In an era where data is currency, the importance of resilient, fast, and reliable backup strategies cannot be overstated. Whether it’s a fintech handling millions of transactions per second or a SaaS company syncing customer configs, database backups are the last line of defense against data loss, corruption, and disasters.
This post dives deep into the backup strategies employed by top-tier companies (Google, Netflix, Amazon, Meta, etc.), and helps you decide when to use which, with a focus on trade-offs, performance impact, and restoration goals.
🧰 1. Full Backups
Definition: A complete copy of the entire database at a specific point in time.
✅ When to Use:
- Small to medium-sized databases
- Initial base for incremental/differential backups
- Nightly backups when storage & time are not a concern
⚖️ Trade-offs:
| Pros | Cons |
|---|---|
| Simplest to restore | High storage cost |
| No dependency on other files | Slower backup time |
| Ideal for compliance | Not feasible for very large datasets |
🏢 Real-World:
- Stripe: Performs full encrypted backups for compliance and auditing needs.
- Meta: Stores full snapshots in cold storage for long-term retention.
🔁 2. Incremental Backups
Definition: Only the changes made since the last backup (full or incremental) are saved.
✅ When to Use:
- Large datasets with low write frequency
- To reduce backup window and storage
- Systems with tight RTO (Recovery Time Objective) and RPO (Recovery Point Objective)
⚖️ Trade-offs:
| Pros | Cons |
|---|---|
| Saves bandwidth and storage | Restore requires chaining backups |
| Fast backups | Slower and more complex restores |
| Efficient for frequently updated | Prone to chain corruption |
🏢 Real-World:
- Netflix: Leverages incremental S3-based backups for Cassandra.
- Google Cloud Spanner: Supports incremental export with minimal lock time.
🧱 3. Differential Backups
Definition: Backs up all changes made since the last full backup (not the last backup).
✅ When to Use:
- You want a balance between full and incremental
- Systems where restore speed matters more than backup time
⚖️ Trade-offs:
| Pros | Cons |
|---|---|
| Faster restore than incremental | Grows in size over time |
| Safer than incremental | Less efficient than incremental |
| Simple to manage | Still requires full base backup |
🏢 Real-World:
- Microsoft Azure SQL Database: Uses differential for mid-week recovery points.
🧪 4. Logical Backups (SQL Dumps)
Definition: Backs up database schema and data in a human-readable format (e.g., SQL dump).
✅ When to Use:
- Migrating between DBMS types (MySQL → PostgreSQL)
- Smaller databases where portability matters
- Versioning schema with Git
⚖️ Trade-offs:
| Pros | Cons |
|---|---|
| DB-agnostic & portable | Slower and more resource intensive |
| Easy to diff and version | Not suited for big datasets |
| Useful for partial restores | Lacks consistency on large writes |
🏢 Real-World:
- GitLab: Uses logical backups to mirror production data for staging.
- Startups & Open Source Projects: Often default to logical due to simplicity.
💾 5. Physical Backups (Binary Snapshots)
Definition: File-level copies of the database's data files, often using filesystem or volume-level snapshot tools (e.g., LVM, ZFS, EBS snapshots).
✅ When to Use:
- High-throughput databases (PostgreSQL, Cassandra, MongoDB)
- Large datasets requiring fast restore
- Consistency needed across multiple nodes (sharded systems)
⚖️ Trade-offs:
| Pros | Cons |
|---|---|
| Extremely fast restore times | Platform-dependent (e.g., EBS only) |
| No performance hit | Snapshot may require quiescing writes |
| Can be scheduled with volume managers | Complex cross-region replication |
🏢 Real-World:
- Amazon RDS / Aurora: Uses EBS snapshots under the hood.
- Airbnb: Uses EBS snapshots for MySQL replicas across regions.
🌍 6. Streaming Replication + PITR (Point-In-Time Recovery)
Definition: Continuously streams write-ahead logs (WALs) or binlogs to replicas or storage for precise recovery.
✅ When to Use:
- Mission-critical systems (financial, telecom)
- Systems with low RPO requirements
- Append-only workloads
⚖️ Trade-offs:
| Pros | Cons |
|---|---|
| Near real-time backups | Complex to manage/monitor |
| Enables PITR | Requires fine-tuned log storage |
| Critical for HA + failover systems | Storage-intensive over time |
🏢 Real-World:
- Robinhood: Uses WAL streaming for PostgreSQL with PITR.
- Facebook: Applies PITR + logical backups in combination for MyRocks.
🧬 7. Change Data Capture (CDC) + Backup Pipelines
Definition: Tracks row-level changes via logs and exports them into a backup store like S3, GCS, etc.
✅ When to Use:
- Real-time sync across regions or cloud providers
- Analytical pipelines (e.g., Snowflake, BigQuery)
- Decoupling backup from primary DB load
⚖️ Trade-offs:
| Pros | Cons |
|---|---|
| Real-time change tracking | Requires infra (Debezium, Kafka, etc) |
| Ideal for hybrid transactional/analytical | Complex to set up |
| Can recover specific changes | Expensive for small orgs |
🏢 Real-World:
- Uber: Uses CDC + Kafka for MySQL to BigQuery pipelines.
- DoorDash: Relies on Debezium for PostgreSQL change tracking.
🚦 Backup Selection Matrix
| Use Case | Recommended Strategy |
|---|---|
| Small, simple app | Full + periodic logical dump |
| Large, write-heavy system | Full + Incremental or Differential + PITR |
| High availability (HA) systems | Streaming Replication + WAL/Binlog Backups |
| Disaster recovery compliance | Physical Snapshots + Offsite Storage |
| Multi-cloud/hybrid sync | CDC pipelines + Object Storage (S3, GCS) |
| Data migration | Logical backups or CDC with schema export |
🧠 Best Practices for Backup Management
- Automate everything – Cron, Airflow, or Kubernetes CronJobs.
- Encrypt backups at rest and in transit.
- Test restores regularly – backups are only as good as your ability to restore.
- Version your backups – use timestamped folders.
- Store offsite and cross-region – S3 Glacier, GCS Coldline, etc.
- Monitor & alert on backup failures.
📦 Tools & Technologies Widely Used
| Tool/Service | Purpose |
|---|---|
pg_basebackup, WAL-G |
PostgreSQL physical + streaming backups |
mysqldump, xtrabackup
|
MySQL logical and physical backups |
Velero |
Kubernetes-aware volume backups |
Restic, BorgBackup
|
Cross-platform encrypted backups |
AWS Backup, GCP Backup
|
Managed cloud-native backup |
Debezium, Kafka
|
Real-time CDC pipelines |
🧭 Conclusion
Top tech companies don’t rely on a single backup strategy—they combine multiple layers (full + incremental + PITR + CDC) to build resilient, auditable, and fast-recovery systems. Your choice should align with:
- Data volatility
- Downtime tolerance
- Recovery time (RTO) & point (RPO) goals
- Storage & compute budgets
A well-architected backup strategy is like insurance—you hope you never use it, but when you do, it must just work.
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