REL09-BP01: Identify and back up all data that needs to be backed up, or reproduce the data from sources

Overview

Implement comprehensive data discovery and classification processes to identify all critical data assets that require backup protection. Establish clear data categorization based on business criticality, regulatory requirements, and recovery objectives to ensure complete data protection coverage.

Implementation Steps

1. Conduct Data Discovery and Inventory

Implement automated data discovery across all systems and services
Create comprehensive data asset inventory and classification
Identify data sources, dependencies, and relationships
Establish data lineage and flow mapping

2. Classify Data by Criticality and Requirements

Define data classification categories based on business impact
Establish Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO)
Identify regulatory and compliance requirements for different data types
Create data retention and lifecycle policies

3. Design Backup Strategies by Data Type

Implement differentiated backup strategies based on data classification
Design backup frequency and retention policies for each data category
Establish cross-region and multi-tier backup approaches
Configure backup validation and integrity checking

4. Implement Automated Data Discovery

Configure continuous data discovery and classification updates
Implement automated backup policy assignment based on data classification
Design data change detection and backup triggering
Establish data governance and policy enforcement

5. Create Data Reproducibility Framework

Identify data that can be reproduced from authoritative sources
Implement automated data regeneration and reconstruction processes
Design source system integration and data pipeline automation
Establish data quality validation and consistency checking

6. Monitor and Maintain Data Coverage

Track backup coverage across all identified data assets
Monitor data classification accuracy and completeness
Implement continuous improvement based on data discovery insights
Establish data protection gap analysis and remediation

Implementation Examples

Example 1: Comprehensive Data Discovery and Backup Management System

AWS Services Used

AWS Backup: Centralized backup service for cross-service data protection
Amazon S3: Object storage with lifecycle policies and cross-region replication
Amazon RDS: Database backup with automated snapshots and point-in-time recovery
Amazon DynamoDB: NoSQL database with point-in-time recovery and on-demand backup
Amazon EFS: File system backup with automatic and manual snapshots
Amazon EBS: Block storage snapshots with lifecycle management
AWS Organizations: Multi-account data discovery and governance
AWS Config: Resource inventory and configuration tracking
Amazon CloudWatch: Metrics collection for storage utilization and backup monitoring
AWS CloudTrail: Audit logging for data access and backup operations
AWS Systems Manager: Parameter management for backup configurations
AWS Lambda: Custom data discovery and classification automation
Amazon EventBridge: Event-driven backup policy enforcement
AWS Step Functions: Complex data discovery workflow orchestration
AWS Glue: Data catalog and metadata management for discovery

Benefits

Complete Coverage: Comprehensive discovery ensures no critical data is missed
Risk-Based Protection: Data classification enables appropriate protection levels
Cost Optimization: Differentiated backup strategies optimize storage costs
Compliance Assurance: Automated classification supports regulatory requirements
Operational Efficiency: Automated discovery reduces manual inventory management
Data Governance: Centralized data asset management and policy enforcement
Recovery Planning: Clear RTO/RPO objectives enable effective disaster recovery
Source Integration: Reproducible data strategies reduce backup storage requirements
Continuous Monitoring: Ongoing discovery maintains accurate data inventory
Policy Automation: Automated policy assignment ensures consistent protection