Many developers assume RAID (Redundant Array of Independent Disks) provides data protection equivalent to backups. This confusion stems from not understanding the distinct purposes of these technologies.

RAID provides fault tolerance through redundancy. For example, RAID 1 mirrors data across disks:

# Linux mdadm RAID 1 creation example
mdadm --create /dev/md0 --level=1 --raid-devices=2 /dev/sda1 /dev/sdb1
mkfs.ext4 /dev/md0
mount /dev/md0 /mnt/raid_array

While this protects against disk failure, it doesn't guard against:

• Accidental file deletion
• Corruption propagating across mirrors
• Ransomware attacks
• Catastrophic physical damage

A proper backup solution provides:

# Example backup script with versioning
#!/bin/bash
BACKUP_DIR="/backups/$(date +%Y-%m-%d)"
rsync -a --link-dest=/backups/previous /data/ $BACKUP_DIR
ln -sfn $BACKUP_DIR /backups/previous

Key differences from RAID:

Consider these cases where RAID fails but backups save the day:

// Database corruption example
// RAID would preserve the corrupted data across all mirrors
// Backup would allow restoring to pre-corruption state

const backup = require('db-backup');
backup.restore({
  timestamp: '2023-06-15T14:00:00Z',
  target: '/var/lib/mysql'
});

The optimal solution combines both approaches:

1. Use RAID for high availability
2. Implement automated backups with versioning
3. Store backups in geographically separate locations
4. Regularly test restore procedures

Here's a complete backup solution example using AWS S3:

# Python backup script with S3 integration
import boto3
from datetime import datetime
import os

s3 = boto3.client('s3')
backup_time = datetime.now().strftime('%Y%m%d_%H%M%S')
backup_file = f'/tmp/backup_{backup_time}.tar.gz'

os.system(f'tar -czf {backup_file} /important_data')
s3.upload_file(backup_file, 'my-backup-bucket', f'backups/{backup_file}')

Let's cut through the confusion immediately: RAID (Redundant Array of Independent Disks) provides fault tolerance, not data protection. While both concepts involve multiple disks, their purposes diverge dramatically in enterprise environments.

// RAID 1 (Mirroring) pseudo-implementation
void writeToDisks(Data data) {
    disk1.write(data); // Primary disk
    disk2.write(data); // Mirror disk
    // Immediate sync - no versioning
}

This simple mirroring example reveals the core limitation - RAID maintains real-time synchronization but offers zero protection against:

• Human error (rm -rf /data)
• Malware/crypto locker attacks
• Logical corruption spreading instantly
• Physical disasters (fire, flood)

A proper backup solution implements:

class TrueBackup {
    constructor() {
        this.versioning = true;
        this.airGap = true;
        this.retentionPolicy = '30-60-90';
        this.verification = checksumValidation();
    }
    
    snapshot(data) {
        // Creates point-in-time recovery
        return new VersionedCopy(data);
    }
}

Consider these production nightmares:

1. Case 1: RAID 5 array with bit rot corruption - all volumes instantly corrupted
2. Case 2: Accidental database DROP TABLE propagates across mirrored drives
3. Case 3: Ransomware encrypts live storage with RAID 10 - no recovery points

For PostgreSQL databases, combine RAID with proper backups:

# PostgreSQL backup script with WAL archiving
pg_basebackup -D /backup/$(date +%Y-%m-%d) \
    -X stream \
    -P \
    -U replicator \
    -h primary.example.com
    
# Combine with RAID 10 for performance
mdadm --create /dev/md0 --level=10 --raid-devices=4 /dev/sd[b-e]