response = self.vault_client.secrets.kv.v2.read_secret_version(
path='datalineage/encryption-key'
)
key_b64 = response['data']['data']['key']
return base64.b64decode(key_b64.encode('utf-8'))
else:
# Fallback to environment variable
key_b64 = os.getenv('DATALINEAGE_MASTER_KEY')
if not key_b64:
raise KeyError("Master encryption key not found")
return base64.b64decode(key_b64.encode('utf-8'))# security/encryption/transit_security.py
class TransitSecurity:
def __init__(self):
self.tls_context = self._create_tls_context()
def _create_tls_context(self) -> ssl.SSLContext:
"""Create secure TLS context"""
context = ssl.create_default_context(ssl.Purpose.SERVER_AUTH)
context.minimum_version = ssl.TLSVersion.TLSv1_2
context.set_ciphers('ECDHE+AESGCM:ECDHE+CHACHA20:DHE+AESGCM:DHE+CHACHA20:!aNULL:!MD5:!DSS')
return context
def create_secure_connection(self, host: str, port: int) -> ssl.SSLSocket:
"""Create secure TLS connection"""
sock = socket.create_connection((host, port))
secure_sock = self.tls_context.wrap_socket(sock, server_hostname=host)
return secure_sock
def verify_certificate(self, cert_path: str) -> bool:
"""Verify SSL certificate"""
try:
with open(cert_path, 'rb') as cert_file:
cert_data = cert_file.read()
cert = x509.load_pem_x509_certificate(cert_data, default_backend())
# Check if certificate is expired
if cert.not_valid_after < datetime.utcnow():
return False
# Additional certificate validation logic
return True
except Exception:
return False# security/audit/audit_logger.py
class AuditLogger:
def __init__(self, db_session, encryption_manager):
self.db = db_session
self.encryption = encryption_manager
self.logger = logging.getLogger('audit')
def log_access(self, user_id: str, resource: str, action: str,
result: str, metadata: Dict[str, Any] = None):
"""Log access attempt"""
audit_entry = AuditLog(
timestamp=datetime.utcnow(),
user_id=user_id,
resource=resource,
action=action,
result=result, # SUCCESS, DENIED, ERROR
ip_address=self._get_client_ip(),
user_agent=self._get_user_agent(),
session_id=self._get_session_id(),
metadata=json.dumps(metadata or {})
)
self.db.add(audit_entry)
self.db.commit()
# Also log to external SIEM if configured
self._send_to_siem(audit_entry)
def log_data_access(self, user_id: str, dataset_id: str,
columns_accessed: List[str], row_count: int):
"""Log data access for compliance"""
self.log_access(
user_id=user_id,
resource=f"dataset:{dataset_id}",
action="data_access",
result="SUCCESS",
metadata={
"columns_accessed": columns_accessed,
"row_count": row_count,
"access_type": "read"
}
)
def generate_compliance_report(self, start_date: datetime,
end_date: datetime, user_id: str = None) -> Dict[str, Any]:
"""Generate compliance report for auditors"""
query = self.db.query(AuditLog).filter(
AuditLog.timestamp.between(start_date, end_date)
)
if user_id:
query = query.filter(AuditLog.user_id == user_id)
audit_logs = query.all()
return {
"report_period": {"start": start_date, "end": end_date},
"total_access_attempts": len(audit_logs),
"successful_accesses": len([log for log in audit_logs if log.result == "SUCCESS"]),
"denied_accesses": len([log for log in audit_logs if log.result == "DENIED"]),
"unique_users": len(set(log.user_id for log in audit_logs)),
"most_accessed_resources": self._get_resource_access_stats(audit_logs),
"compliance_violations": self._detect_compliance_violations(audit_logs)
}# security/middleware/api_security.py
class SecurityMiddleware:
def __init__(self, app, jwt_manager, rbac_engine, rate_limiter):
self.app = app
self.jwt_manager = jwt_manager
self.rbac_engine = rbac_engine
self.rate_limiter = rate_limiter
self.audit_logger = AuditLogger()
async def __call__(self, scope, receive, send):
if scope["type"] == "http":
request = Request(scope, receive)
# Rate limiting
if not await self.rate_limiter.is_allowed(request):
response = JSONResponse(
{"error": "Rate limit exceeded"},
status_code=429
)
await response(scope, receive, send)
return
# Authentication
try:
token = self._extract_token(request)
user_data = self.jwt_manager.validate_token(token)
request.state.user = user_data
except InvalidTokenError:
response = JSONResponse(
{"error": "Invalid or expired token"},
status_code=401
)
await response(scope, receive, send)
return
# Authorization
resource = self._extract_resource(request)
action = self._extract_action(request)
if not self.rbac_engine.check_permission(
user_data["user_id"], resource, action
):
self.audit_logger.log_access(
user_data["user_id"], resource, action, "DENIED"
)
response = JSONResponse(
{"error": "Insufficient permissions"},
status_code=403
)
await response(scope, receive, send)
return
# Log successful access
self.audit_logger.log_access(
user_data["user_id"], resource, action, "SUCCESS"
)
await self.app(scope, receive, send)# security/config/security_settings.py
class SecurityConfig:
# Authentication
JWT_SECRET_KEY = os.getenv('JWT_SECRET_KEY')
JWT_ALGORITHM = 'HS256'
JWT_EXPIRATION_HOURS = 24
JWT_REFRESH_DAYS = 30
# Password Policy
PASSWORD_MIN_LENGTH = 12
PASSWORD_REQUIRE_UPPERCASE = True
PASSWORD_REQUIRE_LOWERCASE = True
PASSWORD_REQUIRE_NUMBERS = True
PASSWORD_REQUIRE_SYMBOLS = True
PASSWORD_HISTORY_COUNT = 5
# Session Management
SESSION_TIMEOUT_MINUTES = 30
MAX_CONCURRENT_SESSIONS = 3
# Rate Limiting
RATE_LIMIT_PER_MINUTE = 100
RATE_LIMIT_BURST = 200
# Encryption
ENCRYPTION_ALGORITHM = 'AES-256-GCM'
KEY_ROTATION_DAYS = 90
# Audit
AUDIT_LOG_RETENTION_DAYS = 2555 # 7 years for compliance
AUDIT_LOG_ENCRYPTION = True
# Compliance
GDPR_ENABLED = True
SOX_ENABLED = True
HIPAA_ENABLED = True- Week 1-2: JWT Authentication + Basic RBAC
- Week 3-4: Data Encryption + Key Management
- Week 5-6: Audit Logging + Compliance Framework
- Week 7-8: API Security + Rate Limiting
- Week 9-10: SSO Integration + MFA
- Week 11-12: Security Testing + Penetration Testing
- Authentication: Multi-factor, SSO, password policies
- Authorization: RBAC, fine-grained permissions, policy engine
- Encryption: AES-256 at rest, TLS 1.3 in transit
- Key Management: Vault integration, automatic rotation
- Audit Logging: Comprehensive, tamper-proof, 7-year retention
- API Security: Rate limiting, input validation, CORS
- Compliance: GDPR, SOX, HIPAA ready
- Security Testing: Penetration testing, vulnerability scanning
This security framework transforms DataLineagePy from 0/100 to enterprise-grade security standards.
This roadmap provides a comprehensive path to enterprise readiness. Each phase builds upon the previous one, ensuring a solid foundation for enterprise-grade data lineage tracking.