dataops-disaster-recovery-review

Translate RTO/RPO objectives into backup, replication, restore, and failover requirements for data platforms.

14|1|Updated May 5, 2026
One-click install
npx skills add https://github.com/ivanshamaev/de-agent-skills --skill dataops-disaster-recovery-review
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dataops-disaster-recovery-review
Source: https://github.com/ivanshamaev/de-agent-skills/tree/main/group_skills/infra_dataops_group_skills/dataops_disaster_recovery_review
Command: npx skills add https://github.com/ivanshamaev/de-agent-skills --skill dataops-disaster-recovery-review

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps data teams design and validate disaster recovery (DR) plans by translating business availability goals into concrete backup, replication, restore, and failover procedures for data platforms.

Core Features & Use Cases

  • Define RTO/RPO by platform component: Establishes restore time and data-loss limits for systems like Airflow metadata DB, data lake, Kafka, Trino catalog, and Kubernetes workloads.
  • Operationalize DR with tested mechanisms: Covers practical approaches for Airflow DB backup/restore (pg_dump/pg_restore), Kafka cross-region replication (MirrorMaker2), S3 data lake replication, Kubernetes backup/restore (Velero), and reconciliation-ready runbooks.
  • Run DR “game day” and verify readiness: Includes guidance to execute periodic DR tests, validate backup integrity through restores, measure actual recovery time, and catch anti-patterns that create false confidence.

Quick Start

Ask the AI to produce an RTO/RPO-driven DR runbook for your platform by reviewing Airflow, Kafka, the data lake, Trino, and Kubernetes against your target recovery timelines.

Frequently Asked Questions about dataops-disaster-recovery-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I define RTO and RPO objectives for data platform components like Airflow and Kafka?

To define RTO and RPO for data platforms, establish restore time and data-loss limits for each component, including Airflow metadata DB, Kafka, data lakes, and Kubernetes workloads. This translates business availability goals into concrete recovery requirements.

What is the best way to set up disaster recovery for a Kafka streaming data platform?

Disaster recovery for Kafka streaming uses MirrorMaker2 for cross-region replication. You operationalize this by monitoring replication lag and validating the failover procedures to ensure your streaming data meets target RPO limits during a regional outage.

How do I create a DR failover runbook for Kubernetes and S3 data lakes?

A DR failover runbook for Kubernetes and S3 uses Velero for workload backup/restore and validates S3 cross-region replication. It operationalizes recovery timelines by documenting step-by-step failover procedures and reconciliation processes.

Can I use pg_dump and pg_restore for Airflow metadata database disaster recovery?

Yes, pg_dump and pg_restore are the practical mechanisms for Airflow metadata database backup and restore. You operationalize DR by integrating these procedures into your runbook to meet your defined RTO and RPO objectives.

How do I validate disaster recovery readiness and avoid false confidence in backup integrity?

You validate disaster recovery readiness by running periodic DR game day tests. This involves executing failover procedures, measuring actual recovery time, and performing restorative tests to catch anti-patterns and verify backup integrity.

Does this disaster recovery approach support Trino catalog rebuilds during failover?

Yes, disaster recovery planning includes Trino catalog rebuilds as part of the platform component recovery. It ensures your data lake query engine is reconciled and operational within your defined RTO limits after a failure event.