Disaster Recovery Basics

Why this matters

Data Engineers keep data flowing and available. Disasters still happen: region outages, corrupted tables, bad deployments, accidental deletes, or ransomware. A good Disaster Recovery (DR) plan protects the business from data loss and long downtime.

• Restore a data warehouse after a bad migration
• Recover a Kafka cluster after a zone outage
• Roll back a corrupted partition with point-in-time recovery (PITR)
• Fail over analytics to a secondary region during a cloud incident

Concept explained simply

Two dials define DR:

• RPO (Recovery Point Objective): how much data you can afford to lose (time window)
• RTO (Recovery Time Objective): how long you can be down

Everything you choose (backups, replication, automation) tunes these two dials, at a cost.

Mental model

Imagine a timeline of events. Backups and replication place safe checkpoints along that timeline. Failover switches traffic to a standby system. Your runbook is the map to reach the latest safe checkpoint fast.

Core building blocks

• Backups: full, incremental, differential. Keep multiple restore points.
• Snapshots and PITR: create fast restore points for databases, object storage versions, and metadata stores.
• Replication: synchronous (low RPO, higher cost/latency) vs asynchronous (slightly higher RPO, lower cost/latency).
• Environments: cold (cheapest, slowest), warm (pre-provisioned, faster), hot/active-active (fastest, costliest).
• 3-2-1 rule: 3 copies, 2 media/types, 1 offsite or cross-region. Add immutable/object-lock where possible.
• Runbooks: clear, testable steps to fail over, restore, and fail back.
• Infrastructure as Code (IaC): lets you recreate infra consistently.
• Testing: regular drills catch drift and missing permissions.

Worked examples

1) Nightly batch warehouse

Context: Warehouse refreshed nightly at 02:00 UTC. Business can tolerate 24 hours of data loss and 8 hours downtime.

• RPO: 24h; RTO: 8h
• Strategy: daily full backup + weekly validation; cross-region copy of backups; restore IaC templates ready
• Result: cold or warm standby acceptable; low cost

2) Streaming analytics dashboard

Context: Kafka + stream processing feeding near real-time dashboards. Business tolerates 5 minutes data loss and 15 minutes downtime.

• RPO: 5m; RTO: 15m
• Strategy: multi-AZ Kafka with synchronous replication; offset checkpoints replicated; warm standby stream processors in secondary region with async topic replication
• Result: higher cost, fast recovery; small risk of last seconds lost

3) Feature store for ML inference

Context: Online feature store serving models. Business tolerates 1 minute data loss and 5 minutes downtime.

• RPO: 1m; RTO: 5m
• Strategy: managed DB with PITR; cross-region read replica (sync if latency allows); hot standby services with automated failover
• Result: expensive but aligned with strict SLAs

Build a minimal DR plan (step-by-step)

1. Define RPO/RTO with stakeholders. Write them down.
2. Choose backup cadence and retention (apply 3-2-1 and immutability).
3. Select replication mode and target (zones/regions).
4. Document a failover/failback runbook (who, what, when, how).
5. Automate with IaC and orchestrator tasks.
6. Test with a drill; record gaps and fix them.
7. Monitor recovery signals: backup success, lag, replica health, restore times.

Exercises

Do these before the quick test. For saved progress, log in; the quick test is available to everyone.

1. Exercise 1: Set RPO/RTO and propose a DR approach for a warehouse with constraints. See details below.
2. Exercise 2: Draft a failover runbook for a streaming pipeline. See details below.

• [ ] I wrote explicit RPO/RTO numbers
• [ ] I defined backup frequency, retention, and offsite/immutable copy
• [ ] I selected replication mode and target regions
• [ ] I drafted failover and failback steps
• [ ] I planned a drill and success metrics

Common mistakes and self-check

• Mixing up RTO and RPO. Self-check: Can you state both in minutes/hours for your system?
• Backups without restore tests. Self-check: When was your last restore drill? How long did it take?
• Single-region everything. Self-check: If the region dies, what works today?
• Forgetting stateful dependencies (metadata DBs, checkpoints). Self-check: List each stateful component and its recovery method.
• No immutability. Self-check: Can ransomware delete your backups?
• Unclear ownership. Self-check: Who declares disaster and leads recovery?

Practical projects

• Design a DR plan for your current pipeline: fill the runbook template and schedule a 30-minute tabletop drill.
• Implement hourly incremental backups with object-lock for a key dataset; measure restore time from last two points.
• Stand up a warm standby for your orchestrator metadata DB and validate failover.

Who this is for

• Aspiring and working Data Engineers
• Analytics engineers and platform engineers touching storage and orchestration

Prerequisites

• Basic understanding of data pipelines (batch or streaming)
• Familiarity with cloud storage/compute concepts and IAM

Learning path

• Start: Disaster Recovery Basics (this page)
• Next: Infrastructure as Code Basics to codify your recovery
• Then: Monitoring and Alerting to detect incidents early
• Finally: CI/CD for Data Pipelines to reduce deployment-related incidents

Next steps

• Complete the exercises and run a mini tabletop drill with a teammate
• Take the quick test below
• Book time to implement at least one improvement (e.g., immutable backups)

Mini challenge

Your primary region is down for 4 hours. Your dashboard can tolerate 10 minutes of data loss and 20 minutes downtime. In one paragraph, describe your failover steps, what data (if any) you reprocess, and how you decide when to fail back.