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@faun shared a link, 6 months, 3 weeks ago
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MariaDB Kubernetes Operator 25.08.0 Adds AI Vector Support and Disaster Recovery Enhancements

MariaDB Kubernetes Operator 25.08.0 drops some real upgrades. First up:physical backups. Now supported through native MariaDB tools and Kubernetes CSI snapshots—huge win if you're dealing with chunky datasets and tight recovery windows. It alsodefaults to MariaDB 11.8, which brings in anative vect.. read more  

MariaDB Kubernetes Operator 25.08.0 Adds AI Vector Support and Disaster Recovery Enhancements
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@faun shared a link, 6 months, 3 weeks ago
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Kubernetes Will Solve YAML Headaches with KYAML

Kubernetes is eyeing a YAML remix. Version 1.34 may bring inKYAML—a stricter, YAML-compatible subset built to cut down on sloppy configs and sneaky formatting bugs. KYAML keeps the good parts: comments, trailing commas, unquoted keys. But it dumps YAML’s whitespace drama. Existing manifests and Hel.. read more  

Kubernetes Will Solve YAML Headaches with KYAML
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Kubernetes Observability: Pillars, Tools & Best Practices

Kubernetes observability isn’t just about catching metrics or tailing logs. It’s about stitching togethermetrics, logs, and tracesto see what’s actually happening—across services, over time, and through the chaos. Thing is, Kubernetes doesn’t come with this built in. So teams hack together toolchai.. read more  

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@faun shared a link, 6 months, 3 weeks ago
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Introducing Headlamp AI Assistant

Headlamp just dropped an AI Assistant plugin that foldsLLM-driven actions and queriesstraight into the Kubernetes UI. It taps intocontext-aware promptsto spot issues, restart deployments, and hunt down flaky pods—without leaving the interface. System shift:This pushes Kubernetes toward intent-based.. read more  

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Cloudera Acquires Taikun for Managing Kubernetes and Cloud

Cloudera acquired Taikun for seamless deployment of data and AI workloads in any environment. This move reinforces Cloudera's commitment to flexibility and innovation in managing complex IT infrastructures... read more  

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How We Saved $1.22 Million Annually on GCP Costs in a Few Simple Steps

Arpeely chopped$140K/monthoff their cloud bill using a surgical mix of GCP tricks. Committed Use Discounts (CUDs) for high-availability services? Check. Smarter Kubernetes HPA configs? Definitely. Archiving old BigQuery data into GCS Archive? That one alone slashed storage costs 16x. The real kicker.. read more  

How We Saved $1.22 Million Annually on GCP Costs in a Few Simple Steps
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@faun shared a link, 6 months, 3 weeks ago
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Introducing Kubernetes for Snowflake

Snowflake just leveled up its workload scheduler—now driven by LLMs and reinforcement learning. Instead of locking jobs to static warehouses, it predicts where to send them in real-time. Smarter routing, tighter hardware use, over40%shaved off compute bills. Bigger picture:Another nod toward ML-bas.. read more  

Introducing Kubernetes for Snowflake
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Accessing the Kubernetes API from SQL Server 2025

SQL Server 2025 rolls outspinvokeexternalrestendpoint, a new way to hit REST APIs straight from T-SQL. That includes calling the Kubernetes API—thanks to a reverse proxy in front. The setup’s not exactly plug-and-play. You’ll need custom TLS certs, an nginx reverse proxy, and Kubernetes RBAC to kee.. read more  

Accessing the Kubernetes API from SQL Server 2025
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@laura_garcia shared a post, 6 months, 3 weeks ago
Software Developer, RELIANOID

🔒 How can you improve the high availability and performance of your firewalls?

We're sharing a diagram on Firewall Load Balancing, along with a link to our technical article that explains how it works, its benefits, and best practices. 👉https://www.relianoid.com/resources/knowledge-base/misc/what-is-firewall-load-balancing-fwlb/ #FirewallLoadBalancing#HighAvailability#NetworkS..

diagrama firewall load balancing RELIANOID
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.