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Considerations for Disaster Recovery – Part 3: Networking

Zerto

Data Protection and Recovery Architecture Why It Matters: Data loss during a disaster disrupts operations, damages reputations, and may lead to regulatory penalties. How to Achieve It: Implement multi-layered security with firewalls, intrusion prevention systems, zero trust architecture, and encryption. Do you conduct regular DR tests?

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Understand resiliency patterns and trade-offs to architect efficiently in the cloud

AWS Disaster Recovery

Firms designing for resilience on cloud often need to evaluate multiple factors before they can decide the most optimal architecture for their workloads. This will help you achieve varying levels of resiliency and make decisions about the most appropriate architecture for your needs. Resilience patterns and trade-offs.

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Beyond the Hype: The Truth behind the Marketing Claims of Many New Data Storage Platforms

Pure Storage

In addition, it can deliver upgrades that are 100% non-disruptively compliments of our Evergreen architecture to support future scale and upgrades. In this first installment of our “Beyond the Hype” blog series, we’ll discuss what customers may want to consider when evaluating storage solutions in the market.

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Implementing Multi-Region Disaster Recovery Using Event-Driven Architecture

AWS Disaster Recovery

In this blog post, we share a reference architecture that uses a multi-Region active/passive strategy to implement a hot standby strategy for disaster recovery (DR). With the multi-Region active/passive strategy, your workloads operate in primary and secondary Regions with full capacity. This keeps RTO and RPO low.

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IT Resilience Within AWS Cloud, Part II: Architecture and Patterns

AWS Disaster Recovery

In Part II, we’ll provide technical considerations related to architecture and patterns for resilience in AWS Cloud. Considerations on architecture and patterns. Resilience is an overarching concern that is highly tied to other architecture attributes. Let’s evaluate architectural patterns that enable this capability.

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Why Does Storage Matter?

Pure Storage

Cleaning, where the raw data is sorted, evaluated, and prepared for transfer and storage. . Finally, a portion of the data is held back to evaluate model accuracy. As seen above, each stage in the AI data pipeline has varying requirements from the underlying storage architecture.

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Storage and Data Protection News for the Week of March 28; Updates from Cohesity, Concentric AI, Infinidat & More

Solutions Review

To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools. Register free on LinkedIn Insight Jam Panel Highlights: Does AI Fundamentally Change Data Architecture? Product demo included!