Recovery capability: During data replication, it is important to evaluate the ability to restore them at the destination site. This may require implementing appropriate restoration procedures, having suitable hardware and software resources available, and the ability to test and verify the restoration process.<\/li>\n<\/ul>\n\n\n\nOf course, the blanket is always too short, and we must be aware that disaster recovery must necessarily be a compromise. In support of this, I would like to mention a fundamental theorem for distributed systems: the CAP theorem.<\/p>\n\n\n\n
The CAP Theorem<\/h2>\n\n\n\n The CAP theorem (Consistency, Availability, Partition tolerance)<\/strong>, which normally applies to distributed database systems, can be similarly applied in the context of Disaster Recovery (DR) with some additional considerations. Also known as Brewer’s theorem, it states that it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees:<\/p>\n\n\n\n\nConsistency: Data consistency in the context of DR refers to the assurance that replicated data is consistent between the production site and the recovery site. In other words, the replicated data should reflect the latest changes made in the production system. However, during a disruption event and during the recovery process, a brief period of data inconsistency between the two sites may be acceptable to ensure operational continuity. Therefore, DR may temporarily sacrifice short-term consistency to ensure availability and partition tolerance.<\/li>\n\n\n\n Availability: The primary goal of DR is to ensure the availability of critical services and data in the event of a disruption. This means that the recovery system should be able to provide essential services in a timely manner, even if the production site is compromised. Availability in the context of DR can be achieved by sacrificing short-term data consistency or by using techniques such as restoring from backups or using dedicated recovery resources.<\/li>\n\n\n\n Partition tolerance: Partition tolerance is crucial in the context of DR. It refers to the system’s ability to continue functioning even when disruptions or partitions occur in the communication between the production site and the recovery site. Replicating data and resources in geographically separate sites contributes to ensuring partition tolerance. In case of communication disruption between the sites, the recovery system should be able to operate autonomously until communication is restored.<\/li>\n<\/ul>\n\n\n\nWhen applying the CAP theorem to DR, a conscious choice is often made to balance consistency, availability, and partition tolerance based on specific business needs and the consequences of disruptions. For example, in a prioritized recovery environment, availability may be given priority, temporarily sacrificing data consistency. Conversely, in an environment highly sensitive to consistency, consistency may be prioritized, temporarily sacrificing availability.<\/p>\n\n\n\n
All this is just to understand how disaster recovery is done, and how we transition from production to our secondary site. But there is a crucial point in the matter that is essential and often overlooked by everyone, which is designing how to return to production after the disastrous event has subsided<\/strong>. In short, we always forget to define how to “go back.”<\/p>\n\n\n\nIn this case, it is important to follow a well-planned process to ensure a safe and smooth transition. <\/p>\n\n\n\n
First and foremost, a recovery assessment must be conducted. Before returning to normal production, it is necessary to carefully assess the state of the system and the production environment<\/strong>. Verify that the problem or disruption event has been completely resolved and that the environment is ready for restoration. In this case, having good monitoring systems is crucial to detect any anomalies or issues. The use of monitoring tools and alerts is helpful in identifying and promptly resolving any problems that may occur during the transition back to production. Repetition helps: monitoring is also crucial post-restoration to identify any residual issues or side effects that may arise and to intervene promptly to resolve them.<\/p>\n\n\n\nIt is not enough for the production environment to be finally available; testing and validation must be performed. This may include functional testing, load testing, and other appropriate tests to ensure that everything functions correctly as expected. Verify that the data is consistent and intact.<\/p>\n\n\n\n
In some cases, it may be necessary to perform a rollback of the changes made during the DR process. This may involve restoring configurations, application changes, or previous versions of data. It is important to plan the rollback in order to minimize the impact on operations and ensure data consistency.<\/p>\n\n\n\n
Communication with users and stakeholders involved<\/strong> should not be underestimated in emergency situations. The individuals involved must be made aware of the changes made and the necessary steps to return to normal production.<\/p>\n\n\n\nTo inform the involved individuals, it is necessary to document our procedures, recording all the steps, changes made, and actions taken during the DR process. This will help reconstruct the disruption event and analyze it later to improve future DR strategies.<\/p>\n\n\n\n
Additionally, we conclude with a connection to the next article (on-prem to cloud and then cloud to cloud).<\/p>\n\n\n\n
Conclusions<\/h2>\n\n\n\n We have reached the end of the first stage of our journey in Cloud Disaster Recovery. After defining the macro concepts and understanding the dynamics, it is time to delve into the implementation of DR techniques in complex business scenarios.<\/p>\n\n\n\n
We will start by exploring the best DR techniques for hybrid on-prem to cloud contexts in the next article, and then discuss DR for Business Continuity in the Cloud-to-Cloud context in the third article of our mini-series.<\/p>\n\n\n\n
Are you ready? See you in 14 days on Proud2beCloud!<\/p>\n\n\n\n
Read Part 2<\/a> | Read Part 3<\/a><\/p>\n\n\n\n \n\n\n\nAbout Proud2beCloud<\/h4>\n\n\n\n Proud2beCloud<\/strong> is a blog by beSharp<\/a>, an Italian APN Premier Consulting Partner expert in designing, implementing, and managing complex Cloud infrastructures and advanced services on AWS. Before being writers, we are Cloud Experts working daily with AWS services since 2007. We are hungry readers, innovative builders, and gem-seekers. On Proud2beCloud, we regularly share our best AWS pro tips, configuration insights, in-depth news, tips&tricks, how-tos, and many other resources. Take part in the discussion!<\/p>\n","protected":false},"excerpt":{"rendered":"Read Part 2 | Read Part 3 It’s no longer just a quote, but a mantra: “Everything fails, all the […]<\/p>\n","protected":false},"author":5,"featured_media":5904,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[482],"tags":[649,653],"yoast_head":"\n
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