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@koukibadr shared a link, 1 week, 6 days ago

Mobile Developer, Nventive

Code Templating

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@laura_garcia shared a post, 2 weeks ago

Software Developer, RELIANOID

𝗛𝗶𝗴𝗵 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗹𝗼𝗻𝗲 𝘄𝗼𝗻’𝘁 𝘀𝗮𝘃𝗲 𝘆𝗼𝘂.

🚨 𝗛𝗶𝗴𝗵 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗹𝗼𝗻𝗲 𝘄𝗼𝗻’𝘁 𝘀𝗮𝘃𝗲 𝘆𝗼𝘂.

HA handles failures like node crashes or AZ outages.

But what about:

❌ Ransomware

❌ Region-wide outages

❌ Human error

👉 That’s 𝗗𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 (𝗗𝗥) territory.

Real-world proof:

GitLab → redundancy ≠ recovery

Maersk → one offline backup saved everything

Code Spaces → no DR = shutdown

🎯 𝗛𝗔 = 𝗸𝗲𝗲𝗽 𝗿𝘂𝗻𝗻𝗶𝗻𝗴

🎯 𝗗𝗥 = 𝗰𝗼𝗺𝗲 𝗯𝗮𝗰𝗸 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲

At RELIANOID, we design both:

✔️ HA with clustering & failover

✔️ DR with multi-region + immutable backups

Because uptime is good—but 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗶𝘀 𝗯𝗲𝘁𝘁𝗲𝗿.

#HighAvailability #DisasterRecovery #Resilience #Cloud #DevOps #RELIANOID

https://www.relianoid.com/blog/beyond-high-availability-why-disaster-recovery-matters-and-how-relianoid-delivers/

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AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.