Kubernetes v1.35 Timbernetes Release: 60 Enhancements
Kubernetes v1.35, the Timbernetes Release, debuts with 60 enhancements, including stable in-place Pod updates and beta features for workload identity and certificate rotation.
Updates and recent posts about BigQuery..
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@kaptain added a new tool Kubernetes Gateway API , 3 hours, 9 minutes ago.
News FAUN.dev() Team
@kala shared an update, 3 hours ago
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Google Releases Magika 1.0: AI File Detection in Rust
Google releases Magika 1.0, an AI file detection system rebuilt in Rust for improved performance and security.
News FAUN.dev() Team
@kala shared an update, 4 hours ago
FAUN.dev()
Google’s Cloud APIs Become Agent-Ready with Official MCP Support
Google supports the Model Context Protocol to enhance AI interactions across its services, introducing managed servers and enterprise capabilities through Apigee.
Activity
@devopslinks added a new tool BigQuery , 4 hours, 29 minutes ago.
News FAUN.dev() Team
@devopslinks shared an update, 5 hours ago
FAUN.dev()
AWS Previews DevOps Agent to Automate Incident Investigation Across Cloud Environments
AWS introduces an autonomous AI DevOps Agent to enhance incident response and system reliability, integrating with tools like Amazon CloudWatch and ServiceNow for proactive recommendations.
Activity
@devopslinks added a new tool ServiceNow , 5 hours, 5 minutes ago.
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@cmndrsp0ck started using tool Terraform , 8 hours, 24 minutes ago.
Activity
@cmndrsp0ck started using tool Ansible , 8 hours, 24 minutes ago.
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).
