Disaster recovery
Disaster recovery is the planning and execution of processes and capabilities for restoring systems, data, and services after a disruptive event.
Think Like a CTO
A disaster recovery strategy is a planned approach for restoring services after a disruptive event by transferring operations to an alternate environment.
- How it is used
- Plans continuity of client-facing operations when the primary environment fails.
- How deep it goes
- Works through downtime, reduced service, and switch procedures; stops short of implementation-level instructions.
- Examples
- Uses fire and network failure scenarios, read-only social posts, and ecommerce browsing without checkout.
- What you can do after
- Practice/perform a documented recovery switch and test the plan, including how to fail forward or back.
AI for Everyday IT
The process of preparing documented procedures for restoring systems and operations after a disruptive event.
- How it is used
- Uses generative AI to draft disaster recovery documentation and help teams prepare for service disruptions.
- How deep it goes
- Explains DR, distinguishes COOP, supplies planning inputs, and stops short of real-time plan generation or unattended recovery.
- Examples
- Uses a MySQL environment with about 100 servers and requests for inventory, backup strategy, and recovery objectives.
- What you can do after
- Build it from scratch by supplying infrastructure and recovery requirements to an AI service, then reviewing its documentation.
100 SQL Server Mistakes and How to Avoid Them
Disaster recovery is the set of processes and capabilities for restoring systems and data after a disruptive failure or disaster.
- How it is used
- Distinguishes recovery from high availability so availability planning matches business failure scenarios.
- How deep it goes
- Defines DR architectures and gives operational testing risks, while deferring actual HA/DR implementation guidance.
- Examples
- Uses data-center loss, cross-site availability-group failover, inadequate secondary performance, permissions, and encryption misconfiguration.
- What you can do after
- Evaluate/understand whether a recovery design withstands site loss and requires regular tested failover.
Read how 100 SQL Server Mistakes and How to Avoid Them explains disaster recovery →
Amazon Web Services in Action, Third Edition: An in-depth guide to AWS
Disaster recovery is the set of processes and capabilities for restoring systems and data after a disruptive failure or destructive event.
- How it is used
- Frames recovery requirements and AWS architectures for virtual-machine, availability-zone, and region-wide failures.
- How deep it goes
- Defines RTO and RPO, then demonstrates cross-region snapshot copying; it stops short of a complete recovery runbook.
- Examples
- Uses a WordPress database snapshot copied from us-east-1 to eu-west-1 and a Jenkins server outage to illustrate recovery objectives.
- What you can do after
- Operate/configure Amazon RDS snapshot commands and evaluate recovery architectures against RTO, RPO, cost, and compliance constraints.
Architecting an Apache Iceberg Lakehouse
Disaster recovery is the set of processes and capabilities for restoring data and system operation after failures, corruption, or other disruptive events.
- How it is used
- Protects Iceberg lakehouses against metadata loss, file deletion, catalog outages, region failures, and damaging operational changes.
- How deep it goes
- Explains backup, replication, rollback, reconstruction, monitoring, and orchestrated recovery while stopping short of provider-specific implementation detail.
- Examples
- Uses catalog backups, metadata.json reconstruction, cross-region replication, snapshot rollback, and recovery DAGs as operational examples.
- What you can do after
- Build it from scratch by backing up metadata, replicating storage, encoding recovery workflows, and validating snapshot lineage.
Read how Architecting an Apache Iceberg Lakehouse explains disaster recovery →
Microsoft Azure in Action
Disaster recovery is the set of processes and infrastructure used to restore services and data after a catastrophic failure or disruptive event.
- How it is used
- Protects Azure-hosted services and Banning Books’ ordering database from downtime and data loss.
- How deep it goes
- Explains backup types, schedules, retention, and point-in-time restoration, but stops short of detailed disaster-response procedures.
- Examples
- Uses Banning Books’ ordering system, Azure SQL DB backups, and a relational-database comparison to illustrate recovery choices.
- What you can do after
- Evaluate Azure database choices by comparing disaster-recovery capabilities, cost, security, and potential data loss.
Read how Microsoft Azure in Action explains disaster recovery →
Generative AI in Action
Disaster recovery is the set of processes for restoring systems, data, and services after a catastrophic disruption.
- How it is used
- Uses disaster recovery to preserve generative AI availability and business continuity during catastrophic disruptions or service failures.
- How deep it goes
- Frames redundancy, failover, monitoring, and testing, but stops short of specifying recovery procedures, targets, or implementation runbooks.
- Examples
- Uses redundancy and failover planning, regular testing, monitoring, hyperscaler dependencies, and differing LLM, SLM, and edge scenarios.
- What you can do after
- Operate/configure production AI deployments with recovery planning linked to availability, monitoring, validation, and failover.
Read how Generative AI in Action explains disaster recovery →
Related concepts
- recovery point objectivemeasured by · A recovery point objective is the maximum acceptable amount of data loss, typically expressed as the time between a disruption and the latest recoverable state.
- recovery time objectivemeasured by · A recovery time objective is the maximum acceptable duration for restoring a disrupted system, service, workload, or business function to operation.
- backup and restoreuses · Backup and restore is the practice of preserving copies of data or system state and using them to recover it after loss, damage, or failure.
- disaster recovery testingused by · Disaster recovery testing is a planned exercise that evaluates whether systems, data, procedures, and personnel can recover after a disruptive failure.
- replicationaddressed by · Replication is the creation or maintenance of multiple copies of data, state, or functionality across separate locations or components.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.