The Most Absurd (and Brilliant) Kubernetes Cluster at KubeCon 2025
Engineer Justin Garrison showcased a backpack-sized PETAFLOP Kubernetes cluster at KubeCon 2025, demonstrating localized AI capabilities without cloud reliance.
Updates and recent posts about BigQuery..
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Google Breaks Kubernetes Limits Again: Inside the 130,000-Node GKE Cluster
Google successfully operates a 130,000-node Kubernetes cluster to enhance GKE's scalability for AI workloads.
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Inside Cloudflare's Worst Outage Since 2019: How a Single Config File Broke the Internet
A database permissions change led to a Cloudflare outage by creating an oversized feature file, causing network failures initially mistaken for a DDoS attack.
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Valkey 9.0 Released: Faster Clusters, New TTL Controls, and Big Networking Gains
Valkey 9.0 debuts with atomic slot migrations, hash field expiration, and improved cluster mode support, enhancing data management and scalability.
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Grafana 12.3 Lands With New Learning Tools, Better Logs, and a Critical Security Fix
Grafana 12.3 enhances user experience with interactive learning, improved logs visualization, and a critical security fix, alongside new features like dashboard image export and expanded data source support.
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Google Unveils Antigravity: An Agentic IDE Built for Autonomous Coding
Google introduces Antigravity, an AI-driven platform enhancing IDEs with autonomous coding capabilities, now in public preview.
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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).
