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Zero Trust and Cloud-Native Windows

Microsoft’s moving the cheese again—this time steering Windows deep into the cloud. The old on-prem management playbook? Getting dusty. At the core:Intune, pushingZero Trustlike it means it. Identity-based access, always-on compliance, real-time config—no more trusting the device just because it’s .. read more  

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Creating a GitHub App based Azure DevOps Pipelines Service Connection

Azure DevOps made it easier to link up with GitHub—no more re-installing the Azure Pipelines GitHub App to kick things off. Teams can spin up aGitHub App–based service connectiondirectly from a dummy pipeline setup. The service connection comes GitHub App–authenticated out of the gate. Super handy .. read more  

Creating a GitHub App based Azure DevOps Pipelines Service Connection
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Beyond IAM access keys: Modern authentication approaches for AWS

AWS wants long-term IAM access keys gone. In their place:temporary creds via IAM roles,IAM Identity Center,CloudShell, andOIDC integrations. The push covers everything—CLI tools, local dev, compute, CI/CD, even old-school on-prem. The message is clear: rotate automatically, grant minimally, and sto.. read more  

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vCluster: The Performance Paradox – How Virtual Clusters Save Millions Without Sacrificing Speed

vClustercuts Kubernetes infra costs by running virtual clusters as pods inside a shared host. No more spinning up full control planes for every tenant. Itslean Syncerfilters API traffic to keep clusters from melting down.Shared controllersand a built-insleep modekeep idle workloads quiet—and cheap... read more  

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From Borg to Broken: why Kubernetes 2.0 is an apology letter

Kubernetes 2.0 is kicking YAML to the curb.After years of living and breathing.yamlfiles, the project is eyeing a hard break. Maintainers haven’t said it outright, but the message is clear: YAML isn’t cutting it anymore. System shift:This could signal a real usability reboot—maybe even a less painf.. read more  

From Borg to Broken: why Kubernetes 2.0 is an apology letter
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Choosing the Best Kubernetes API Gateway: comparing Kong, Envoy, and kgateway

TheKubernetes Gateway APIhit v1.0 and is officially stable. It's a clean break from the old Ingress model, bringing modular, role-aware, multi-protocol control. Core players:Gateway,GatewayClass, andHTTPRoute. On the flip side,Kong Gatewayis losing ground. The newer kids—Envoy Gatewayandkgateway—ar.. read more  

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Docker Scout for Vulnerability management of Containers and remediation

Docker Scout now scans Azure Linux 3.0 containers for CVEs in real time—right in your pipeline. It spots vulns by layer, shows you how to fix them, and plays nice withDocker,Azure DevOps, andGitHub Actions. Security scanning isn't extra credit anymore. It's shipping with the build... read more  

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How to use cache mounts to speed up Docker builds

Depot just droppedNVMe-backed cache mounts—persistent, high-speed, and wired for true incremental Docker builds. Yes, even inephemeral CI. It hooks intonative BuildKit cache mounts, supporting bothsharedandexclusiveaccess. No more fragile registry caches. No more arcane CI cache duct tape... read more  

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Azure Kubernetes Cluster with Terraform

Spin up a production-gradeAKScluster withTerraform, but skip the hand-wavy theory. This new hands-on project gets into the weeds—RBAC, autoscaling, network policies, IP lockdowns, and yes,Azure Monitorwired up for observability out of the gate. Costs? Controlled. Infra? All code. It’s IaC for teams.. read more  

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20 Best Kubernetes Monitoring Tools in 2025

Kubernetes monitoring isn't just about scraping metrics anymore. It's grown up into full-stack observability—metrics, logs, traces, plus flashy toys like AI-powered anomaly detection, real-time dashboards, and distributed tracing that actually works. The big players—Prometheus,Grafana,Datadog,Dynat.. read more  

20 Best Kubernetes Monitoring Tools in 2025
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).