When dealing with exabyte-scale data (1 EB = 1 billion GB), traditional backup approaches become impractical. Companies like Google reportedly operate over 450,000 servers with 80+ TB drives each (modern HDD capacities have increased since the Wikipedia reference). Maintaining 1:1 backups at this scale would be cost-prohibitive.

Tech giants implement several key strategies:

• Erasure Coding: Instead of full copies, data is split into fragments with parity. A common 10+4 scheme means 10 data fragments can reconstruct the original from any 4 fragments.
• Multi-Region Storage: Data is automatically replicated across geographically distributed data centers.
• Versioned Snapshots: Point-in-time copies with change tracking rather than full backups.

Here's a simplified Python prototype using erasure coding:

import erasurecode
from distributed_storage import StorageNode

class HyperBackupSystem:
    def __init__(self):
        self.nodes = [StorageNode(f"node_{i}") for i in range(14)]
        
    def store_data(self, data):
        # Split data into 10 fragments with 4 parity fragments
        fragments = erasurecode.encode(data, data_fragments=10, parity_fragments=4)
        
        # Distribute fragments across nodes
        for i, fragment in enumerate(fragments):
            self.nodes[i % len(self.nodes)].store(fragment)
    
    def retrieve_data(self):
        # Need only 10 out of 14 fragments to reconstruct
        fragments = []
        for node in self.nodes:
            try:
                fragments.append(node.retrieve())
                if len(fragments) >= 10:
                    break
            except NodeDownError:
                continue
                
        return erasurecode.decode(fragments)

Production systems combine multiple technologies:

• Google Colossus: Successor to GFS, handles distributed storage with automatic recovery
• Facebook Warm BLOB Storage: Hybrid cold/warm storage with configurable replication
• Amazon S3 Glacier: Deep archive with retrieval latency tradeoffs

Large providers use sophisticated data lifecycle management:

// Pseudocode for tiered storage policy
if (data_age < 7 days) {
    store_in_ssd_tier(replicas=3);
} else if (data_age < 30 days) {
    store_in_hdd_tier(replicas=2, erasure_coding=6+3);
} else {
    archive_in_tape_library(erasure_coding=10+4);
}

When dealing with petabyte-scale data storage like Google's estimated 450,000+ servers (each with 80GB+ disks), traditional backup approaches simply don't scale. The total raw storage approaches 36 petabytes - and that's just the primary storage.

Major tech companies implement sophisticated distributed systems rather than simple 1:1 backups. Here's a conceptual framework similar to what Google might use:

// Simplified distributed backup system architecture
type BackupStrategy interface {
    Store(shardID string, data []byte) error
    Retrieve(shardID string) ([]byte, error)
    VerifyIntegrity(shardID string) bool
}

type ReedSolomonBackup struct {
    dataShards     int
    parityShards   int
    storageNodes   []StorageNode
}

func (rs *ReedSolomonBackup) Store(data []byte) error {
    // Split data into shards with erasure coding
    encoded, err := reedsolomon.Encode(data, rs.dataShards, rs.parityShards)
    if err != nil {
        return err
    }
    
    // Distribute shards across nodes
    for i, shard := range encoded {
        node := rs.storageNodes[i%len(rs.storageNodes)]
        if err := node.Store(fmt.Sprintf("shard-%d", i), shard); err != nil {
            return err
        }
    }
    return nil
}

Large companies employ several advanced techniques:

• Erasure Coding: Instead of full replication, use algorithms like Reed-Solomon to store data more efficiently (e.g., 10+6 configuration where 6 parity shards protect against 6 failures)
• Incremental Forever Backups: Only backup changed blocks after initial full backup
• Geographic Distribution: Store copies in multiple data centers across continents
• Storage Tiering: Hot, warm, and cold storage layers based on access patterns

Here's a simplified version of how data might flow through a backup system:

// Sample data pipeline in Go
package main

import (
    "bytes"
    "compress/gzip"
    "crypto/sha256"
    "encoding/hex"
    "io"
    "log"
)

func processBackup(data []byte) (string, error) {
    // Step 1: Checksum verification
    hash := sha256.Sum256(data)
    checksum := hex.EncodeToString(hash[:])
    
    // Step 2: Compression
    var buf bytes.Buffer
    gz := gzip.NewWriter(&buf)
    if _, err := gz.Write(data); err != nil {
        return "", err
    }
    if err := gz.Close(); err != nil {
        return "", err
    }
    
    // Step 3: Erasure coding would happen here in production
    // (shown in previous example)
    
    return checksum, nil
}

func main() {
    sampleData := []byte("This represents production data")
    checksum, err := processBackup(sampleData)
    if err != nil {
        log.Fatal(err)
    }
    log.Printf("Backup processed with checksum: %s", checksum)
}

Validating petabytes of backed-up data requires automation:

• Checksum verification for all data chunks
• Regular test restores of random data samples
• Automated integrity checking during data migration
• Canary systems that continuously verify backup accessibility

Rather than maintaining full 1:1 backups (which would double storage costs), companies use:

• Delta encoding for changed data only
• Cold storage (tape or slow disks) for older backups
• Multi-level retention policies (daily for 30 days, weekly for 1 year, etc.)
• Deduplication across entire storage fleet